Toolkit: Complex Systems Toolkit.

Author: Dr. James E. Pickering, PhD, SFHEA, MIET, MInstMC (Harper Adams University); Dr. George Amarantidis (MathWorks).

Topic: Developing competence in model-based systems engineering.

Title: Practical control engineering education through the ACE-Model.

Resource type: Teaching activity.

Relevant disciplines: Systems engineering; electrical engineering; control engineering.

Keywords: Available soon.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

Downloads: A PDF of this resource will be available soon.

Learning and teaching resources:

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, Integration, and Technical Leadership 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). 

Educational level: Beginner; intermediate. 

 

Learning and teaching notes:

Modern engineering is increasingly digital, interconnected, and system oriented. To prepare students for this evolving landscape, the Automatic Control Engineering (ACE) Model offers  a systems-driven, application-focused framework for practical control engineering education. Developed through a MathWorks-funded project launched in the summer of 2025, the ACE-Model unifies three complementary components that together cultivate systems thinking and model-based systems engineering competence: 

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

What does the ACE-Model consist of?:

 

Figure 1: The ACE-Model: Integrating the Toolkit (ACE-Box), with the Processes (ACE-CORE), to Lead to the Real-Life Application (ACE-Apply) to build a progressive mastery in Automatic Control Engineering (ACE).

The ACE-Model is closely aligned with Bloom’s Taxonomy, see (Anderson and Krathwohl, 2001) and Figure 2(a) providing a structured pathway for students to progress through the cognitive hierarchy, while developing capabilities across multiple levels of system abstraction. Figure 2(b) offers a schematic view of the three stages of the ACE-Model, as introduced in Figure 1. An initial overview of the ACE-Model is presented here, with further details provided in the following sections.  

 The ACE-Box is a portable, self-contained hardware tool that brings ACE to life beyond the traditional costly, full-scale laboratories. All that is required is a laptop and the ACE-Box. Designed to support the ACE-CORE methodology, ACE-Box can be set up on a desk, in a classroom, or even at home. MATLAB and Simulink serve as the primary platforms for model-based design, enabling system modelling, control system development, and the deployment of control algorithms to physical hardware (e.g. Arduino Uno) through code generation tools. 

 ACE-CORE guides learners through successive levels of Bloom’s framework: 

At each stage of CORE, learners move from recognising system components to synthesising complex interactions, mirroring the systems engineering lifecycle from requirement capture through verification and validation. This alignment supports AHEP4’s emphasis on analytical and problem-solving competence and INCOSE’s System Definition and Integration competencies. 

Finally, learners progress to Create, the highest stage of Bloom’s Taxonomy, by applying their knowledge to design complete control systems for real-world applications such as drones, vehicles, and automation systems. In this way, the ACE-Model scaffolds learning in parallel with Bloom’s progression, from foundational comprehension to advanced problem-solving, design and innovation. 

Together, these three pillars form a cohesive learning ecosystem: the toolkit, the process, and the application. 

Figure 2: Bloom’s Taxonomy (Anderson and Krathwohl, 2001) (a) and the ACE-Model Three Stages (b).

 

Collaborative community:

The ACE-Model ‘sits’ within the ACE-Lab, a collaborative community of academics and industry professionals committed to developing, validating, and disseminating open-access systems education resources. The ACE-Lab approach embodies complex adaptive systems principles, where the community evolves through continuous feedback, iteration, and co-design. Membership to the ACE-Lab is open to anyone who shares our vision of advancing control engineering teaching tools and practices. Through this approach, the ACE-Model equips graduates with the knowledge and hands-on skills required to excel in modern ACE careers. Find out more about the ACE-Lab through the following website: www.ace-lab.co.uk 

As an evolving community, ACE-Lab continually expands its open-access content through the active contributions of its members. New materials are regularly developed and shared, ensuring the resources remain current and relevant. Through this dynamic, collaborative approach, embodied in the ACE-Model, students not only gain technical knowledge but also develop the capacity to understand, navigate, and work effectively with complex, interconnected engineering systems. 

 

ACE-Box: The toolkit:  

The ACE-Box is based on the early development work of Control-Lab-in-a-Box (Pickering, 2023; 2025). CLB integrates sensors, actuators, and microcontroller to allow students to experience dynamic behaviour, and feedback control. 

