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, Configuration Management, Requirements Definition, Communication, Verification, and Validation 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: Advanced.
Learning and teaching notes:
Overview:
This multi-part case study guides students through the complex systems challenges of Prince Edward Island, Canada’s ambitious 100% renewable energy transition by 2030. Students will experience how technical, social, and economic factors interact through emergence, feedback loops, and multi-scale dynamics that traditional engineering analysis alone cannot capture.
Learners have the opportunity to:
Identify complex systems characteristics (emergence, feedback loops, nonlinearity) in real energy systems.
Apply multiple modelling approaches (ABM, system dynamics, network analysis) to analyse system behaviour.
Evaluate how technical decisions create emergent social and economic consequences.
Synthesise insights from different modelling approaches to inform policy recommendations.
Communicate complex systems concepts and uncertainties to non-technical stakeholders.
Teachers have the opportunity to:
Demonstrate complex systems concepts through hands-on modelling.
Facilitate discussions on emergence and system-level behaviours.
Evaluate learners’ ability to apply systems thinking to engineering problems.
Connect technical modelling to real-world policy and social implications.
Overview: Energy transition as a complex systems challenge:
Prince Edward Island (PEI), Canada’s smallest province, aims to achieve 100% renewable electricity by 2030. Its small grid, dependence on imported power, and growing renewable infrastructure make it a natural laboratory for systems thinking in energy transitions.
This case invites students to explore how technical, social, and policy decisions interact to shape renewable integration outcomes. Using complexity-science tools, they will uncover how local actions produce emergent system behaviour, and why traditional linear models often fail to predict real-world dynamics.
The complex challenge: Traditional engineering approaches often treat energy systems as predictable and linear, optimising components like generation, transmission, or storage in isolation. However, energy transitions are complex socio-technical systems, characterised by feedback loops, interdependencies, and emergent behaviours.
In PEI’s case, replacing stable baseload imports with variable wind and solar generation creates cascading effects on grid stability, pricing, storage demand, and social acceptance. The island’s bounded geography magnifies these interactions, making it an ideal context to observe emergence and system-level behaviour arising from local interactions.
PEI currently imports about 75% of its electricity via two 180 MW submarine cables, while 25% is produced locally through onshore wind farms (204 MW). Plans for offshore wind, community solar, and hydrogen projects have triggered debates around stability, affordability, and social acceptance.
Taking on the role of an engineer at TechnoGrid Consulting, students are tasked to advise Maritime Electric, the island’s utility, on modelling strategies to guide $2.5 billion in renewable investments.
Competing goals:
Maintain grid reliability while replacing fossil baseloads.
Achieve policy targets without increasing public resistance.
Balance economic cost, environmental benefit, and technological feasibility.
Discussion prompt:
In systems terms, where do you see tensions between policy, technology, and society? How might feedback loops amplify or mitigate these tensions?
While Maritime Electric’s engineering team insists the project scope should stay strictly technical, limited to grid hardware, generation, and storage, policy advisors argue that social behaviour, market pricing, and community engagement are part of the system’s real dynamics.
Expanding boundaries makes the model richer but harder to manage; narrowing them simplifies computation but risks missing the very factors that determine success.
Temporal boundaries: timescales from milliseconds (grid response) to decades (infrastructure).
Organisational boundaries: stakeholders, regulations, and markets.
Discuss how including or excluding elements (e.g., electric-vehicle uptake, community cooperatives, carbon policy) changes the model’s focus and meaning.
Learning insight:
Complex systems cannot be fully understood in isolation; boundaries are analytical choices that shape both perception and leverage. Every inclusion or exclusion reflects an assumption about what matters and that assumption determines which complexities emerge, and which stay hidden.
Part three: Modelling the system: Multiple lenses of complexity:
(a) Agent-Based Modelling (ABM) with NetLogo:
Students construct simplified models of households, businesses, and grid operators:
Household agents: decide to adopt rooftop solar based on payback time and neighbour influence.
Technology providers: adjust prices in response to market demand.
Grid operator: balances reliability and cost.
Emergent patterns such as adoption S-curves or network clustering illustrate how simple local rules generate complex collective dynamics.
(b) System Dynamics (SD) with Vensim:
Students then develop causal loop diagrams capturing key feedbacks:
Adoption–Learning Loop: installations ↓ costs ↓ encourage more adoption.
Cost–Acceptance Loop: higher bills ↓ public support ↓ investment capacity.
This provides a macroscopic view of feedback, delay, and leverage points.
(c) Network Analysis with Python (NetworkX):
Students model actor interdependencies: how households, utilities, industries, and regulators interact. Network metrics (centrality, clustering, connectivity) reveal where resilience or vulnerability is concentrated.
