The EPC’s Complex Systems Toolkit provides accessible, practical resources for embedding complex systems into engineering education. The Complex Systems Toolkit is supported by Quanser.
The content in our resource library aims to signpost you to additional research and resources that may be useful in your learning and teaching.
If you would like to suggest a resource to be added, please email Wendy Attwell.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Who is this article for?: This article should be read by educators at all levels of higher education looking to highlight the connection between complex systems and sustainability within engineering learning.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Life Cycles, Capability Engineering, Systems Modelling and Analysis, and Design INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach(essential to the solution of broadly-defined problems). In addition, this resource addresses AHEP themes of Materials, equipment, technologies and processes, and Sustainability.
Several sustainability challenges, such as transitioning to a circular economy, are embedded in complex socio-technical systems. A circular economy is an economic model that replaces the linear take-make-dispose pattern with systems that keep materials and products in use for longer through designing for durability, reuse, remanufacturing, and recycling, while minimising waste and regenerating natural systems (Rizos, Tuokko, and Behrens, 2017).
Complex systems like these exhibit feedback loops, delays, non-linear change, path dependence and emergent behaviour (Sterman, 2000; Meadows, 2008). This article introduces the idea of systems-based interventions using the example of aluminium recycling systems. It is designed for engineering educators who plan to provide learners with a baseline understanding of complexity and practical entry points for designing and developing and evaluating interventions that can move a system towards sustainability.
Complexity of aluminium recycling systems:
Aluminium is infinitely recyclable, yet achieving truly closed material loops at scale remains a challenge. Most of today’s recycling occurs in situations where post-consumer scrap is collected from a wide variety of end-of-life products and the boundaries of the recycling system are difficult to define and control. This creates high variability in both the composition and the quality of recovered aluminium, since different products contain different alloys and levels of contamination (IRT M2P, 2023). At the same time, the volume of available scrap is difficult to predict, as it depends on product lifespans and consumer behaviour. These fluctuations make it harder for producers to plan and optimise secondary aluminium output, particularly when industries rely on consistent standards or just-in-time manufacturing.
The recycling system is also shaped by broader economic and regulatory forces. On the one hand, demand for low-carbon materials and the cost advantage of recycled over primary aluminium are powerful drivers of growth. On the other hand, the system faces constraints from volatile scrap prices and shifting global trade dynamics, such as U.S. tariffs on aluminium imports. Meanwhile, new policy instruments are adding further complexity. The EU’s Carbon Border Adjustment Mechanism (CBAM) is set to reshape trade flows and investment patterns, while the forthcoming Digital Product Passport (DPP) will transform how information is shared across the value chain. Together, these forces influence technologies, markets and business models, underscoring the dynamic and interconnected nature of aluminium recycling.
These interconnected factors highlight aluminium recycling as a complex socio-technical system, in which technological capabilities, market incentives, policy frameworks, and global trade are deeply interconnected. For educators, this makes aluminium an effective example for teaching students how multiple forces interact to create both opportunities and challenges for sustainable engineering.
Intervention from systems perspective:
System Dynamics (SD), first formalised byForrester (1968), has proven to be a highly valuable approach for understanding and managing complex resource and recovery systems. SD is an interdisciplinary approach, drawing on insights from psychology, organisational theory, economics, and related fields (Sterman, 2000). More supporting information about SD pedagogical tools and techniques can be found through the System Dynamics Society and Insight Maker.
From a systems perspective, interventions are not isolated events but strategic effort to influence system behaviour by targeting its structure and dynamics. A key concept here is leverage points – places within a complex system where small changes can lead to significant, systemic effects (Meadows, 1999). Meadows identified twelve types of leverage points, ranging from adjusting parameters to transforming the system’s underlying goals and paradigms, proving a conceptual framework for identifying impactful intervention.
Figure 1. Donella Meadows’ leverage points (Source: based on Meadows (1999); credit: UNDP/Carlotta Cataldi; reproduced fromBovarnick and Cooper (2021))
Exploration of potential leverage points:
System Dynamics (SD) tools such as Causal Loop Diagrams (CLDs) can help explore leverage points. CLDs can help visualise main components of a system and their interdependencies, making complex dynamics easier to understand. Besides, the process of building a CLD or more computational SD model encourages practitioners to clarify system boundaries, relationships, and drivers, laying the foundation for identifying leverage points.
For example, a CLD of aluminium recycling might capture how classification and sorting processes influence scrap quality, which then affects remelting efficiency and ultimately market uptake of recycled alloys (see Figure 2 below).
Figure 2. The causal loop diagram for auto aluminium recycling (Liu et al., 2025)
By tracing these circular cause-and-effect relationships, learners can see where interventions may ripple through the system. Highlighting reinforcing loops, balancing loops, and delays also shows why some interventions produce limited short-term results but more substantial long-term effects.
Leverage points can also be examined through the lens of information, rules, and goals. Improved information flows, such as those enabled by the Digital Product Passport, could reshape how scrap is sorted and valued. Rules, such as alloy specifications or trade tariffs, determine what types of recycled material can enter the market. At a deeper level, the goals of the system, whether to maximise throughput or to retain material value, fundamentally shape behaviour. Here too, CLDs are valuable because they allow users to visualise how changes to information, rules, or goals can shift system dynamics, providing a clearer picture of where interventions might be most effective.
Implication for educators:
This article equips educators with a focused, practical pathway to teach systems thinking through the example of aluminium recycling. Students can gain both conceptual understanding and hands-on skills to map feedback loops, identify delays, and design interventions that account for short-term trade-offs and long-term system behaviour. Teaching a single clear CLD followed by one modelling or scenario activity produces measurable learning gains while keeping the task accessible for beginners.
Educational approach:
Prioritise structure before solutions: have students map feedback loops and delays before proposing fixes.
Use one classroom-ready CLD as the anchor activity and one hands-on modelling task to test interventions.
Emphasise leverage thinking: move from parameter tweaks to information, rules, goals and paradigms as students mature.
Keep language simple and concrete: avoid jargon, introduce terms with examples, and reuse the same CLD across activities.
Use open-access tools (Insight Maker, Loopy, Vensim PLE) so students can visualise and experiment without software barriers.
Focus assessment on reasoning about system behaviour and predicted long-term effects rather than exact numerical answers.
Potential related learning outcomes within this topic:
Define stocks, flows, feedback loops, delays, reinforcing and balancing loops.
Explain why aluminium recycling is a complex socio-technical system influenced by technology, markets, policy, and information.
Construct a simple CLD for an aluminium recycling pathway and identify at least two reinforcing and one balancing loop.
Identify two leverage points and justify which one to prioritise, citing anticipated short- and long-term system effects.
Translate the CLD into a basic stock-and-flow sketch in an open-access tool and run one scenario to compare outcomes.
Further resources:
European Commission: Joint Research Centre, Environmental and socio-economic impacts of the circular economy transition in the EU cement and concrete sector – Analysing plastics material flows with life cycle-based and macroeconomic assessment models, Publications Office of the European Union, 2025, https://data.europa.eu/doi/10.2760/6579506
The Complexity and Interconnectedness of Circular Cities and the Circular Economy for Sustainability — analysis of research themes and networked interactions relevant for urban/material systems; useful for teaching complexity and cross-sector links. https://onlinelibrary.wiley.com/doi/pdf/10.1002/sd.2766
Bovarnick, A. and Cooper, S. (2021) “From what to how: rethinking food systems interventions,” Agriculture for Development. Edited by K. Hussein, 22 April, pp. 49–53.
Forrester, J.W. (1968) “Industrial Dynamics—After the First Decade,” Management Science, 14(7), pp. 398–415. Available at: https://doi.org/10.1287/mnsc.14.7.398.
Liu, M., Schneider, K., Litos, L., Salonitis, K., 2025. Enhancing Secondary Aluminium Supply: Optimising Urban Mining Through a Systems Thinking Approach, in: Edwards, L. (Ed.), Light Metals 2025. Springer Nature Switzerland, Cham, pp. 1273–1279.
Meadows, D.H. (1999) Leverage Points – Places to Intervene in a System, The Sustainability Institute.
Meadows, D.H. (2008) Thinking in systems: A primer. White River Junction, VT: Chelsea Green Publishing Company.
Sterman, J. (2000) “Business Dynamics, System Thinking and Modeling for a Complex World.” Available at: http://hdl.handle.net/1721.1/102741 (Accessed: September 4, 2025).
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Who is this article for?: Thisarticle should be read by educators at all levels in higher education who are seeking an overall perspective on teaching approaches for integrating complex systems in engineering education.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).
Premise:
This document aims to provide definitions of key terms regarding engineered complex systems.
There are many existing relevant glossaries (for example, the Systems Engineering Body of Knowledge or SEBoK) so we have implemented a process to select a curated list of 14 common terms that are fundamental when considering the idea of complexity in engineered solutions, and therefore of importance to educators in this space. Rather than adding new definitions for each term we offer appropriate and accessible definitions from the literature, together with commentary exploring wider context and consideration where relevant.
Approach:
Some care is needed when using any definition around terms relating to complexity – because complexity itself is complex. There are multiple valid perspectives and so any one definition is unlikely to capture the totality of nuance and satisfy the variety of viewpoints. The process for selecting these terms involved collating an initial long list for potential inclusion, along with the ways in which each has been previously defined. These are provided as a supplementary annex to the main glossary. The method is further described in the following sub-section.
An initial list of potential terms to define was generated by cross-referencing existing glossaries. Terms that occurred in multiple glossaries were included in the long list. The definitions of these terms were extracted from these existing glossaries and are cited in the references. In addition, the relationship to the INCOSE Competencies is shown. The range of potential terms, and the variety of definitions that already exist, illustrate the complexity of describing complexity!
The authors used three categorisations of the definitions to help further group and classify the terms. The following categories are tagged to relevant terms in the glossary:
1. Property – whether or not the term describes a property applied to systems;
2. Principle – whether or not the term represents a principle that should be used when engineering complex situations or systems;
3. Approach – whether or not the term represents an approach, or element of an approach that should / could be used when engineering complex situations or systems.
Finally, explanatory commentary was added to most definitions to more specifically address an engineering education context.
Glossary:
Architecture
Definition: “an abstract description of the entities of a system and the relationship between those entities.” Crawley et al. (2016) System Architecture: Strategy & Product Development for Complex Systems
Boundary
#Property #Principle
Definition: “Define the system to be addressed. A description of the boundary of the system can include the following: definition of internal and external elements/items involved in realizing the system purpose as well as the system boundaries in terms of space, time, physical, and operational. Also, identification of what initiates the transitions of the system to operational status and what initiates its disposal is important.” NASA (2007) NASA Systems Engineering Handbook, p304
Commentary: The boundary defines the scope of the system being considered, and by implication, what sits outside of the system. As such, it is critically important to define the boundary of the system-of-interest. When dealing with complex systems this can be a challenging task and may even benefit from acknowledging multiple boundaries (e.g. physical, spatial, functional, logical etc.). For example, the boundary of the physical elements of a system could be considered within a wider boundary of the problem space.
Complexity
#Property
Definition: “A complex system is a system in which there are non-trivial relationships between cause and effect: each effect may be due to multiple causes; each cause may contribute to multiple effects; causes and effects may be related as feedback loops, both positive and negative; and cause-effect chains are cyclic and highly entangled rather than linear and separable.” INCOSE (2019) INCOSE Systems Engineering and Systems Definitions
Commentary: Early conceptions of complexity emphasised the difficulty in understanding, predicting or verifying the behaviours of a system. A key distinction arising from this is the complicated and complex are not synonymous. This concept of the difficulty in predicting behaviours is reflected in the definitions of the NASA Systems Engineering Handbook, SEBoK and ISO 24765. This is the key resultant consideration but does not describe the underlying property which causes this difficulty. While this definition relates more to complex systems than complexity, it is chosen for the way in which it goes beyond the consequences of complexity.