 For now, two ACE-Box kits have been developed: 

1. Base and sense 

2. Actuate  

 The ACE-Box (base and sense) is illustrated in Figure 3, with the 15 key components labelled, along with an exploded view of the main parts in Figure 4. The ACE-Boxes integrate the essential microcontrollers, electronics, sensors, and actuators needed to design, implement, and test elements of digital control algorithm development, e.g. control algorithms in real time. It bridges the gap between theory and practice, allowing learners to see how abstract concepts behave in physical systems. The ACE-Box is also available as an open-access resource, with laboratory exercises included, with details provided later in this article. The ACE-Box (labelled (1) in Figure 3) and the tray (labelled (2) in Figure 3) are manufactured using 3D printing, with the necessary files available on the project website referenced above. A list of the required components and their sources is also provided on the project website, corresponding to labels (3) to (15) in Figure 3. Due to the open-source design of ACE-Lab, the library of exercises will continue to expand, supported by contributions from both academia and industry. The ACE-Box (Actuate) is illustrated in Figure 5, with the key actuator components detailed in (a), along with some typical lab set-ups (b, c and d). Figure 6 illustrates both the ACE-Box (Base + Sense) and also ACE-Box (Actuate).    

Figure 3: The ACE-Box (Base and Sense).

 

Figure 4: Assemble of the 3D Printed ACE-Box (Base and Sense).

 

Figure 5: The ACE-Box (Actuate).

 

Figure 6: ACE-Box (Actuate) Alongside the ACE-Box (Base + Sense).

 

ACE-CORE: The methodology:

ACE-CORE is a four-step framework designed to scaffold learning from components to system-of-systems understanding: 

The methodology explicitly develops systems thinking, and integration competencies, core to both AHEP4 and INCOSE frameworks.  

ACE-CORE is intentionally designed to offer a scaffolded learning experience, allowing students to build confidence step by step as they deepen their understanding. Due to its flexible structure, students can also follow a completely practical route, i.e. avoiding the modelling and simulation. The emphasis is not on rote memorisation of theory, but on progression through understanding the fundamentals of control engineering, e.g. the components that form a feedback control system. These routes enable learners to apply concepts in practical control engineering contexts and develop genuine competence. 

 

ACE-Apply: Real-world application:

ACE-Apply is the project stage, where the skills and knowledge gained from ACE-Box and ACE-CORE are consolidated by tackling authentic challenges aligned with the expectations of industry and professional engineers, see Figure 2(b). At this stage, learners prove their mastery by addressing engineering application problems that reflect the standards of industry practice. The focus is on: 

This stage reinforces AHEP4 Themes 3 and 5, particularly:  

It also strengthens INCOSE competencies in System Realisation, Integration, and Technical Project Management, encouraging students to act as systems integrators capable of managing interfaces and dependencies across mechanical, electrical, and software domains.   

By bridging theory, simulation, and hardware using industry-standard digital tools, ACE-Apply nurtures the ability to navigate complex adaptive systems, anticipate emergent behaviour, and work collaboratively within multidisciplinary engineering ecosystems.  

 

ACE-Box activities:

Upon visiting the ACE-Lab website (www.ace-lab.co.uk), under the tab ‘ACE-Box’, the following tabs exist (with the links provided):  

The “What is the ACE-Box?” page introduces educators and students to the ACE-Box platform, outlining its purpose, key features, and practical considerations such as sourcing components and 3D-printing enclosure parts.  

The “Prior Exercises” page provides essential onboarding material designed to help users become familiar with MATLAB and Simulink. This includes links to the relevant OnRamp courses, guidance on installing the required software packages, and short tutorial videos that introduce the MATLAB and Simulink graphical user interfaces (GUIs).  

The “Base + Sense” section contains a set of introductory tutorial exercises that use the ACE-Box (Base + Sense configuration). These activities help users get started with Simulink code generation for the Arduino Uno, while working with a range of basic sensors and electronic components.  

Finally, the “Base + Sense + Actuate” section builds on the previous material by introducing actuation hardware. Using both the Base + Sense and Actuate modules, students and educators learn how to interface with and control devices such as DC motors, servomotors, and stepper motors. This section is designed to familiarise users with actuator integration and reinforce practical control engineering workflows.  

 

Example use of ACE-Box (Base + Sense):

To demonstrate the use of the ACE-Box (Base + Sense), an introductory activity is provided, i.e. the on-off blinking of an LED. Prior to this activity, through ACE-CORE, students should receive a short introduction to microcontrollers covering key concepts such as digital input/output pins, analogue pins, and pulse-width modulation (PWM). Once students are familiar with these fundamentals, they progress to the initial exercise detailed here, which is aligned with defined learning outcomes. 

Since MATLAB and Simulink are the primary software tools used with the ACE-Box, students are first guided through installing the Simulink Support Package for Arduino Hardware. After the hardware and software setup is complete, they assemble a simple circuit, see Figure 7(a), and configure a Simulink model for the first exercise, see Figure 6(b). This initial activity requires students to control the state of a digital output pin on the Arduino, switching it on and off. The Simulink model, provided in Figure 7(b), enables students to quickly build the exercise using a visual programming approach. To run the activity, they follow a sequence of steps that includes code generation, which compiles the Simulink model into embedded C code and deploys it onto the Arduino Uno microcontroller. Once completed, the LED connected to the circuit blinks on and off according to the settings of the Simulink pulse generator. A visual of the complete set-up for this initial exercise can be found in Figure 8. At this stage, students are encouraged to experiment with the pulse generator parameters in real-time, observing how changes to the signal properties immediately affect the LED’s behaviour. Scopes can also be used (see Figure 7(b)) to visualise the pulse generator’s square-wave output, including its amplitude, period, and pulse width. This hands-on interaction reinforces the link between the initial set-up and hardware implementation while deepening their understanding of microcontrollers. 

Figure 7: LED Simple Circuit (a) and Simulink for Code Generation for the on-off Blinking of an LED.

Figure 8: LED Simple Circuit Set-Up using Simulink for Code Generation for on-off Blinking of an LED.

The initial exercise is designed to familiarise students with the ACE-Box and the use of Simulink’s code generation tools. This type of activity is typical for introducing students to a new software and hardware environment. The next exercise involves using pulse width modulation (PWM) to vary the brightness of the LED. This exercise involves using additional blocks in Simulink, see Figure 8, where multiple scopes are used to visualise the signals in real-time. Once students understand the fundamental building blocks of Simulink, they can quickly progress to developing feedback control systems that meet a variety of application requirements. In the authors’ view, student familiarity with Simulink makes it a more accessible platform for designing advanced control algorithms, particularly when working with sub-systems. 

Figure 9: LED Simple Circuit Set-Up using Simulink for Code Generation Varying Brightness of an LED using Pulse Width Modulation (PWM).

Building on this foundation, a wide range of laboratory exercises can be developed using the electronic components involved in ACE-Box (Base + Sense), as illustrated in Figure 3, with the option to expand further by incorporating additional components. Examples of extended exercises include: 

In addition to sensing activities, the ACE-Box (Actuate) provides four actuators: a servomotor, a DC motor with encoder, a stepper motor, and a DC motor fan. This unit can be used independently or in combination with the Base and Sense ACE-Box to enable more advanced control experiments, such as DC motor speed control or motor control based on light intensity measurements from an LDR. 

The flexibility of the ACE-Box system ensures that the number of possible exercises is effectively unlimited, as new experiments can be designed by combining existing sensors and actuators or by integrating additional measurement devices. This also allows unique coursework assignments to be created. 

 

Summary:

The ACE-Model provides a systemic and holistic framework for practical control engineering education that: 

 

Acknowledgements:  

Dr James E. Pickering gratefully acknowledges the support from MathWorks, whose funding made this project possible. He also extends his sincere thanks to Hari Sudeskkumar for his exceptional engineering design contributions and 3D-printing work. The authors would like to thank the Project Advisory Group (PAG) for their valuable guidance throughout the development of this work.  

 

References:

 

 

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.  

Toolkit: Complex Systems Toolkit.

Author: Professor Nici Zimmermann (University College London).

Topic: Illuminating complex interactions in a system through participatory modelling methods.  

Title: Using a participatory modelling approach to urban regeneration.

Resource type: Teaching activity.

Relevant disciplines: Any; civil engineering.

Keywords: Available soon.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

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 AHEP themes of Design and Practical and workshop skills. 

Educational level: Intermediate or advanced.

 

Learning and teaching notes: 

This activity is suitable for those having acquired some familiarity with complex systems and related concepts – especially causal loop diagrams, stock and flow diagrams, cognitive maps or participatory maps – who are looking for additional ideas for activities or who have a specific interest in participatory modelling. The activity is also useful at the point when students learn about interaction between different elements of a complex system or when they learn about the importance of human factors. 