Coupling
#Property #Principle
Definition: “Coupling […] means to fasten together, or simply to connect things […] Coupling suggests a relationship between connected entities. If they are coupled, in some way they can affect each other […] For the system to be useful, its components have to be connected – coupled – so that they can work together. That said, putting them together arbitrarily won’t do the trick. The components have to be coupled in a way that achieves the goals of the system. Not only is coupling the glue that holds a system together, but it also makes the value of the system higher than the sum of its parts.” Khononov (2024) Balancing Coupling in Software Design: Universal Design Principles for Architecting Modular Software Systems, Ch1
Commentary: Coupling is a very important concept. It is the interconnection and interdependence that makes the system more (or less) than the sum of its parts. Standard Systems architecture advice is to minimise coupling between system elements (or between the systems in a system-of-systems). This is because high coupling correlates to higher structural complexity, reduced resilience and flexibility in the system, and introduces challenges for modularity in the system design. Lower or looser coupling means changes in one part of the system (in design or operation) are less likely to induce or require changes in another part. However, this lower coupling is not always possible and may be necessary to improve system performance (for example communication through intermediate layers in a system to reduce coupling can introduce unacceptable amounts of overhead and latency in the system). In design terms, high coupling between system elements means that those elements cannot be designed independently.
Emergence
#Principle
Definition: “As the entities of a system are brought together, their interaction will cause function, behaviour, performance and other intrinsic (anticipated and unanticipated) properties to emerge… Emergence refers to what appears, materializes, or surfaces when a system operates.” Crawley et al. (2016) System Architecture: Strategy & Product Development for Complex Systems
Commentary: It is worth noting that Crawley et al. (2014) go on to add “As a consequence of emergence, change propagates in unpredictable ways. System success occurs when anticipated emergence occurs, while system failure occurs when anticipated emergent properties fail to appear or when unanticipated undesirable emergent properties appear.” This emergence that gives rise to the difficulty in understanding, predicting or verifying the behaviours of a system (see Complexity).
Form
#Property
Definition: “The shape, size, dimensions, mass, weight, and other measurable parameters which uniquely characterize an item.” SAE International (2019) ANSI/EIA-649C
Function
#Principle #Approach
Definition: “A function is defined as the transformation of input flows, with defined performance targets for how well the function is performed in different conditions. A function usually has logical pre-conditions that trigger its operation. ”Systems Engineering Body of Knowledge v2.12 (2025)
Commentary: In general usage it is common to hear reference to ‘Form and Function’ in tandem, but it is the distinction between them and their relationship to one another that is important to engineering complex systems. Thinking in terms of functionality is a good way of abstracting the system to define what it does (or is needed to do) rather than what it is (and therefore by extension its form). Functions are normally allocated to single sub-elements of the system. Complexity arises at functional interfaces, or when different elements perform the same function. Thinking in terms of functionality encourages creativity as designers consider all the different ways in which the function could be performed – and then apply requirement constraints to choose the best/most feasible option. Thinking in terms of “objects” first constrains design by presupposing the solutions. Equally, when the solution goes wrong, thinking in terms of what function is failing and why, rather than focusing on a failed part allows identification of the true root cause. Organisations also have functions (such as Engineering, Human Resources, etc.) as a group of roles that perform a specific set of activities. This is important for considering the organisation/System that creates the engineered solution (which is itself a complex system, but secondary to the main application of the idea of function).
Iteration
#Approach
Definition: “Iteration is used as a generic term for successive application of a systems approach to the same problem situation, learning from each application, in order to progress towards greater stakeholder satisfaction.” Systems Engineering Body of Knowledge v2.12 (2025)
Lifecycle
#Property #Principle #Approach
Definition: “The evolution of a system, product, service, project or other human-made entity from conception through retirement.” ISO (2024) ISO/IEC/IEEE 24748-1:2024
Commentary: Understanding the lifecycle of an engineered artefact is very important. Issues arising in later stages (e.g. production, support/maintenance, upgrade and disposal) must be considered during the system’s initial development. In a system-of-systems or a capability system a significant source of complexity is the fact that different system elements have different lifecycles, and so may change or be changed independently of other elements with which they may interact or interdepend.
Model
#Approach
Definition: “An abstraction of a system, aimed at understanding, communicating, explaining, or designing aspects of interest of that system” Dori, D. (2003) Conceptual modelling and system architecting, p286
Commentary: An abstraction is a simplification. The selection of what to exclude, what to include, and at what level of granularity to depict it, is informed by the purpose of the model and the point of view from which it is created. Models do not have to be quantitative, nor is their purpose exclusively analytical.
Stakeholder
Definition: “A group or individual who is affected by or has an interest or stake in a program or project.” NASA (2019) NASA Systems Engineering Handbook SP-2016-6105 (Rev. 2)
Commentary: It is worth noting the potential difference between a stakeholder of the project that develops the system, and a stakeholder of the system that is developed.
System
#Principle #Approach
Definition: “A system is an arrangement of parts or elements that together exhibit behaviour or meaning that the individual constituents do not.” INCOSE (2019) INCOSE Fellows Briefing to INCOSE Board of Directors, January 2019
Commentary: There are many similar definitions of a system, each may offer a slightly different phrasing which can resonate better with different individuals. The origins of this definition is explained in the Systems Engineering Body of Knowledge. In assessing complexity in engineered system, the concept of “systems” is of course of key value. There are two important aspects two consider:
1) Many schools of Systems Science argue that systems do not actually exist (apart from perhaps the complete universe) – they are defined for the convenience of consideration, and so the definition of the boundary of the “system of interest” is both important and somewhat arbitrary. As such, the system-of-interest can have multiple useful boundaries. While it might be possible to identify and articulate the physical boundary of an engineered artefact (and it should be acknowledged), it might not be the most useful boundary to consider.
2) The point of defining a “system of interest” includes being able to consider it as a system and so use the properties seen in systems (boundary, interface with outside, affected by/affecting environment, made up of parts, part of something larger, has a lifecycle, seen differently by different people (with different perspectives), are dynamic, exhibit emergence etc.) as a “framework for curiosity” (as the INCOSE SE competency framework defines systems thinking).
In engineered systems (rather than natural systems) it is important to distinguish between purpose (what those engineering or creating it want it do) and emergence (what it actually does).
Systems Engineering
#Principle #Approach
Definition: “Systems Engineering is a transdisciplinary and integrative approach to enable the successful realization, use, and retirement of engineered systems, using systems principles and concepts, and scientific, technological, and management methods.” INCOSE (2019) INCOSE Systems Engineering and Systems Definitions
Systems Thinking
#Approach
Definition: “Systems thinking is thinking about a question, circumstance, or problem explicitly as a system – a set of interrelated entities.” Crawley et al. (2016) System Architecture: Strategy & Product Development for Complex Systems
Commentary: Crawley et al (2016) go on to add “This means identifying the system, its form and function, by identifying its entities and their interrelationships, its system boundary and context, and the emergent properties of the system based on the function of the entities, and their functional interactions.”
References:
Crawley, E. Cameron, B. & Selva, D. (2016). System Architecture: Strategy & Product Development for Complex Systems, Pearson
Dori, D. (2003). “Conceptual modeling and system architecting.” Communications of the ACM, 46(10), pp. 62-65.
ISO (2024). Systems and software engineering — Life cycle management -ISO/IEC/IEEE 24748-1:2024
Khononov, V. (2024). Balancing Coupling in Software Design: Universal Design Principles for Architecting Modular Software Systems, Addison-Wesley Professional,
NASA. (2007). Systems Engineering Handbook – Revision 1. Washington, DC, USA: National Aeronautics and Space Administration (NASA). NASA/SP-2007-6105.
NASA (2016). Systems Engineering Handbook – Revision 2. Washington, DC, USA, National Aeronautics and Space Administration (NASA). NASA/SP-2016-6105 (Rev. 2)
SAE International (2019). National Consensus Standard for Configuration Management -ANSI/EIA-649C
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Who is this article for?: Thisarticle should be read by educators at all levels in higher education who are seekingto provide students with an overall perspective on complex systems in engineering.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).
Engineering systems today are increasingly complex, interconnected, and adaptive. To understand and manage them effectively, engineers must move beyond reductionist thinking where systems are broken into isolated parts and adopt systems thinking, which views systems as wholes made up of interacting components.
At the heart of this perspective lies emergence, a defining characteristic of complex systems. Emergence refers to properties or behaviours that arise from interactions among components but cannot be predicted or understood by examining those components in isolation. Appreciating emergence helps engineers anticipate how individual design decisions can produce system-level outcomes, sometimes beneficial, sometimes negative and unintended.
This article introduces the concept of emergence as one key characteristic of complex systems, situates it within systems thinking, and provides practical guidance for recognising and managing emergent behaviours in engineering practice.
1. What is a system?:
A system can be defined as “a set of interconnected elements organised to achieve a purpose” (Meadows, 2008). Systems possess structure (components), relationships (interactions), and purpose (function). Engineering systems such as aircraft, power grids, transport networks, or data infrastructures are composed of numerous subsystems that depend on each other.
Crucially, systems thinking emphasises interdependence and feedback. The behaviour of the whole cannot be fully explained by the behaviour of the parts alone. Properties such as resilience, adaptability, and emergence result from interactions within the system’s structure and environment. Recognising these relationships is essential to understanding how system-level behaviours arise.
Emergence describes the appearance of new patterns, properties, or behaviours at the system level that are not present in individual components. These properties are often irreducible: they cannot be explained solely by analysing each part separately (Holland, 2014).
Researchers distinguish between:
Weak emergence – behaviours that are theoretically predictable if all component interactions were known but are practically impossible to compute due to complexity (e.g. traffic flow patterns).
Strong emergence – properties that are fundamentally novel and irreducible to component-level descriptions (e.g., consciousness in biological systems).
In engineering, most emergent behaviours are weakly emergent: complex yet explainable with sufficient data and computational tools such as agent-based modelling or system dynamics.
A key caveat is that emergence depends on perspective and system boundaries. What seems emergent at one scale (e.g., the stability of a power grid) might appear straightforward when viewed at another. Therefore, engineers must define boundaries and assumptions clearly when analysing emergence.
3. Why emergence matters in engineering:
Emergence shapes how engineering systems behave, evolve, and sometimes fail. It can produce both desired outcomes (like adaptability or resilience) and undesired ones (like instability or cascading failure).
Understanding emergence enables engineers to:
anticipate how local interactions scale up to global system behaviour;
design feedback loops and architectures that promote stability; and
identify potential points for intervention when emergent behaviour becomes undesirable.
For instance, in cyber-physical systems, emergent coordination can enhance efficiency, but it may also create unpredictable vulnerabilities if feedback loops reinforce errors. Engineers therefore must not only observe emergence but learn how to influence it through design and governance.
4. Recognising and managing emergent behaviour:
Recognising emergence
Engineers can identify emergence by looking for:
System-level patterns that do not trace directly to any single component (e.g. global traffic flow or collective oscillations in a power grid).
Unexpected behaviours, such as new failure modes or self-organising phenomena.
Scale-dependent properties, where behaviour changes qualitatively as the system grows or interacts with its environment.