Learners have the opportunity to:  

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

Overview:

This activity introduces students to a participatory systems thinking – or more specifically –  participatory modelling exercise. This is an approach used in group settings to explore complex issues and represent them via models. In this activity, students assume the roles of stakeholders involved in an urban regeneration project and take part in a group model-building workshop.  

It teaches students core principles of a participatory modelling method called group model-building or participatory system dynamics, but it can also be used to teach the underlying system structure of a specific phenomenon. This makes it well-suited for modules that contain elements of systems thinking and system dynamics (including causal loop diagramming and simulation modelling) or modules that contain an element on group facilitation and participatory methods.  

The activity is designed to run over 1.5 to 2.5 hours and is adaptable. While the current example focuses on the phenomenon of urban dynamics around the population development in a city, the activity can be reframed using a case and model from a project management, water management, energy or other sustainability-related context. 

The activity is directly aligned with systems thinking by immersing students in a participatory modelling process. It develops students’ awareness of system content and its interactions by teaching them qualitative modelling skills. It develops their skills in managing complexity and representing system elements with visual models consisting of items and their relationships depicted in causal loop diagrams and/or stock and flow diagrams. This serves to build students’ analysis skills and ability to apply systems approaches to problems. It also develops their practitioner, practical and workshop skills of collaborating with stakeholders. It does so by advancing their facilitation skills essential for collaborative systems work. This includes rules of conduct and techniques for managing a group discussion and group dynamics, making a broad range of ideas heard, prioritising them and mapping them visually. 

 

Materials or software required:

For in-person sessions, the following materials are recommended: 

If software or an online tool are used, these are used to collate concepts (variables suggested by students) and to build a diagram of their interactions. This will be projected to the in-person and/or online participants, replicating the participatory nature of in-person workshops. 

 

Detailed explanation of the activity:  

This activity introduces students to participatory modelling, an approach used in group settings to explore and understand complex issues and represent them via models. In this case, participatory modelling is introduced through a structured group model-building workshop, using a simplified version (Alfeld & Graham, 1976; Richardson, 2014) of Jay Forrester’s (1969) famous Urban Dynamics model, which sparked quite some debate because of its counter-intuitive insights. The session begins with a brief historical overview of urban growth and decline in major cities up to the 1980s (e.g. New York, Boston, London; see files under ‘Downloads’, above), before focusing on the London Docklands in London in 1981, as an example of an opportunity for urban redevelopment to counter the trend of population decline. Student groups are assigned stakeholder roles from that time, including the founder of the London Docklands Development Corporation (1–2 students), the Surrey Quays Housing Action Group (2–6 students), the London Chamber of Commerce (2–6 students) and the Greater London Council (2–6 students). Each student group receives a role sheet outlining their perspective and a small set of key variables relevant to their stakeholder position (see linked files). These variables are intentionally curated to align with the Urban Dynamics model and to ensure that collaboration is necessary to draw the interlinkages between the variables, i.e. the relevant system structure.  

A list of variables provided to the student groups via the role sheets is included below. Note that not all variables that are necessary to draw the model are included; students need to infer a few variables to train their thinking. 

Before the workshop begins, students are introduced to core concepts of participatory modelling (see linked slides), including facilitator roles (based on Richardson & Andersen, 1995) and common workshop scripts (e.g. hopes and fears, variable elicitation, voting, model building, policy option elicitation and system storytelling, as described in Scriptapedia and Andersen & Richardson, 1997). 

The workshop proceeds in three main phases: 

1. Variable elicitation: After a short introduction by the student playing the founder of the London Docklands Development Corporation into the setting and by the facilitator/teacher into the process, each group sketches behaviour-over-time graphs of their assigned variables (10 minutes). These are presented using a round-robin or nominal group technique, meaning one variable per group at a time only, and placed on a whiteboard/blackboard or digital canvas. The round-robin collection is a technique useful to foster inclusivity and avoid talking heads. Behaviour-over-time graphs rather than just variable names are useful because the final model is believed to explain a behavioural trend over time, linking model structure and dynamics. However, it is also possible to simplify by letting students just write the variable names on sheets of paper. 

2. Voting and prioritisation: To decide on a starting point for modelling, each student votes on which variables they consider to be the most important to include in the model, e.g. giving the students as many votes as there are variables on the board and freedom of how to distribute their votes. While all variables will be included in the model, the voting activity provides a basis for reflection on different priorities and students’ personal vs. their group’s perspective, after the modelling activity. 