Adaptive or learning responses, where the system adjusts without explicit central control.
Intervening in emergent systems
Not all emergence is beneficial. Engineers often need to mitigate unwanted emergent behaviours such as instability or inefficiency while reinforcing desirable ones. Effective approaches include:
Redesigning interactions rather than individual components, focusing on how feedback and connectivity shape outcomes.
Introducing constraints or buffers to dampen runaway feedback loops.
Enhancing diversity and modularity so subsystems can adapt locally without propagating failures globally.
Monitoring system states continuously, using sensors, data analytics, or digital twins to detect emergent behaviour early.
Managing emergence requires humility: complex systems cannot be fully controlled, only influenced. The goal is to guide system dynamics toward safe and productive outcomes.
5. Illustrative examples of emergence in engineering systems:
Network systems
The Internet exemplifies emergence: billions of devices follow simple communication protocols, yet collectively create a resilient, adaptive global network. No single node dictates its performance; instead, routing efficiency and viral content propagation arise from local interactions among routers and users.
Transportation systems
Urban traffic patterns such as congestion waves, spontaneous lane formation, and adaptive rerouting emerge from individual driver behaviour and infrastructural design. Traffic engineers use simulation models to study how simple decision rules generate complex city-wide flows.
Energy systems
Electrical grids maintain frequency and voltage stability through distributed interactions among generators, loads, and controllers. Emergent synchronisation enables reliability, but loss of coordination can cause cascading blackouts showing both beneficial and harmful emergence.
Manufacturing systems
In smart factories, machines and sensors collaborate autonomously, producing system-wide optimisation in scheduling and quality control. Adaptive algorithms and feedback loops create emergent flexibility beyond what central planning alone could achieve.
6. Practical guidance for engineers and educators:
For engineers, the key is to design with emergence in mind:
focus on local rules that encourage desirable global behaviour;
incorporate feedback and sensing to detect changes early; and
use modular, diverse architectures to enhance resilience.
For educators, teaching emergence provides an opportunity to bridge theory and practice. Software such as NetLogo and Insight Maker allows students to visualise emergent behaviour through agent-based and system-dynamics models. Linking engineering examples to ecological, social, or digital systems helps learners appreciate the universality of emergence.
Conclusion:
Emergence is not an anomaly to be avoided but a natural attribute of complex systems. It challenges traditional engineering by revealing that system behaviour often arises from relationships, not components.
Understanding emergence equips engineers to recognise interdependencies, design adaptive solutions, and work with complexity rather than against it. By embracing systems thinking, engineers can create technologies that are not only functional but resilient, sustainable, and aligned with real-world dynamics.
References:
Holland, J.H. (2014). Complexity: A Very Short Introduction. Oxford: Oxford University Press.
Johnson, S. (2001). Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York: Scribner.
Mitchell, M. (2009). Complexity: A Guided Tour. Oxford: Oxford University Press.
Bar-Yam, Y. (2003). Dynamics of Complex Systems. Cambridge, MA: Perseus Publishing.
Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51-59.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Relevant disciplines:Energy engineering; Chemical engineering; Process systems engineering; Mechanical engineering; Industrial engineering.
Keywords: Available soon.
Licensing:This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It is based upon the author’s 2025 article “A Simulation Tool for Pinch Analysis and Heat Exchanger/Heat Pump Integration in Industrial Processes: Development and Application in Challenge-based Learning”. Education for Chemical Engineers 52, 141–150.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Systems Modelling and Analysis and Critical Thinking INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses the themes of Science, mathematics and engineering principles; Problem analysis; and Design.
Educational level: Intermediate.
Educational aim:To equip learners with the ability to model, analyse, and optimise pathways for industrial decarbonisation through a complex-systems lens – integrating technical, economic, and policy dimensions – while linking factory-level design decisions to wider value-chain dynamics, multi-stakeholder trade-offs, and long-term sustainability impacts.
Learning and teaching notes:
This teaching activity explores heat integration for the decarbonisation of industrial processes through the lens of complex systems thinking, combining simulation, systems-level modelling, and reflective scenario analysis. It is especially useful in modules related to energy systems, process systems, or sustainability.
Learners analyse a manufacturing site’s energy system using a custom-built simulation tool to explore the energy, cost and carbon-emission trade-offs of different heat-integration strategies. They also reflect on system feedback, stakeholder interests and real-world resilience using causal loop diagrams and role-played decision frameworks.
This activity frames industrial heat integration as a complex adaptive system, with interdependent subsystems such as process material streams, utilities, technology investments and deployments, capital costs, emissions, and operating constraints.
Learners run the simulation tool to generate outputs to explore different systems integration strategies: pinch-based heat recovery by heat exchangers, with and without heat pump-based waste heat upgrade. Screenshots of the tool graphical user interface are attached as separate files:
The learning is delivered in part, through active engagement with the simulation tool. Learners interpret the composite and grand composite curves and process tables, to explore how system-level outcomes change across various scenarios. Learners explore, using their generated simulation outputs, how subsystems (e.g. hot and cold process streams, utilities) interact nonlinearly and with feedback effects (e.g., heat recovery impacts), shaping global system behaviour and revealing leverage points and emergent effects in economics, emissions and feasibility.
Using these outputs as a baseline, and exploring other systems modelling options, learners evaluate trade-offs between heat recovery, capital expenditure (CAPEX), operating costs (OPEX), and carbon emissions, helping them develop systems-level thinking under constraints.
The activity embeds scenario analysis, including causal loop diagrams, what-if disruption modelling, and stakeholder role-play, using multi-criteria decision analysis (MCDA) to develop strategic analysis and systems mapping skills. Interdisciplinary reasoning is encouraged across thermodynamics, economics, optimisation, engineering ethics, and climate policy, culminating in reflective thinking on system boundary definitions, trade-offs, sustainability transitions and resilience in industrial systems.
Learners have the opportunity to:
Analyse non-linear interactions in thermodynamic systems.
Reconcile conflicting demands (e.g. energy savings vs costs vs emissions vs technical feasibility) using data generated from real system simulation.
Model and interpret feedback-driven process systems using pinch analysis, heat recovery via heat exchangers, and heat upgrade via heat pump integration.
Explore emergent behaviour, trade-offs, and interdisciplinary constraints.
Navigate system uncertainties by simulation data analysis and scenario thinking.
Understand the principles of heat integration using pinch analysis, heat exchanger networks, and heat pump systems, framed within complex industrial systems with interdependent subsystems.
Evaluate decarbonisation strategies and their performances in terms of energy savings, CAPEX/OPEX, carbon reduction, and operational risks, highlighting system-level trade-offs and nonlinear effects
Develop data-driven decision-making, navigating assumptions, parameter sensitivity, and model limitations, reflecting uncertainty and systems adaptation.
Explore ethical, sustainability, and resilience dimensions of engineering design, recognising how small changes or policy shifts may act on leverage points and produce emergent behaviours.
Analyse stakeholder dynamics, policy impacts, and uncertainty as part of the broader system environment influencing energy transition pathways.
Construct and interpret causal loop diagrams (CLDs), explore what-if scenarios, and apply multi-criteria decision analysis (MCDA), building competencies in feedback loops, system boundaries, and systems mapping.
Teachers have the opportunity to:
Embed systems thinking and complex systems pedagogy into energy and process engineering, using real-world simulations and data-rich problem-solving.
Introduce modelling and scenario-based reasoning, helping students understand how interactions between process units, energy streams, and external factors affect industrial decarbonisation.
Facilitate exploration of design trade-offs, encouraging learners to consider technical feasibility, economic sustainability, and environmental constraints within dynamic system contexts.
Support students in identifying leverage points, feedback loops, and emergent behaviours, using tools like CLDs, composite curves, and stakeholder role play.
Assess complex problem-solving capacity, including students’ ability to model, critique and adapt industrial systems under conflicting constraints and uncertain futures.
Proprietary Simulator for Pinch Analysis & Heat Integration. Freely available for educational use and can be accessed online through a secure link provided by the author on request (james.atuonwu@nmite.ac.uk or james.atuonwu@gmail.com). No installation or special setup is required; users can access it directly in a web browser.
About the simulation tool (access and alternatives):
This activity uses a Streamlit-based simulation tool, supported with process data (Appendix A, Table 1, or an educator’s equivalent). The tool is freely available for educational use and can be accessed online through a secure link provided by the author on request (james.atuonwu@nmite.ac.uk or james.atuonwu@gmail.com). No installation or special setup is required; users can access it directly in a web browser.The activity can also be replicated using open-source or online pinch analysis tools such as OpenPinch, PyPinchPinCH, TLK-Energy Pinch Analysis Online. SankeyMATIC can be used for visualising energy balances and Sankey diagrams.
Pinch Analysis, a systematic method for identifying heat recovery opportunities by analysing process energy flows, forms the backbone of the simulation. A brief explainer and further reading are provided in the resources section. Learners are assumed to have prior or guided exposure to its core principles. A key tunable parameter in Pinch Analysis, ΔTmin, represents the minimum temperature difference allowed between hot and cold process streams. It determines the required heat exchanger area, associated capital cost, controllability, and overall system performance. The teaching activity helps students explore these relationships dynamically through guided variation of ΔTmin in simulation, reflection, and trade-off analysis, as outlined below.
Introducing and prioritising ΔTmin trade-offs:
ΔTmin is introduced early in the activity as a critical decision variable that balances heat recovery potential against capital cost, controllability, and safety. Students are guided to vary ΔTmin within the simulation tool to observe how small parameter shifts affect utility demands, exchanger area, and overall system efficiency. This provides immediate visual feedback through the composite and grand composite curves, helping them connect technical choices to system performance.
Educators facilitate short debriefs using the discussion prompts in Part 1 and simulation-based sensitivity analysis in Part 2. Students compare low and high ΔTmin scenarios, reasoning about implications for process economics, operability, and energy resilience.
This experiential sequence allows learners to prioritise competing factors (technical, economic, and operational), while recognising that small changes can create non-linear, system-wide effects. It reinforces complex systems principles such as feedback loops and leverage points that govern industrial energy behaviour.
Data for decisions:
The simulator’s sidebar includes some default values for energy prices (e.g. gas and electricity tariffs) and emission factors (e.g. grid carbon intensity), which users can edit to reflect their own local or regional conditions. For those replicating the activity with other software tools, equivalent calculations of total energy costs, carbon emissions and all savings due to heat recovery investments can be performed manually using locally relevant tariffs and emission factors.
The Part 1–3 tasks, prompts, and assessment suggestions below remain fully valid regardless of the chosen platform, ensuring flexibility and accessibility across different teaching contexts.
Educator support and implementation notes:
The activity is designed to be delivered across 3 sessions (6–7.5 hours total), with flexibility to adapt based on depth of exploration, simulation familiarity, or group size. Each part can be run as a standalone module or integrated sequentially in a capstone-style format.
Part 1: System mapping: (Time: 2 to 2.5 hours) – Ideal for a classroom session with blended instruction and group collaboration:
This stage introduces students to the foundational step of any heat integration analysis: system mapping. The aim is to identify and represent energy-carrying streams in a process plant, laying the groundwork for further system analysis. Educators may use the Process Flow Diagram of Fig. 1, Appendix A (from a real industrial setting: a food processing plant) or another Process Diagram, real or fictional. Students shall extract and identify thermal energy streams (hot/cold) within the system boundary and map energy balances before engaging with software to produce required simulation outputs.