3. Model building: The class collaboratively constructs a stock and flow diagram (see Figure 1). Stocks such as Housing Structures, Business Structures, and Population are identified, and their net rates of change are discussed. To connect the variables, some more variables need to be added such as the ratio of population to jobs, total land available, and land occupied. To help students identify these variables, the teacher can ask questions: “Population and housing are linked by a ratio. What concepts and respective variables could link the other stocks?”. Students are encouraged to identify feedback loops and classify them as reinforcing or balancing. 

It is useful to pay attention to uncovering less obvious relationships, such as the spatial competition between housing and business infrastructure. Once the diagram is complete, the facilitator reviews the model with the class, highlighting key feedback structures. 

After the modelling activity, students can be shown images of the Docklands after the redevelopment as well as urban population trends of London and other major cities until today, prompting discussion on the long-term impacts of different types of development and prioritisation of a business vs. housing focus. The session can conclude with a reflection on the participatory process and its relevance to real-world decision-making and the value of participatory modelling in complex policy environments. 

A more comprehensive version of this activity – including a more introductory group model-building exercise that teaches basic causal loop diagramming concepts and an alternative context using project management as an example – is available: https://discovery.ucl.ac.uk/id/eprint/10160261/   

 

Figure 1: Stock and flow diagram of urban dynamics (produced with Vensim software). This figure is intended for educators and serves an illustrative purpose. It provides educators with a reference for how the model built during the activity is expected to look. 

 

Note: The author was originally inspired to focus on this case by the historical accounts found on the London Docklands Development Corporation (LDDC) History Pages (http://www.lddc-history.org.uk/index.html, now inactive).  

 

References:

 

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.  

Toolkit: Complex Systems Toolkit.

Author: James C Atuonwu, PhD, MIET, FHEA (NMITE).

Topic: Simulating pinch analysis and multi-stakeholder trade-offs.

Title: Modelling complexity in industrial decarbonisation.

Resource type: Teaching activity.

Relevant disciplines: Energy engineering; Chemical engineering; Process systems engineering; Mechanical engineering; Industrial engineering.

Keywords: Climate change; Modelling; Decarbonisation; Energy production; Heat integration; Optimisation; Stakeholders; Trade offs.

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: 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

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, PyPinch PinCH, 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: 

 

Discussion prompts: 

 

Student deliverables: 

 

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:

 

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:

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. 

 

Instructor guidance:
Each student or small subgroup first constructs a causal loop diagram (CLD) from the viewpoint of their assigned stakeholder (e.g., operations, finance, environment). They then reconvene to integrate these perspectives into a single, shared system map, revealing conflicting goals, reinforcing and balancing feedback, and common leverage points. This two-step approach mirrors real-world decision dynamics and strengthens collective systems understanding. Support materials such as a CLD starter template and a stakeholder impact matrix may be provided to assist instructors in scaffolding systems-thinking activities.

 

Discussion prompts:

 

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: 

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:

Each group evaluates the resilience and flexibility of the proposed integration design. They consider:

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: 

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:

 

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: 

2. Systems mapping and scenario reasoning 

3. Decision memo (max 2 pages) 

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:

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: 

In real operations, the evaporation subprocess occurs 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: 

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: 

 

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% 

2) Systems Thinking & Scenario Analysis — 30% 

3) Stakeholder & Implementation Insight — 20% 

4) Decision Quality & Justification — 15% 

5) Communication & Presentation — 10% 

 

References:

 

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.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Scott Strachan (University of Strathclyde).

Topic:  Sociotechnical aspects of implementing renewable energy systems.

Title: Climate science and policy solutions workshops. 

Resource type: Teaching activity: Workshop.

Relevant disciplines: Any.

Keywords: Available soon.

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:  

Teachers have the opportunity to:  

 

Downloads: A PDF of this resource will be available soon.

Learning and teaching resources:

 

Activity overview:   

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. 

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 

2. Facilitators 

3. Materials and tools 

 

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)

Figure 2. Causal loop diagram showing how increasing GHG emissions drive climate action and emissions reduction. 

 

2. Initial actions brainstorm (5 mins)

Figure 3. Example of a Menti poll to capture learners’ understanding of climate solution impacts.  

 

3.Using EN-ROADS (15 mins)

 

4. Group simulation (15 mins)

 

5. Achieving 1.5 °C (10 mins)

 

6. Reflection and debrief (5 mins)

 

7. Post Workshop – optional

 

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.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Rhythima Shinde (KLH Sustainability).