Key activities and concepts include:
Defining system boundaries: Focus solely on thermal energy streams, ignoring non-thermal operations. The boundary is drawn from heat sources (hot streams) to heat sinks (cold streams).
Identifying hot and cold streams: Students classify process material streams based on whether they release or require heat. Each stream is defined by its inlet and target temperatures and its heat capacity flow rate (CP).
Building the stream table: Students compile a simple table of hot/cold streams (name, supply temperature, target temperature and heat capacity flow CP).
Constructing energy balances and Sankey Diagrams: Students manually calculate energy balances across each subsystem in the defined system boundary, identifying energy inputs, useful heat recovery, and losses. Using this information, they construct Sankey diagrams to visualise the magnitude and direction of energy flows, strengthening their grasp of system-wide energy performance before optimisation.
Pinch Concept introduction: Students are introduced to the concept of “the Pinch”, including the minimum heat exchanger temperature difference (ΔTmin) and how it affects heat recovery targets (QREC), as well as overall heating and cooling utility demands (QHU & QCU, respectively).
Assumptions: All analysis is conducted under steady-state conditions with constant CP and no heat losses.
Discussion prompts:
What insights does the Sankey diagram reveal about energy use, waste and recovery potential in the system? How might these visual insights shape optimisation decisions?
Why might certain streams be excluded from the analysis?
How does the choice of ΔTmin influence the heat recovery potential and cost?
What trade-offs are involved in system simplification during mapping?
How can assumptions (like steady-state vs. transient) impact integration outcomes?
Student deliverables:
A labelled system map showing the thermal process boundaries, hot and cold streams.
A structured stream data table.
Justification for selected ΔTmin values based on process safety, economics, or practical design and operational considerations.
A basic Sankey diagram representing the energy flows in the mapped system, based on calculated heat duties of each stream.
Part 2: Running and interpreting process system simulation results (Time: 2 to 2.5 hours) – Suitable for lab or flipped delivery;only standard computer access is needed to run the tool (optional instructor demo can extend depth):
Students use the simulation tool to generate their own results.The process scenario of Fig. 1, Appendix A, with the associated stream data (Table 1) can be used as a baseline.
Tool-generated outputs:
Curves: Composite and Grand Composite (pinch location, recovery potential).
Scenario summary: QREC, QHU, QCU; COP (where applicable); CAPEX/OPEX/CO₂; payback period for various values of system levers (e.g., ΔTmin levels, tariffs, emission factors).
Heat Pump (HP) tables: Feasible pairs, Top-N heat pump selections (where N = 0, 1, or 2); QEVAP, QCOND, QCOMP, COP. All notations are designated in the simulator’s help/README section.
Learning tasks:
1. Scenario sweeps Run different scenarios (e.g., different ΔTmin levels, tariffs, emission factors, and Top-N HP selections). Prompts: How do QREC, QHU/QCU, HX area, and CAPEX/OPEX/CO₂ shift across scenarios? Which lever moves the needle most?
2. Group contrast (cases A vs B: see time-phased operations A & B in Appendix A) Assign groups different cases; each reports system behaviours and trade-offs. Prompts: Where do you see CAPEX vs. energy-recovery tension? Which case is more HP-friendly and why?
3. Curve reading Use the Composite & Grand Composite Curves to identify pinch points and bottlenecks; link features on the curves to the tabulated results. Prompts: Where is the pinch? How does ΔTmin change the heat-recovery target and utility demands?
4. Downstream implications Trace how curve-level insights show up in HX sizing/costs and HP options. Prompts: When does adding HP reduce utilities vs. just shifting costs? Where do stream temperatures/CP constrain integration?
5. Systems lens: feedback and leverage Map short causal chains from the results (e.g., tariffs → HP use → electricity cost → OPEX; grid-carbon → HP emissions → net CO₂). Prompts: Which levers (ΔTmin, tariffs, EFs, Top-N) create reinforcing or balancing effects?
Outcome:
Students will be able to generate and interpret industrial simulation outputs, linking technical findings to economic and emissions consequences through a systems-thinking lens. They begin by tracing simple cause–effect chains from the simulation data and progressively translate these into causal loop diagrams (CLDs) that visualise reinforcing and balancing feedback. Through this, learners develop the ability to explain how system structure drives performance both within the plant and across its broader industrial and policy environment.
Optional extension: Educators may provide 2–3 predefined subsystem options (e.g., low-CAPEX HX network, high-COP HP integration, hybrid retrofit) for comparison. Students can use a decision matrix to justify their chosen configuration against CAPEX, OPEX, emissions, and controllability trade-offs.
Part 3: Systems thinking through scenario analysis (Time: 2 to 2.5 hours) – Benefits from larger-group facilitation, a whiteboard or Miro board (optional), and open discussion. It is rich in systems pedagogy:
Having completed simulation-based pinch analysis and heat recovery planning, learners now shift focus to strategic implementation challenges faced in real-world industrial settings. In this part, students apply systems thinking to explore the broader implications of their heat integration simulation output scenarios, moving beyond process optimisation to consider real-world dynamics, trade-offs, and stakeholder interactions. The goal is to encourage students to interrogate the interconnectedness of decisions, feedback loops, and unintended consequences in process energy systems including but not limited to operational complexity, resilience to disruptions, and alignment with long-term sustainability goals.
Activity: Stakeholder role play / Multi-Criteria Decision Analysis Students take on stakeholder roles and debate which design variant or operating strategy should be prioritised. They then conduct a Multi-Criteria Decision Analysis (MCDA), evaluating each option based on criteria such as CAPEX, OPEX savings, emissions reductions, risk, and operational ease.
Stakeholders include:
Operations managers, focused on ease of control and process stability.
Investors and finance teams, focused on return on investment.
Environmental officers, concerned with emissions and policy compliance.
Engineers, responsible for design and retrofitting.
Community members, advocating for sustainable industry practices.
Government reps responsible for regulations and policy formulation, e.g. taxes and subsides.
The team must present a strategic analysis showing how the heat recovery system behaves as a complex adaptive system, and how its implementation can be optimised to balance technical, financial, environmental, and human considerations.
Optional STOP for questions and activities:
Before constructing causal loop diagrams (CLDs), learners revisit key results from their simulation — such as ΔTmin, tariffs, emission factors, and system costs — and trace how these parameters interact to influence overall system performance. Educators guide this transition, helping students abstract quantitative outputs (e.g., changes in QREC, OPEX, or CO₂) into qualitative feedback relationships that reveal cause-and-effect chains. This scaffolding helps bridge the gap between process simulation and systems-thinking representation, supporting discovery of reinforcing and balancing feedback structures.
Activity: Construct a causal loop diagram (CLD) Students identify at least five variables that interact dynamically in the implementation of a heat integration system (e.g. energy cost, investment risk, emissions savings, system complexity, staff training). They must map reinforcing and balancing feedback loops that illustrate trade-offs or virtuous cycles.
Where could policy or process changes trigger leverage points?
How could delays in response (e.g. slow staff adaptation to new technologies) affect outcomes?
How might design choices affect local energy equity, air quality, or community outcomes?
What policy incentives or ethical trade-offs might reinforce or hinder your proposed solution?
Instructor debrief (engineering context with simulation linkage): After students share their CLDs, the educator facilitates a short discussion linking their identified reinforcing and balancing loops to common dynamic patterns observable in the simulation results. For instance:
Limits to growth: As ΔTmin decreases, heat recovery (QREC) initially improves, but exchanger area, CAPEX, and controllability demands grow disproportionately — diminishing overall economic benefit.
Shifting the burden: Installing a heat pump may appear to improve carbon performance, but if low process efficiency remains unaddressed, electricity use and OPEX rise — creating a new dependency that shifts rather than solves the problem.
Tragedy of the commons: Competing units or stakeholders optimising locally (e.g. for their own OPEX or production uptime) can undermine total system efficiency or resilience.
Success to the successful: Design options with early financial or policy support (e.g. high-COP heat pumps) attract more investment and attention, reinforcing a positive but unequal feedback loop.
This reflection connects quantitative model outputs (e.g. QREC, OPEX, CAPEX, emissions) to qualitative system behaviours, helping learners recognise leverage points and understand how design choices interact across technical, economic, and social dimensions of decarbonisation.
Activity: Explore “What if?” scenarios
Working in groups, students choose one scenario to explore using a systems lens:
What if gas prices fluctuate drastically?
What if capital funding is delayed by 6 months?
What if a heat exchanger fouls during peak season?
What if CO₂ emissions policy tightens?
What if current electricity grid decarbonisation trends suffer an unexpected setback?
What if government policies now encourage onsite renewable electricity generation?
Each group evaluates the resilience and flexibility of the proposed integration design. They consider:
System bottlenecks and fragilities.
Leverage points for intervention.
Need for redundancy or modular design.
Educators may add advanced scenarios (e.g. carbon tax introduction, supplier failure, or project delay) to challenge students’ resilience modelling and stakeholder negotiation skills.
Stakeholder impact reflection:
To extend systems reasoning beyond the technical domain, students assess how their chosen design scenarios (e.g., low vs. high ΔTmin, with or without heat pump integration) affect each stakeholder group. For instance:
Operations managers assess control complexity, downtime risk, and maintenance implications.
Finance teams evaluate CAPEX/OPEX trade-offs and payback periods.
Environmental officers examine lifecycle emissions and regulatory compliance.
Engineers reflect on reliability, retrofit feasibility, and process safety.
Community members or regulators consider social and policy outcomes, such as visible sustainability impact or energy equity.
Each team member rates perceived benefits, risks, or compromises under each design case, and the results are summarised in a stakeholder impact matrix or discussion table. This exercise links quantitative system metrics (energy recovery, emissions, cost) to qualitative stakeholder outcomes, reinforcing the “multi-layered feedback” perspective central to complex systems analysis.
Learning Outcomes (Part 3):
By the end of this part, students will be able to:
Identify systemic interdependencies in industrial energy systems.
Analyse how feedback loops and delays influence system behaviour.
Assess the resilience of energy integration solutions under different future scenarios.
Balance multiple stakeholder objectives in complex engineering contexts.
Apply systems thinking tools to communicate complex technical scenarios to diverse stakeholder audiences.
Use systems diagrams and decision tools to support strategic analysis.
Instructor Note – Guiding CLD and archetype exploration:
Moving from numerical heat-exchange and cost data to CLD archetypes can be conceptually challenging. Instructors are encouraged to model this process by identifying at least one reinforcing loop (e.g. “energy savings → lower OPEX → more investment in recovery → further savings”) and one balancing loop (e.g. “higher capital cost → reduced investment → lower heat recovery”). Relating these loops to common system archetypes such as “Limits to Growth” or “Balancing with Delay” helps students connect engineering data to broader system dynamics and locate potential leverage points. The activity concludes with students synthesising their findings from simulation, systems mapping, and stakeholder analysis into a coherent reflection on complex system behaviour and sustainable design trade-offs.
Assessment guidance:
This assessment builds directly on the simulation and systems-thinking activities completed by students. Learners generate and interpret their own simulation outputs (or equivalent open-source pinch analysis results), using these to justify engineering and strategic decisions under uncertainty.
Assessment focuses on students’ ability to integrate quantitative analysis (energy, cost, carbon) with qualitative reasoning (feedbacks, trade-offs, stakeholder dynamics), demonstrating holistic systems understanding.
Deliverables (portfolio; individual or group):
1. Reading and interpretation of simulation outputs
Use the outputs you generate (composite & grand composite curves: HX match/area/cost tables; HP pairing/ranking; summary sheets of QHU, QCU, QREC, COP, CAPEX, OPEX, CO₂, paybacks) for a different industrial process (from the one used in the main learning activity) to:
Identify the pinch point(s) and explain what the curves imply for recovery potential and bottlenecks.
Comment on QHU/QCU/QREC and how they change across the scenarios you run (e.g., ΔTmin, tariffs, emission factors, Top-N HP selection).
Interpret trade-offs among energy, CAPEX, OPEX, emissions, using numbers reported by the simulator. No calculations beyond light arithmetic/annotation.
2. Systems mapping and scenario reasoning
A concise system boundary sketch and a simple stream table.
A Causal Loop Diagram (CLD) highlighting key feedbacks (e.g., tariffs ↔ HP use ↔ grid carbon intensity ↔ emissions/cost).
A short MCDA (transparent criteria/weights) comparing the scenario variants you test; include a brief stakeholder reflection.
3. Decision memo (max 2 pages)
Your recommended integration option under stated assumptions, with one “what-if” sensitivity (e.g., +20% electricity price, tighter CO₂ factor).
State uncertainties/assumptions and any implementation risks (operations, fouling, timing of capital).
Students should include a short reflective note addressing assumptions, feedback insights, and how their stakeholder perspective shaped their recommendation.
Appendix A: Example process scenario for teaching activity:
Sample narrative: Large-scale food processing plant with time-sliced operations
The following process scenario explains the industrial context behind the main teaching activity simulations. A large-scale food processing plant operates a milk product manufacturing line. The process, part of which is shown in Fig. 1, involves the following:
Thermal evaporation of milk feed.
Cooking operations after other ingredient mixing and formulation upstream.
Oven heating to drive off moisture and stimulate critical product attributes.
Pre-finishing operations as the product approaches packaging.
In real operations, the evaporation subprocessoccurs at different times from the cooking/separation, oven and pre-finishing operations. This means that their hot and cold process streams are not simultaneously available for direct heat exchange. For a realistic industrial pinch analysis, the process is thus split into two time slices:
Time Slice A (used for scenario Case A): Evaporation streams only.
Time Slice B (Case B): Cooking/separation, oven and pre-finishing streams only.
Separate pinch analyses are performed for each slice, using the yellow-highlighted sections of Table 1 as stream data for time slice A, and the green-highlighted sections as stream data for time slice B. Any heat recovery between slices would require thermal storage (e.g., a hot-water tank) to bridge the time gap.
Fig.1. Simplified process flowsheet of food manufacturing facility.
Note on storage and system boundaries:
Because the two sub-processes occur at different times, direct process-to-process heat exchange between their streams is not possible without thermal storage. If storage is introduced:
Production surplus heat at time slice A can be stored at high temperature (e.g., 80 °C) and later discharged to preheat time slice B cold streams.
The size of the tank depends on the portion of hot utility demand of sub-process B to be offset, the temperature swing, and the duration of the sub-process B.
Table 1. Process stream data corresponding to flowsheet of Fig. 1. Yellow-highlighted sections represent processes available at time slice A, while green-highlighted sections are processes available at time slice B.
Appendix B: Suggested marking rubric (Editable):
Adopter note: The rubric below is a suggested template. Instructors may adjust criteria language, weightings and band thresholds to align with local policies and learning outcomes. No marks depend on running software.
1) Interpretation of Simulation Outputs — 25%
A (Excellent): Reads curves/tables correctly; uses QHU/QCU/QREC, COP, CAPEX/OPEX/CO₂, payback figures accurately; draws clear, defensible trade-offs.
B (Good): Mostly accurate; links numbers to decisions with some insight.
C (Adequate): Mixed accuracy; limited or generic trade-off discussion.
D/F (Weak): Frequent misreads; cherry-picks or contradicts generated data.
2) Systems Thinking & Scenario Analysis — 30%
A: Clear CLD with at least one reinforcing and one balancing loop; leverage points identified; scenarios coherent; MCDA with explicit criteria, weights, and justified ranking; uncertainty acknowledged.
B: Reasonable CLD; scenarios sound; MCDA present with partial justification.
C: Superficial CLD; scenarios/MCDA incomplete or weakly reasoned.
D/F: Little or no systems view; scenarios/MCDA absent or not meaningful.
Atuonwu, J.C. (2025). A Simulation Tool for Pinch Analysis and Heat Exchanger/Heat Pump Integration in Industrial Processes: Development and Application in Challenge-based Learning. Education for Chemical Engineers 52, 141-150.
Oh, X.B., Rozali, N.E.M., Liew, P.Y., Klemes, J.J. (2021). Design of integrated energy- water systems using Pinch Analysis: a nexus study of energy-water-carbon emissions. Journal of Cleaner Production 322, 129092.
Rosenow, J., Arpagaus, C., Lechtenböhmer, S.,Oxenaar, S., Pusceddu, E. (2025). The heat is on: Policy solutions for industrial electrification. Energy Research & Social Science 127, 104227.
Bale, C.S.E., Varga, L., Foxon, T.J. (2015). Energy and complexity: New ways forward. Applied Energy 138, 150-159.
Atuonwu, J.C. (2025). Proprietary Simulator for Pinch Analysis & Heat Integration. Private reviewer access available on request (demo video or temporary login).
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Licensing:This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It was originally developed as part of the Strathclyde Climate Ambassadors Networks (StrathCAN) at Strathclyde in collaboration with the Centre for Sustainable Development.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, and Critical Thinking INCOSE Competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):Integrated / Systems Approach (essential to the solution of broadly-defined problems), Problem analysis, Sustainability, and Science, mathematics, and engineering principles.
Educational level: Beginner.
Learning and teaching notes:
The two complementary workshops, Climate Fresk and En-ROADS, are introduced to situate Renewable Energy Technologies within the wider context of the Net Zero transition. Their purpose is first to deepen students’ understanding of the climate science that underpins climate change as the driver of technical innovation, and then to broaden awareness of the social, economic, political, and environmental factors that shape global decarbonisation pathways. Building on this foundation, the workshops shift focus to the policy levers required to enable systems change – highlighting, often with surprising insights, their relative effectiveness in reducing emissions and limiting temperature rise. Together, they provide the context for a Renewable Energy Technologies module, where attention turns to the role of renewables and other low-carbon technologies as climate solutions. The central takeaway is that there is no single “silver bullet” solution; instead, a coordinated “silver buckshot” approach is essential.
Learners do not require any prior learning in the area of climate change or climate solutions for these workshops. The workshops are the perfect introduction to these. Climate Fresk facilitator training begins with attending a workshop as a participant, followed by a session on facilitation. Staff can train via local workshops, the CF MOOC and peer-to-peer practice, or through official training, enabling institutions to build a self-sustaining community of facilitators at minimal cost.
Learners have the opportunity to:
Learn about the climate science that underpins the IPCC assessment reports.
Develop an understanding of systems thinking by applying it to climate science and the impacts of climate change, while also exploring the policy solutions needed to drive systems-level change.
Be Introduced to the En-Roads Climate Simulator tool and the possibility of becoming trained Climate Fresk Facilitators themselves.
Teachers have the opportunity to:
Actively engage the students in taking a systems thinking approach to their understanding of ‘the problem’ of climate change, and exploring ‘the solutions’ to it.
Use these workshops as stand-alone activities (for outreach) or embed within the curriculum.
Collaborate with other teachers and students, by training them as facilitators to support future workshops.
Engage students with different competencies such as systems thinking, critical thinking, and anticipatory thinking and highlight to students how these are relevant and applied.
This activity utilises off-the-shelf educational tools in the form of the Climate Fresk workshop and the En-Roads Climate Simulator tool. These are used in two separate (but connected) workshops adapted from the guided assignment that is presented in the resources above. These are not intended to be run back-to-back. In fact, some gap (of days) in between is desirable as together, this would be too exhausting, and also give time for reflection in between.
The Climate Fresk workshop is designed to build a solid understanding of cause-and-effect in climate systems. This develops baseline systems thinking without overloading students.
En-ROADS, is designed to engage students with the types of sectors that we need to decarbonise and get them thinking about the need for system wide policies to achieve system wide decarbonisation of our energy system.
Figure 1. Using Climate Fresk and En-ROADS as complementary workshops focusing on ‘the climate problem’ and ‘climate solutions’.
A key aspect of both workshops is highlighting the need for systems thinking in both understanding the problem of, and exploring the solutions to, climate change. This involves introducing students to the cause and effects of climate change, feedback loops and the concept of tipping points – both in terms of climate tipping points (BBC Sounds – The Climate Tipping Points, no date) that can potentially trigger irreversible changes in the climate system, as well as positive, social tipping points (TEDx Talks 2023) that once crossed can shift social norms, and how policies can affect this. It allows discussions around leverage points in the context of climate solutions and policies, which Donella Meadows describes as where “a small shift in one thing can produce big changes in everything”.
Part one: Climate Fresk workshop:Understanding the problem of climate change (or the ‘science piece’):
Overview:
Climate Fresk is a 2-2.5 hour facilitator-led gamified workshop based on the latest IPCC report, where participants work in groups to build a causal-loop diagram (or fresk) of the Earth’s climate system using specially designed cards. The activity encourages discussion, challenges assumptions, and develops systems thinking by illustrating the interconnections, feedback loops, and tipping points of climate change. In doing so, it supports UNESCO Education for Sustainable Development competencies in anticipatory and systems thinking, helping participants understand the potential impacts of climate dynamics on ecological, social, and economic systems in a way that is accessible.
Scalability and setup:
The Climate Fresk workshop can be delivered to almost any number of participants, limited only by the size of the space, the number of trained facilitators available, and the number of card decks. Participants are usually divided into groups of 8 (10 at a push), each working around a table roughly 2m x 1m in size. Each group (table) requires a dedicated facilitator to guide the process.
Facilitation and training:
Facilitators must be trained before running the activity. Training can be undertaken through official Climate Fresk courses (see resources) or, once enough experience has been built, in-house peer-to-peer training supported by staff development units. Facilitators use a “crib sheet” containing guiding questions and timings to help keep groups on track.
Workshop materials:
The Fresk uses a deck of 42 cards, each representing a cause, effect, or impact within the climate system-ranging from fossil fuel use across industry, buildings, transport and agriculture, to wider impacts on society, biodiversity, and ecosystems. Each card has a graphic on one side and explanatory text on the other, which participants use to determine its role in the system.
Learning process:
Over the session, participants collaboratively arrange the cards on a large sheet of paper to construct a causal-loop diagram of the climate system. In doing so, they identify drivers of carbon emissions, critical carbon sinks, feedback loops, and potential tipping points. The activity encourages discussion, challenges assumptions, and introduces key climate science terminology, while making visible the complex interdependencies of Earth systems.
Reflection and discussion:
After constructing the Fresk, participants are encouraged to reflect on how the process made them feel (normative competency) and what insights they gained. This is followed by open discussion on mitigation strategies and possible solutions. In a standard Climate Fresk workshop, around 45 minutes is devoted to this. However, when combined with the En-Roads simulator, this discussion naturally transitions from the “problem space” of the Fresk workshop to the “solutions space” of the subsequent En-Roads workshop, where participants explore the realistic impacts of different climate solutions, their co-benefits, and the equity issues they raise – engaging participants in deeper systems-level thinking.