Topic: Applying Cynefin framework for climate resilience.  

Title: Managing floods in urban infrastructure.

Resource type: Teaching – Case study.

Relevant disciplines: Civil engineering; Environmental engineering; General engineering.

Keywords: Systems thinking; Climate change; Sustainability; Risk; Decision-making; Problem-solving; Disaster mitigation.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

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: 

 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

Time required: 

The teaching activity is designed for 4–6 hours of structured learning, delivered across three modules: 

1. Context (1–2 hours) 

2. Analysis and insights (1–2 hours) 

3. Discussion and transferable learning (1–2 hours) 

 

Materials required:

1. Open access online learning platform: Engineering for a complex world

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.

2. Case study pack: Queensland Reconstruction Authority flood response

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.

3. Facilitator’s guide: (Appendix A)

This guide equips educators to deliver the course consistently and effectively. It includes:

4. Timeline touchpoints: (Appendix B)

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:

The form provides both quantitative data (Likert scales) and qualitative insights (open-ended reflections), enabling robust evaluation of teaching impact. 

 

Assessment:

 

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: 

2. Analysis & insights module: 

3. Discussion & transfer learning module: 

 

Interactive learning design:

The teaching activity integrates multiple interactive elements to immerse students in systems thinking: 

 

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 

 

Guided questions and activities: 

Facilitators can use these prompts to stimulate inquiry and structured reflection: 

 

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: 

This flexibility allows educators to tailor the activity to their students’ level of expertise, institutional context, and disciplinary focus. 

 

References:

 

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.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Stuart Grey, SFHEA (University of Glasgow).

Topic: Student created interactive simulation for complex sociotechnical systems.

Title: LLM-driven interactive simulation for complex sociotechnical systems.

Resource type: Teaching activity.

Relevant disciplines: Any.

Keywords: Artificial Intelligence; Large Language Model; Sociotechnical systems; Ethics; Modelling or simulation; Emergence.

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. 

Downloads: 

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. 

Education level: Intermediate.

 

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

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. 

 

Why and how: 

The approach aims to give students hands-on experience in putting systems thinking into practice. Concepts such as stakeholders, feedback loops, delays, uncertainty, and emergent behaviour can be implemented and interrogated without heavy tooling.  

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:

 

Delivery modes:

Mode A — Studio activity (3–5 sessions) 

Mode B — Assessment‑only (prompt‑only; 1–2 sessions) 

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. 

 

Assessment:

Studio portfolio — rubric (suggested weighting):

Criterion  A  B  C  D–E  % 
Complexity modelling  Clear boundary; rich stakeholders; ≥4 correct loops; delays explicit; coherent KPIs  Mostly sound  Basic map  Superficial  25 
Simulation design and prompt quality  Consistent state logic; visible feedbacks/delays; non‑linearity; negative choices allowed with consequences; clear commands  Mostly coherent  Playable but brittle  Confusing/linear  25 
Debugging evidence  Systematic play‑tests; clear issue → fix → re‑test artefacts  Some iteration  Minimal  None  20 
Insight and reflection  Deep analysis of emergence, trade‑offs, equity, uncertainty, and LLM limits  Good  Descriptive  Vague  20 
Communication and referencing  Clear, concise, correct Harvard referencing  Minor issues  Adequate  Disorganised  10 

 

Assessment‑only (prompt‑only) — compact rubric: 

 

Scenario options: 

Students may propose their own topic or the module leader may supply a predefined scenario. Options suited to UK engineering contexts include: 

 

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: 

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 

Robustness 

User experience and clarity 

Report 

 

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.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Ewa Ura-Binczyk (Warsaw University of Technology).

Topic: Rail accident investigation and material failure analysis using systems thinking.

Title: Using fault tree analysis in a rail failure investigation.

Resource type: Teaching – Case study.

Relevant disciplines: Mineral, metallurgy & materials engineering; Civil engineering.

Keywords: Public health and safety; Risk; Fault tree analysis; Failure; Ethics; Public trust; Stakeholders; Trade offs; Uncertainty.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 

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, Ethics and Professionalism, Technical Leadership 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 AHEP themes of Design, Ethics and Communication. 

Educational level: Intermediate; Advanced.

 

Learning and teaching notes:

The case is built around 3 × 90-minute sessions and independent report writing. A suggested breakdown of the activities can be seen below. 