Part two: En-ROADS workshop:‘Exploring the solutions’ to climate change (or the ‘policy piece’):
Overview:
This En-ROADS workshop can be run from one to two hours, and can follow a role-play format, where participants are asked to take on the role of policymakers, exploring different policy options to limit global temperatures to 1.5oC (or 2oC). They are introduced to the workshop by telling them “you are policymakers tasked with limiting global heating”. En-Roads is a climate change simulator developed by Climate Interactive and MIT that uses roleplay to explore global policy interventions for limiting temperature rise. It enables participants—from students to policymakers—to test combinations of climate solutions, examine trade-offs and unintended consequences, and understand that no single “silver bullet” exists. The workshop develops UNESCO Education for Sustainable Development competencies in anticipatory, systems, critical, and strategic thinking, while highlighting the challenges of achieving policy consensus across diverse stakeholders.
Preparation:
1. Participants
Suitable for classes split into small groups.
Works well in groups of 3–5 for discussions.
2. Facilitators
1 facilitator to introduce the tool and guide reflection.
Teaching assistants can circulate during group tasks to prompt discussion.
Handouts: list of possible climate “levers” (policy actions) – See resources – En-Roads Control Panel
Online polling software (e.g. Mentimeter)
Step-by-step instructions for 60-minute workshop (but can be expanded to 2 hours involving more discussion and more interaction with En-Roads simulator):
1. Introduction (5 mins)
Introduce EN-ROADS as an interactive simulator for testing climate solutions.
Explain the goal: Can we keep global heating below 1.5 °C?
Use causal loop diagram below to demonstrate how mitigating actions effect positive change.
Figure 2. Causal loop diagram showing how increasing GHG emissions drive climate action and emissions reduction.
2. Initial actions brainstorm (5 mins)
Ask cohort to use an online polling system, asking them to vote for their Topaction (from the list of mitigating actions – see resources – En-Roads Control Panel) that that they think will have the greatest impact on reducing emissions and temperature.
Figure 3. Example of a Menti poll to capture learners’ understanding of climate solution impacts.
Don’t show this yet – keep this for later, when you will ask them to vote again and can then compare how their views have changed from beginning to end of session.
Ask participants to self-sort into groups of 3–4, and now ask them to discuss what their Top 3 actions would be – they must agree on these.
3.Using EN-ROADS (15 mins)
As facilitator, ask one group to volunteer their top action. Demonstrate their action using the simulator with an example (e.g. applying a coal phase-out or carbon tax).
IMPORTANT: Before you implement the action – ask the group (and others) what they expect to happen to emissions and temperature.
Apply their action and ask if it is what they expected. Then discuss why it is, or is not, what they expected to see.
Highlight unintended consequences (e.g. rising energy prices and discussion on the real-world impacts this can have on local communities or households, land-use impacts).
This does require the facilitator to be familiar with the simulator and what these impacts might be and how to use the simulator to show these (there is a Climate Interactive course available (Training, no date), which is free, and there are also many resources available for individuals to work through at their own pace).
Can repeat this with other groups volunteering their top action. Ask for a group who has a different top action, or ask a group for their second top action to ensure you cover different levers.
4. Group simulation (15 mins)
Ask groups to now use En-Roads to consider other levers to get temperature as close to 1.5oC as possible, to determine whether their initial top 5 ranking has changed.
They should record how much each action reduces emissions and temperature rise.
Take the poll again.
Show both polls and look at differences – have they changed their views on certain actions – ask them why.
Discuss: Which actions had the biggest effect? Which had less than expected? Why?
5. Achieving 1.5 °C (10 mins)
Ask who reached 1.5oC
Ask them to show their combined actions.
Open up for discussion on how feasible these actions look – what are the implementation challenges associated with them. Are there risks of unintended consequences such as GDP, equity, biodiversity, etc?
6. Reflection and debrief (5 mins)
Discuss key takeaways:
The importance of combining multiple actions (“silver buckshot” rather than a silver bullet).
Trade-offs and co-benefits (e.g. health, equity, biodiversity).
The role (and challenge) of collaboration and consensus in policy decisions.
7. Post Workshop – optional
Challenge groups to adjust policies and combinations of levers to approach the IEA’s Net Zero 2050 (1.5 °C) pathway, and compare their pathways with this. Gives them a sense of how challenging/possible the net-zero pathway will be.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Who is this article for?: Thisarticle should be read by educators at all levels in higher education who are seeking an overall perspective on teaching approaches for integrating complex systems in engineering education.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).
Premise:
Engineering education is undergoing a fundamental transformation. The convergence of technological, social, and environmental challenges demands that future engineers move beyond procedural problem-solving toward complex thinking – a mindset capable of navigating uncertainty, interdependence, and dynamic change. This shift has been accelerated by advances in Artificial Intelligence (AI), which have redefined both the nature of engineering practice and the competencies students must develop to thrive in it.
For scientists and engineers, understanding complex systems is critical for the ability to apply knowledge and techniques across diverse contexts. This is particularly visible in fields such as bioengineering, which depends on advances in chemistry, physics, computing, and other engineering disciplines. Such integration requires designing subsystems where engineering expertise can be meaningfully applied. Complex systems also involve human interaction, introducing unpredictability, feedback loops, and uncertainty. Modern AI-enabled systems—ranging from autonomous vehicles to smart grids and biomedical devices—cannot be fully understood through a single traditional discipline. These systems are not simply complicated; they are interconnected, dynamic, and often nonlinear (Jakobsson, 2025).
What this means for engineering education and educators:
Across the globe, educators have turned to Problem-Based Learning (PBL) as a central strategy for cultivating systems-oriented thinking. For instance, Tauro et al. (2017) and the case study conducted at Tishk International University demonstrate that integrating PBL within mechatronics education enhances students’ ability to connect theory with practice, encouraging collaboration and creativity in addressing multifaceted engineering problems. Similarly, Watters et al. (2016) show that industry–school partnerships transform classrooms into real-world laboratories, reinforcing the value of experiential learning and knowledge transfer between academia and professional practice. These initiatives reflect a broader movement toward authentic, interdisciplinary engagement, a necessary foundation for understanding and designing complex systems.
However, adopting PBL and interdisciplinary methods is not only a pedagogical improvement but also an epistemological necessity. As Stegeager et al. (2024) emphasise, educators themselves must evolve from instructors to facilitators, cultivating reflective and adaptive learning environments that mirror the complexity of professional engineering contexts. Mynderse et al. further highlight that when students are given responsibility for solving open-ended problems, they report higher satisfaction and deeper conceptual integration. These outcomes suggest that active learning approaches foster the kind of complex, interconnected reasoning required for contemporary engineering practice.
In parallel, the AI-driven classroom is transforming the educational landscape. Emerging evidence shows that generative AI tools support personalised learning and immediate feedback, freeing educators to focus on mentorship and creativity (Jaramillo, 2024). Yet this technological advancement also underscores the limits of automation. Machines can model and predict, but they cannot interpret ethical implications, reconcile trade-offs, or integrate human and ecological perspectives. This is where complex thinking becomes indispensable: it enables learners to understand AI not merely as a computational tool but as a component within broader sociotechnical systems.
The need for complex systems understanding is especially acute in fields such as bioengineering and mechatronics, where technologies intersect with living systems and social contexts. The defining feature of complex systems is the interaction among multiple components that produce emergent, often unpredictable behaviour. For engineering students, grasping these principles means developing the ability to think beyond linear causality and to engage with feedback loops, uncertainty, and adaptive design.
The imperative to transform engineering education:
In traditional engineering education, students get topics presented in discrete classes. They get trained in thermodynamics and fluid mechanics and they often forget what they have learned by the time they are at the control systems course where there is an opportunity to bring together skills from prior knowledge. This modularised model is already losing its effectiveness in preparing the students for encountering real-world problems. As the adage says, “In theory, theory and practice are the same; in practice, they are not”. Understanding the role of noise, measurement errors, simplifying assumptions and computational errors play an essential role. To this end, it is crucial to centre complex system design and embrace interdisciplinarity to develop a competency that supports life-long, adaptive learning.
As an example, Aalborg University in Denmark stands as a global exemplary of systems-oriented engineering education. Its PBL model is not an add-on; it is the spine of the entire curriculum. Every semester, students tackle a new problem – often tied to societal needs such as urban planning, environmental sustainability, or healthcare. Students must identify relevant knowledge areas, work collaboratively across disciplines, and reflect on both process and outcome. Faculty report that this structure promotes holistic thinking, resilience, and a sense of professional identity early on the students’ journeys (Kolmos et al. 2008).
On the undergraduate level, capstones are a common part of engineering education which happens at the late stages of the student’s studies. At Rowan University (New Jersey, USA), Engineering Clinics provide a different but equally powerful model. Students work across all four years on interdisciplinary teams, contributing to faculty research or industry-sponsored projects. These clinics are embedded in the curriculum and require students to engage deeply with current research problems, often involving complex technical and human systems. A junior clinic project, for example, might involve the optimisation of a renewable energy system integrating mechanical, electrical, and computer engineering principles. Therefore, students learn to navigate ambiguity, collaborate with experts, and see the relevance of their disciplinary knowledge in a broader context by confronting the messy nature of real data.
These are two of many examples where systems thinking is cultivated. Students gain exposure to open-ended problems and practice seeking connection across domains as they encounter the limits of their knowledge. In this fast-moving era, crossing disciplines empowers students for lifelong adaptation, allowing them to incorporate their experiences into any new technological developments. It also encourages treating learning as a collaborative social process, rather than a solo race to secure the first job.
Educators must do more than just deliver content; they also need to act as facilitators and learn alongside their students. By redesigning the curriculum around design-oriented problems that mirror real-world changes, higher education will better prepare future engineers to face upcoming systemic global challenges.
Looking ahead:
As artificial intelligence and automation continue to reshape industry, engineering education must also evolve. Integrating complex systems into teaching offers students the opportunity to engage directly with the data-driven ecosystem they will encounter in practice. The goal is not only to produce technically skilled engineers, but also thoughtful stewards of technology who can navigate its broader social and ethical dimensions.
One ongoing challenge is that independent projects often vary in quality and can be difficult to assess. Without intentional design, students may default to trial-and-error approaches instead of drawing on knowledge from prior courses. At the same time, the pressure to cover extensive technical material can make it difficult to provide the broader systems context essential for modern engineering. Yet when learning is reinforced across the curriculum, students are better prepared for future careers that demand systems-based thinking.
Experiential, self-directed projects play a crucial role in this preparation. They allow students to choose their own path while working closely with advisors and industry partners. Whether developing a product, designing a system, or engaging with professionals, students gain a perspective that feels different from traditional coursework. This process offers them a glimpse of what it means to think and act like real engineers, fostering both confidence and adaptability as they transition from the classroom to the workplace.
References:
Jakobsson, Eric et al. (1999) ‘Complex systems: Why and what?’, New England Complex Systems Institute. Available at: https://necsi.edu/complex-systems-why-and-what (Accessed: 16 July 2025).
Stegeager, N., Traulsen, S., Carvalho Guerra, A., Telléus, P., Du, X. (2024) ‘Do good intentions lead to expected outcomes? Professional learning amongst early career academics in a problem-based program’, Education Sciences, 14(2), p. 205. Available at: https://doi.org/10.3390/educsci14020205.
Tauro, F., Cha, Y., Rahim, F., Rasul, M.S., Osman, K., Halim, L., Dennisur, D., Esner, B., Porfiri, M. (2017) ‘Integrating mechatronics in project-based learning of Malaysian high school students and teachers’, International Journal of Mechanical Engineering Education. Available at: https://journals.sagepub.com/doi/full/10.1177/0306419017708636 (Accessed: 1 August 2025).
Watters, J., Pillay, H. and Flynn, M. (2016) ‘Industry-school partnerships: A strategy to enhance education and training opportunities. Australia’, Queensland University of Technology. Available at: https://eprints.qut.edu.au/98390/ (Accessed: 30 July 2025).
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Licensing:This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It is based upon the author’s article “Enhancing Ethical Reasoning in Engineering Education through Student-Created Interactive Ethical Scenarios Using Generative AI,” 2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025, pp. 1-5, doi: 10.1109/EDUCON62633.2025.11016531.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Life Cycle, Configuration Management, Requirements Definition, Verification, and Validation INCOSE Competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses AHEP themes of Ethics and Communication.
Debug their simulation through playtesting, documenting issue → fix → retest cycles and demonstrating how changes improve coherence.
Explore trade-offs and justify decisions in ethics (e.g. consequences and equity) and complex systems (e.g. resilience vs cost vs emissions).
Evidence learning with transparent artefacts: initial prompt, changes via tracked changes or before/after snippets, tester feedback, and final prompt.
Reflect critically on validity, bias and the limitations of LLMs as simulators, including how to handle unsafe/poor choices by surfacing realistic consequences.
Communicate findings clearly to technical and nontechnical audiences.
Teachers have the opportunity to:
Use this as either a studio activity (3–5 sessions) or a compact assessment only task (1–2 sessions), with clear rubrics for each.
Standardise scope by offering a predefined scenario (e.g., Urban Heatwave Response, UK city), or permit student proposed topics.
Scale marking via artefact based evidence (prompt, change log, feedback, final prompt) rather than long reports.
Deliver with institutional Microsoft Copilot licences or any free web LLM; require students to disclose model and version used.
Adapt quickly to different disciplines by swapping the scenario pack (microgrids, water networks, medical device supply chains, etc.).
Overview:
This resource enables engineering students to create, run, and debug a text‑based, interactive simulation of a complex sociotechnical system using a Large Language Model (LLM). It is intentionally flexible and may be delivered as a multi‑session studio activity (including assessment) or used solely as a compact assessment.
Purpose and use:
In both modes, students design a robust text prompt, test it with a user, document changes, and submit auditable artefacts that evidence learning. The key activity is interrogating their own thinking on how complex systems should be modelled by making judgements as to how their game does and does not capture the system dynamics.
The submission is a text LLM prompt with tracked changes, which allows students to demonstrate system design and debugging, produce transparent process evidence, and scale to large cohorts with minimal infrastructure.
Delivery options at a glance:
Audience
Undergraduate Years 2–4 and taught MSc, any engineering discipline
Modes
Studio activity (3–5×2 h + independent study) or Assessment‑only (prompt‑only; 1–2×2 h + 4–6 h)
Teams
3–4 students (solo permitted for assessment‑only)
Assessment
Portfolio (studio) or prompt‑plus‑change‑log (assessment‑only)
Platforms
Institutional Copilot licences successful; encourage exploration of free tools (students record model/version)
Materials and software:
LLM access: institutional Microsoft Copilot licences (proven) or any reputable free web‑based tool. Students disclose the model and version.
Delivery modes:
Mode A — Studio activity (3–5 sessions)
Session 1: Frame the system — boundary, stakeholders, conflicting goals; sketch a Causal Loop Diagram (CLD) with at least two reinforcing and two balancing loops.
Session 2: Make it playable — define 4–8 state variables and KPIs; draft the prompt (based on Appendix A); specify commands, turn length and stop conditions; add debug controls (`trace`, `why`, `show variables`, `revert`).
Between sessions: Prototype v1 — run 10–15 turns; capture a transcript; log defects (e.g. inconsistent updates, missing delays, moralising responses).
Session 3: Play‑test and iterate — exchange prototypes across teams or test with an external user; record issue → fix → re‑test cycles with evidence (make sure edits are captured in tracked changes).
Session 4: Present and reflect — short demo (6–8 turns); explain how feedback/delays manifest; discuss surprises and limits.
Mode B — Assessment‑only (prompt‑only; 1–2 sessions)
Session 1: Brief and rapid scoping — select a scenario (student‑chosen or predefined); write a one‑paragraph boundary and stakeholders note; draft the initial prompt (based on Appendix A) with role choices, 4–6 state variables, simple commands, and a 12–15 turn cap.
Independent work: Debugging loop — run the prompt; identify faults; edit the prompt (make sure edits are captured in tracked changes); re‑run and capture short snippets demonstrating fixes; test with one peer and collect written feedback.
Session 2: Submission — students submit a single document with the initial prompt, change log (before/after snippets), tester feedback, the final prompt, and a short rationale of innovative choices.
In both modes, module leaders may supply a predefined scenario(s) to standardise scope and simplify marking. A ready‑to‑use example is provided in Appendix C.
Critical medical device supply chain — redundancy vs cost; equitable allocation.
Appendix A — Prompt template (simulation + debug‑ready):
Title: Complex Systems Simulator — [Scenario]
Purpose: Run a turn‑based interactive simulation of a complex sociotechnical system. Track named state variables, apply feedback and delays, and let the player’s decisions drive non‑linear outcomes.
Setup:
1) Offer three roles (distinct authority/constraints).
2) Introduce 3–5 NPCs with clear goals and plausible interventions.
3) Show a dashboard of [STATE_VARIABLES] each turn with short context.
State rules:
Track only these variables (with units/ranges): [list 5–8].
Maintain at least two feedback loops and one delay; keep hidden rule notes consistent across turns.
Each turn: recap; propose 3–5 options (plus free‑text); explain updates; show dashboard; request the following action.
Time step: 5 minutes to 1 week; end after 20–30 turns or on stop conditions.
Commands: status, talk [npc], inspect [asset], implement [policy], pilot [intervention], advance time, review log.
Debug commands (for testing): trace on/off (print update logic), why (state which loops/delays drove the change), show variables (print current state table), revert (roll back one turn), reseed (slight exogenous shock).
Realism and ethics: Allow all plausible actions and report consequences neutrally. If unsafe in the real world, refuse, propose safer alternatives, and continue with plausible systemic effects.
LLM pitfalls to avoid: Do not invent new variables; ask clarifying questions rather than guessing; keep outputs concise; summarise trajectory every five turns.
Begin: Greet the player, state the scenario, ask for a role, and wait.
Appendix B — Debugging and play‑test checklist:
Functional coherence
Do state variables update consistently with declared logic?
Are reinforcing and balancing feedback identifiable in play?
Robustness
Does the simulation permit negative choices with realistic consequences?
Do trace/why explanations match outcomes?
Are stop conditions respected?
User experience and clarity
Are commands clear? Is turn pacing appropriate?
Are dashboards concise and informative?
Report
Provide three concrete defects with turn numbers, the prompt edits that fixed them, and evidence of the re‑run.
Appendix C — Predefined scenario (Urban Heatwave Response, UK city):
Boundary: One UK local authority area during the July–August heatwave period. Focus on public health, energy demand, and community resilience.
Roles: (1) Local Authority Resilience Lead; (2) NHS Trust Capacity Manager; (3) Distribution Network Operator (DNO) Duty Engineer.
Stakeholders: Residents (with a focus on vulnerable groups), care homes, schools, SMEs, DNO, local NHS Trust, emergency services, voluntary/community groups, Met Office (for alerts), and local media.
State variables (examples): Heat‑health alert level (0–4); Emergency Department occupancy (%); Electricity demand/capacity (% of peak); Indoor temperature exceedance hours (hrs > 27 °C); Public trust (0–100); Budget (£); Equity index (0–100).
Events/shocks: Red heat alert; substation fault; procurement delay; misinformation spike on social media; transport disruption; community centre cooling failure.
KPIs and stop conditions: Heat‑related admissions; unserved energy; cost variance; equity gap across wards. Stop if alert level 4 persists >3 days, budget overspends >10%, or trust <25.
Notes for assessors: Using a standard, predefined scenario simplifies marking and ensures comparable complexity across teams, while still allowing for diverse strategies and outcomes.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Requirements Definition, Communication, Design For, and Critical Thinking INCOSE Competencies.
AHEP4 mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses the themes of Sustainability and Communication.
Educational level: Beginner; intermediate.
Acknowledgement:
The case study underpinning this teaching activity was developed by Prof. Kristen MacAskill (University of Cambridge). The Module was first developed and implemented in teaching by TEDI- London, led by a team of learning technologists, Ellie Bates, Laurence Chater, Pratishtha Poudel, and academic member, Rhythima Shinde. This work was carried out in collaboration with the Royal Academy of Engineering through its Engineering X programme — a global partnership that supports safer, more sustainable engineering education and practice worldwide. With critical support from Professor Kristen MacAskill and involvement of Ana Andrade and Hazel Ingham, Aisha Seif Salim. This was a collective effort involving many individuals across TEDI-London and RAEng (advisors and reviewers), and while we cannot name everyone here, we are deeply grateful for all the contributions that made this module possible.
Learning and teaching notes:
This case study introduces a structured, systems-thinking–based teaching resource. It provides educators with tools and frameworks—such as the Cynefin framework and stakeholder mapping—to analyse and interpret complex socio-technical challenges. By exploring the case of the Queensland, Australia floods, it demonstrates how engineering decisions evolve within interconnected technical and social systems, helping students link theory with practice.
The Cynefin framework (Nachbagauer, 2021; Snowden, 2002), helps decision-makers distinguish between different types of problem contexts—simple, complicated, complex, chaotic, and disordered. In an engineering context, this framework guides learners to recognise when traditional linear methods work (for simple or complicated problems) and when adaptive, experimental approaches are required (for complex or chaotic systems).
Within this teaching activity, Cynefin is used to help students understand how resilience strategies evolve when facing uncertainty, incomplete information, and changing stakeholder dynamics. By mapping case study events to the Cynefin domains, learners gain a structured way to navigate uncertainty and identify appropriate modes of action.
This case study activity assumes basic familiarity with systems concepts and builds on this foundation with deeper application to real-world socio-technical challenges.
Summary of context:
The activity focuses on a case study of 2010–2011 floods in Queensland, Australia, which caused extensive damage to urban infrastructure. The Queensland Reconstruction Authority (QRA) initially directed resources to short-term asset repairs but subsequently shifted towards long-term resilience planning, hazard management, and community-centred approaches.
The case resonates with global engineering challenges, such as flood, fire, and storm resilience, and can be easily adapted to local contexts. This case therefore connects systems thinking theory directly to engineering and governance decisions, illustrating how frameworks like Cynefin can support engineers in navigating uncertainty across technical and institutional domains.
Learning objectives:
Aligned with AHEP4 (Engineering Council, 2020) – Outcomes 6, 10, and 16 on systems approaches, sustainability, and risk – this activity emphasises systems thinking, stakeholder engagement, problem definition, and decision-making under uncertainty.
This teaching activity introduces learners to the principles and practice of systems thinking by embedding a real-world case study into engineering education (Godfrey et al., 2014; Monat et al.,2022). The objectives are to:
Enable students to recognise interconnections, interdependencies, and evolving behaviours of stakeholders within socio-technical systems.
Support learners in applying systems frameworks —particularly the Cynefin framework and stakeholder mapping—to analyse complexity, uncertainty, and decision-making in climate resilience and disaster mitigation contexts.
Apply systems thinking tools and frameworks to real-world challenges, such as climate resilience and disaster mitigation.
Strengthen confidence in addressing uncertainty and complexity in engineering problem-solving.
Collaborate effectively across diverse teams, appreciating multiple stakeholder perspectives.