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

Session  Focus  Suggested activities and timing 
1  Introduction and problem framing  20 min: Introduce case scenario and system context; 30 min: Group discussion on initial impressions, key stakeholders, and potential causes; 40 min: Begin Fault Tree Analysis (FTA) construction using initial evidence. 
2  Investigation and analysis  30 min: Continue FTA construction and data evaluation; 30 min: Peer review of other groups’ fault trees; 30 min: Consolidate findings and prepare draft report outline. 
3  Reporting and reflection  30 min: Present findings to a simulated stakeholder panel; 30 min: Discuss feedback and defend conclusions; 30 min: Individual reflection on complexity, uncertainty, and assumptions. 

 

Summary of the system or context:

Rail transport systems consist of thousands of interdependent components, including rails, fasteners, sleepers, signalling systems, and maintenance processes. Failures in a single component can cascade, affecting: 

 

Complex system features: 

 

Narrative of the case:

On a cold January morning, a commuter train was halted after inspectors discovered a fractured rail joint component. Services were disrupted for several hours, stranding thousands of passengers. The media quickly picked up the story, raising questions about safety and reliability. 

The rail operator urgently commissioned an engineering consultancy (the students) to investigate the failure. Their findings will inform both the safety authority’s decision on whether the line can reopen and the legal proceedings to determine liability. 

 

The dilemma: 

As consultants, students face incomplete evidence: some lab tests are missing, inspection logs are inconsistent, and eyewitness accounts conflict. They must use Fault Tree Analysis (FTA) to map possible causes, evaluate data, and produce an expert opinion report — knowing that their conclusions could influence legal outcomes and public safety decisions. 

Groups: 3–5 students per group; 3-4 groups can run in parallel. 

Materials required: case narrative handouts, sample inspection log, example FTA, whiteboards/flipcharts, sticky notes for FTA mapping. 

Activity flow: 

1. Introduce case and assign roles. 

2. Construct initial fault trees using evidence. 

3. Peer-review across groups. 

4. Draft expert report and present to simulated stakeholder panel. 

5. Individual reflection on complexity and uncertainty. 

 

Why use Fault Tree Analysis (FTA):

FTA is a structured approach to trace a failure from an observed event back to potential causes, including technical, human, and organisational factors. 

FTA is particularly suitable for this case because it allows students to structure complex, uncertain information in a logical and transparent way. It helps them trace the chain of causes behind the rail component failure, linking material, human, and organisational factors into one coherent framework. By visualising how small events combine into system-level failures, FTA encourages learners to think critically about interdependencies, data gaps, and assumptions. It also mirrors real-world engineering investigations, where professionals must justify conclusions under uncertainty and demonstrate clear reasoning to stakeholders such as regulators or courts. 

Advantages in this case: 

 

Questions and activities: 

Prompt  Expected insight / reflection 
What technical, human, and organisational factors might have contributed to this failure?  Students identify multiple interacting factors, illustrating interdependencies and emergent risks. 
How does Fault Tree Analysis help structure uncertainty in this investigation?  Learners recognise FTA’s role in visualising cause-effect pathways and clarifying assumptions. 
Which assumptions are you forced to make, and how might they affect your conclusions?  Students reflect on data gaps, biased observations, and ethical implications of assumptions. 
How do different stakeholders’ interests shape urgency and framing of your analysis?  Learners understand trade-offs, pressures from conflicting priorities, and the precautionary principle. 
What are the risks of issuing a preliminary report under time pressure?  Students explore implications for safety, liability, professional integrity, and public trust. 

 

Activity  Focus  What “good practice” looks like  Facilitator notes / tips 
1. FTA construction  Collaborative problem analysis  Teams discuss evidence openly, question assumptions, and co-create a logical tree linking technical, human, and organisational causes.   Encourage each group to identify at least one “human/organisational” branch and to label any data gaps explicitly. 
2. Peer review  Critical reflection and systems perspective  Groups provide constructive critique, highlighting hidden assumptions, missing branches, or unclear logic. Dialogue stays professional and evidence-based.  Provide coloured sticky notes or digital comments to record feedback; model how to frame critique as questions (“Have you considered…?”). 
3. Report writing (in-class drafting)  Synthesis and professional communication  Drafts show a clear, defensible reasoning chain from evidence to conclusion. Teams justify assumptions and note uncertainties.  Remind students to separate “facts” from “interpretations.” Encourage use of structured headings (Findings – Analysis – Conclusions). 
4. Simulation role-Play  Perspective-taking and communication under pressure  Presentations are concise (≤5 min), factual, and adapted to stakeholder roles. Learners respond respectfully and clearly to challenging questions.  Provide role cards for the panel (operator, regulator, manufacturer, public). Rotate students if possible. 
5. Reflection  Metacognition and learning from uncertainty  Students identify what surprised them, what they found ambiguous, and how their view of engineering judgment evolved.  Offer prompts like “What would you do differently next time?” or “Where did your reasoning feel uncertain?” 

 

Further challenge:

Instructors may choose to introduce a second “reveal” phase: a new metallurgical test result or a whistle-blower statement emerges halfway through the case. Students must revise their fault tree and defend whether and how their conclusions change. This highlights the evolving nature of complex systems investigations. 

 

Assessment opportunities:

 

 

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.  

 

Authors: Siara Isaac; Valentina Rossi; Joelyn de Lima.

Topic: Transversal skills that promote sustainability.

Tool type: Teaching (Experiential learning activity guide).

Relevant disciplines: Any.

Keywords: Negotiation Skills; Perspective taking; Role-play.

Sustainability competency: Systems thinking; Critical thinking.

Related SDGs: SDG 4 (Quality education); SDG 7 (Affordable and clean energy); SDG 9 (Industry innovation and infrastructure); SDG 12 (Responsible consumption and production); SDG 13 (Climate action).

Reimagined Degree Map Intervention: Active Pedagogies and Mindset Development; More Real-World Complexity; Cross-Disciplinarity.

Who is this article for?: This article should be read by educators at all levels of higher education looking to embed and integrate ESD into curriculum, module, and / or programme design.

Link to resource: How to support students to develop skills that promote sustainability

 

Learning and teaching notes:

This experiential activity aims to incorporate sustainability reflections into students’ group work. It uses a selection of materials with different properties to engage participants in building a wind turbine prototype based on a contextualised negotiation of multiple facets of sustainability.

Taking a disciplinary standpoint, participants first assume one of four engineering roles to identify specific sustainability priorities based on their role’s responsibilities and expertise. Next, they represent the perspective of their assigned role in an interdisciplinary group to optimise sustainability in the design of a wind turbine.

Throughout the activity, students are given targeted and short theoretical input on a selection of transversal skills that facilitate the integration of sustainability in group work: systems thinking, negotiation skills and perspective taking.

This activity guide provides the outline and material to assist the facilitator to prepare, and the slides and handouts for teaching the activity in approximately 75min. It can be facilitated with tangible objects (e.g. LEGO) as well as online. We invite you to adapt this activity to your context and tangibles availability.

 

Click here to access the activity guide

 

Supporting resources on the development of transversal skills:

https://zenodo.org/communities/3tplay/records

 

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.  

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. 

What are the top ethical issues in engineering today, and how can you incorporate these in your teaching?

In our Engineering Ethics workshop at the 2023 SEFI Conference at TU Dublin, we asked participants what they felt were the top ethical issues in engineering today. This word cloud captured their responses, and the results reveal concerns ranging from AI and sustainability to business and policy and beyond.

When incorporating ethics into a lesson or module, educators might want to find teaching resources that address a topic that’s recently been in the news or something of particular relevance to a group of students or to a project brief. But how can this be done efficiently when there are now so many teaching materials available in our Toolkits?

Fortunately, sifting through available resources in the Ethics Toolkit is now easier than ever, with the release of the new Toolkit search function. The Toolkit search allows users to:

  • Choose from a list of suggested keyword tags;
  • Search by multiple keyword tags or their own search terms;
  • Refine the search results by one of more of the following filters: engineering discipline; educational level; type of content.

It even pulls resources from across different toolkits, if so desired.

Not only will this help you discover and find materials that are right for your educational context, but the search function could even become a teaching tool in itself. For instance, you could poll students with the same question we used in the SEFI Workshop, asking them what they think the top ethical issues are in engineering today, and then design (or co-design) a lesson or activity based on their responses and supported by resources in the Toolkit. If you don’t find resources for a particular issue, that could be a great learning opportunity to0 – why might these topics not be addressed? Of course, you can always create a resource that fills a gap and submit it to be a part of the Toolkit: we would love to see a student-developed case study or activity.

Let us know how you have used the Toolkit search function, and if there are ways we could improve it. Happy searching!

This post is also available here.

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.

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