Reflect critically on trade-offs and decision-making in engineering practice.
Equip students to transfer systems insights from case-based scenarios to broader projects in their curriculum and future professional practice.
Teachers have the opportunity to:
Introduce students to complex systems concepts through engaging, real-world case studies.
Facilitate interactive, blended learning using narrative-driven tools, explainer animations, and role-play exercises.
Assess learners’ baseline and improved understanding of systems thinking through pre- and post-module surveys.
Guide students in navigating multiple systems frameworks while managing cognitive load.
Encourage interdisciplinary collaboration and stakeholder-focused analysis within classroom or project-based settings.
Adapt and scale the teaching activity for different educational levels, contexts, and case study themes (e.g., floods, wildfires, extreme heat).
This dedicated platform hosts the interactive modules designed for this teaching activity. Students progress through three modules — Context, Analysis and Insights, and Discussion and Transferable Learning. Each module includes animations, narrative-driven content, scenario prompts, and interactive tasks. The platform ensures flexibility: it can be used in fully online, hybrid, or face-to-face settings. All necessary digital assets (readings, maps, videos, and quizzes) are embedded, so learners have a “one-stop” environment.
The core teaching narrative is anchored in this Engineering X case study. It documents the evolution of the Queensland Reconstruction Authority (QRA) from a short-term flood recovery body to a long-term resilience institution. This resource provides students with authentic socio-technical detail — including stakeholder conflicts, institutional learning, and systemic barriers — which they then interrogate using systems thinking frameworks.
This resource provides a suggested delivery schedule for facilitators. It maps when live sessions, asynchronous tasks, and group discussions should occur, ensuring students remain engaged over the course. It also indicates where key reflective points and assessments (both formative and summative) can be integrated.
5. Pre- and post-module assessment form: (Appendix C)
This tool evaluates students’ systems thinking learning outcomes. It includes:
Baseline survey: assesses initial understanding of systems thinking, approaches to complex problems, and confidence in collaboration.
Scenario-based survey: applies systems thinking questions to a specific context (e.g. extreme monsoon rains and flooding in the case of Sakura Cove – as per the group assignment in module 1).
Post-module survey: measures changes in understanding, confidence, and skills, while also capturing qualitative reflections on learning.
The form provides both quantitative data (Likert scales) and qualitative insights (open-ended reflections), enabling robust evaluation of teaching impact.
Assessment:
Formative: Pre- and post-module surveys assess changes in learners’ self-reported understanding of systems thinking (Appendix A). Facilitators may adapt reflective prompts and scenario-based activities as part of coursework.
Summative (optional): Students can integrate insights into ongoing design projects (e.g. climate resilience in urban redevelopment), with assessment based on problem analysis, stakeholder engagement, and solution development.
Narrative of the case:
Learners are introduced to the case via a fictional guide, “Bernice,” who frames the scenario and supports navigation through the material. Students work through three stages that progressively apply the Cynefin framework and other systems tools to understand how resilience emerges through evolving governance and engineering responses:
1. Context module:
Initial Mandate: Students explore how the QRA was first tasked with rapid technical recovery—fixing roads after flood damage.
Narrative Depth: They study the Queensland floods of 2010–11 not just as a physical shock, but as a systemic stress test on multiple layers: infrastructure, governance, and community systems.
2. Analysis & insights module:
Framework Application: Learners apply systems frameworks (e.g., Cynefin, stakeholder maps) to see how QRA’s remit expanded over time—from asset restoration to hazard anticipation and community resilience.
Knowledge Types: Students distinguish between explicit knowledge (e.g., rebuild standards, hydrology data) and tacit knowledge (e.g., local inter-agency trust, relational coordination).
Governance Layers: Activities explore how resilience depends on multi-level governance, local-state-federal coordination, and overcoming systemic barriers like funding cycles or short-lived institutional mandates.
3. Discussion & transfer learning module:
Reflective Debate: Students weigh whether engineering alone can deliver resilience, or whether social relationships and institutions are equally critical.
Barrier Identification: They debate typical constraints—political, funding, institutional—and propose ways systems thinking can mitigate them.
Transfer Lab: Learners apply the evolved QRA model to other scenarios—e.g., urban heat adaptation or wildfires—considering both technical measures and governance dynamics.
Interactive learning design:
The teaching activity integrates multiple interactive elements to immerse students in systems thinking:
Role-play simulations: Learners take on the role of Queensland Reconstruction Authority (QRA) decision-makers, negotiating trade-offs between immediate engineering fixes and long-term institutional resilience. This requires balancing technical priorities with building trust, relationships, and governance capacity.
Scenario challenges: Students are presented with governance disruptions (e.g. funding cuts, loss of stakeholder trust, leadership turnover). They must reframe solutions using systems approaches, moving from reactive technical patchworks towards adaptive, capacity-building strategies.
Interactive digital tools: The online platform provides hotspot maps for exploring interdependencies, drag-and-drop activities for categorising frameworks, explainer animations, and AI-driven chatbot negotiations with sceptical stakeholders. These exercises develop critical and applied problem-solving skills.
Collaborative reflection: Group discussions and peer-to-peer feedback allow learners to surface diverse perspectives, debate trade-offs, and integrate insights into ongoing project briefs.
Why this approach adds value:
Although rooted in social-technical interactions, the activity explicitly connects systems thinking to core engineering design competencies—problem framing, stakeholder analysis, and iterative solution development under uncertainty
Holistic understanding of resilience: Students experience resilience as more than just technical recovery. They engage with a dynamic system that includes knowledge creation, governance evolution, and social relationships.
Adaptive systems thinking in action: The evolving narrative demonstrates how system boundaries shift over time, and how sustainable outcomes require not only engineering but institutional and cultural change.
Direct relevance to real-world engineering: The case mirrors global infrastructure challenges where effective disaster response and resilience planning depend on the interplay between technical solutions, governance capacity, and community engagement.
Guided questions and activities:
Facilitators can use these prompts to stimulate inquiry and structured reflection:
Who are the key stakeholders in the QRA flood response, and where do their priorities align or conflict?
How do feedback loops and interdependencies influence resilience planning?
What trade-offs exist between rapid repair and long-term resilience?
How can systems frameworks such as the Cynefin model or stakeholder mapping guide decision-making under uncertainty?
In role-play: how would you convince a sceptical funder (AI chatbot) to invest in resilience measures?
How could lessons from flood mitigation be applied to other contexts such as wildfire or urban heat resilience?
Opportunities for extension:
In addition to the Queensland floods and Sakura Cove examples, educators may draw parallels with urban heat planning in London, wildfire adaptation in Australia, or storm resilience in the Netherlands. These comparative cases allow learners to generalise systems insights beyond one event or geography.
The activity is designed to be scalable and adaptable:
Broader case study base: Educators can expand beyond flood resilience to include wildfire, storm, or extreme heat events.
Integration with larger modules: The activity can be embedded into project-based learning modules (e.g. urban redevelopment, transport network resilience).
Advanced complexity: For higher-level learners, facilitators can introduce additional frameworks (e.g. agent-based modelling, system dynamics) to deepen analysis.
This flexibility allows educators to tailor the activity to their students’ level of expertise, institutional context, and disciplinary focus.
References:
Design Council. (2021). Beyond Net Zero: A systemic design approach. Design Council.
Godfrey, P., Crick, R. D., & Huang, S. (2014). Systems thinking, systems design and learning power in engineering education. International Journal of Engineering Education.
Monat, J., Gannon, T., & Amissah, M. (2022). The case for systems thinking in undergraduate engineering education. International Journal of Engineering Pedagogy, 12(3), 50–88.
Nachbagauer, A. (2021). Managing complexity in projects: Extending the Cynefin framework. Project Leadership and Society, 2, 100017.
Snowden, D. (2002). Complex acts of knowing: paradox and descriptive self‐awareness. Journal of knowledge management, 6(2), 100-111.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Who is this article for?: This article should be read by educators at all levels of higher education looking to embed and integrate complex systems topics into curriculum, module, and / or programme design.
Premise:
Teaching and learning engineering carries with it a double layer of complexity. On the one hand, this complexity is connected to the growing interdisciplinary nature of engineering itself. On the other hand, the complexity is connected to the growing diversity of engineering students that are often present in one project team. This multifaceted complexity requires a re-envisioned understanding of the role and purpose of the engineering educator.
With the growing trend of a global classroom reality, we often find that learners in the classroom are representing different cultures, which in turn are rooted in them unconsciously carrying historical and socio-cultural baggage relating to these cultures. Thus, it becomes crucial to unpack the challenge and potential that such a diverse collective intelligence can offer to an engineering learning experience.
As our understanding of the engineering discipline gets more rooted and interconnected with the precarious reality that our world is witnessing today, it becomes essential that the engineering education community would take up a proactive role in actively contributing to the formation of engineering citizenship. In other words, every engineering student should be educated as a citizen that has mastered the engineering cross-cutting fields in such a way that they are free to create and solve problems of the present and the future.
With this in mind, it becomes very clear that the one-size-fits all model of a single discipline engineering classroom can no longer sustain itself. It does not factor in the richness that a diverse student body can offer, and it dilutes the value and potential of an engineering learner to think clearly or solve problems. It is therefore imperative that engineering educators grasp the complex reality of an integrated engineering discipline and address it in a way that fosters scaffolding of diverse knowledge. Some students might specialise in one core technical discipline. Yet, future projections for most students showcase the need to have a wide level of exposure to broader competency development. Students need to learn to understand the field of engineering at large and to develop system thinking skills that enable them to exist, challenge and have an impact on the system that they are a part of.
How to scaffold learning outcomes in a complex engineering curriculum:
The below table has been designed for embedding Complex Systems Learning Outcomes across an engineering curriculum. It maps against competencies and suggests scaffolding techniques across educational levels. It is also important to note, that efforts need to be made to align to the relevant AHEP requirements or other accreditation standards. Table 1 presents the different strands of the Complex Systems Engineering Curriculum, colour coded in line with the INCOSE Competency Framework outline (INCOSE, 2025). Table 2 presents a practical guide for educators to scaffold Complex Systems learning outcomes across a curriculum. The intention is for the scaffolding framework to compare the trade-offs between different elements of the competency group. For example, system modelling and analysis as an element from the core competency and planning from the management competency. The table suggests activities that would integrate different competencies together in a scaffolded approach.
Table 1 presents Competency Areas for Complex Systems. As mentioned, the skills range to include a wide variety of competencies, thereby enabling a solid and grounded systems thinking approach for students. As students approach their learning, they go through a series of development stages that gradually build up student level of expertise until they reach the stage of what the INCOSE competency framework refers to as a lead practitioner role. Building on the competencies of the complex system toolkit presented in Table 1, Table 2 presents a potential outline for a scaffolding framework that maps varying threads of the framework in a way that enables scaffolded activities at every developmental stage for learners. Depending on the learning context and educational level, educators can choose which level of attainment is appropriate to their curriculum.
As we are approaching the fuzzy front end to complexity in engineering pedagogy, as educators we need to be constantly toggling between devising frameworks, being informed by literature, contextualising ideas, validating these in our classrooms and repeating this cycle to continually fine-tune our complex teaching navigational complexity framework. The invitation is open for all educators who would like to connect as we continue to explore different ways of developing responsible engineers who leave a lasting and sustainable mark transforming their stationed realities.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
The EPC’s Complex Systems Toolkit provides accessible, practical resources for embedding complex systems into engineering education. The Complex Systems Toolkit is supported by Quanser.
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