Reflection prompt:
Which modelling approach offered the most insight into system-level behaviour? What were the trade-offs in complexity and interpretability?
Part four: Scenario exploration: Pathways to 2030:
Students explore three transition scenarios, each with distinct emergent behaviours:
A. Distributed Solar + Community Storage
300 MW solar, 150 MWh batteries
Decentralised coordination challenges and social clustering effects.
B. Offshore Wind + Grid Enhancement
400 MW offshore wind, new 300 MW interconnection
Weather-dependent reliability and cross-border dependency.
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, 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:
Deepen their understanding of systems thinking.
Engage with participatory modelling.
Enjoy a fun group activity.
Teachers have the opportunity to:
Introduce students to participatory modelling (i.e. more specifically to participatory system dynamics / group model-building).
Develop students’ understanding of stock-and-flow modelling in the tradition of system dynamics.
Support students’ understanding of complex interactions around urban dynamics, and if adapted also around other contexts.
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.
Miro (free version available with up to three active boards)
MURAL or similar platforms (depending on institutional access)
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.
London Docklands Development Corporation
Population (This is the total number of people living in the city. As the variable represents the sum of all people, it is an integral. Specific pictogram language can highlight this. In the tradition of system dynamics modelling, one would therefore put a rectangle around it, which the teacher can do after the variable has been placed on the board.)
Surrey Quays Housing Action Group
Housing Structures (Indicates the number of dwellings. Housing is important for urban development. As the variable represents the sum of all dwellings, it is also an integral, which would be depicted by a rectangle.)
Ratio of households to housing (This variable is an indicator of crowding. Ideally, there is one household per housing structure. A number above one means that some people need to live in shared places. A number below one means that there is vacant housing.)
London Chamber of Commerce
Business structures (Indicates the number of business units. Businesses are important to urban development. As the variable represents the sum of all business units, it is also an integral, which would be depicted by a rectangle.)
Jobs (Total number of jobs available in the city (whether filled or unfilled).
Greater London Council
Population (Repeated variable to be able to compare between groups.)
Net migration (Change in population. The simple model excludes population changes by births and deaths because of the relative larger changes by national and international migration.)
Jobs (Repeated variable to be able to compare between groups.)
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 inspiredto 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, nowinactive).
References:
Alfeld LE, & Graham AK. 1976. Introduction to Urban Dynamics. Cambridge, MA: Productivity Press.
Andersen DF, & Richardson GP. 1997. Scripts for group model building. System Dynamics Review 13(2): 107–129.
Antunes P, Stave K, Videira N, & Santos R. 2015. Using participatory system dynamics in environmental and sustainability dialogues. In M. Ruth (Ed.), Handbook of Research methods and Applications in Environmental Studies. Edward Elgar Publishing: Cheltenham, UK: 346–374.
Deaton M, & MacDonald R. 2025. System Dynamics Learning Guide. Harrisonburg, VA: James Madison University Libraries.
Zimmermann, N. 2026 (forthcoming). Weaving participatory modelling into teaching: Purposes and practices from system dynamics education. Systems Dynamics Review.
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.
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.
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, 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 addressesAHEP 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:
Explore how technical, human, and organisational factors interact in complex socio-technical systems.
Apply Fault Tree Analysis (FTA) to diagnose ambiguous real-world engineering failures.
Practice making judgements under uncertainty with incomplete and conflicting data.
Analyse competing stakeholder perspectives and the ethical trade-offs in engineering decision-making.
Develop professional communication skills by producing expert reports and presenting findings to a stakeholder panel.
Reflect on their own reasoning, assumptions, and handling of complexity.
Teachers have the opportunity to:
Use an authentic, narrative-driven case to introduce systems thinking and failure analysis.
Facilitate active learning through group FTA construction and peer review.
Engage students in interdisciplinary learning that links materials science, engineering practice, regulation, and ethics.
Adapt the complexity of the case (technical vs organisational) depending on learners’ level and course focus.
Provide formative and summative assessment using expert reports, presentations, and reflective writing.
Encourage metacognitive development by prompting students to examine uncertainty and assumptions in engineering practice.
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:
Safety: Malfunctions may cause derailments or delays.
Economics: Service interruptions lead to financial losses and reputational damage.
Public trust: Media scrutiny increases scrutiny of operational practices.
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:
The operator demands a rapid report to resume services.
The manufacturer insists the component was produced to specification and blames poor maintenance.
The regulator requires an unbiased, defensible technical opinion before approving operations.
The public expects transparency and reassurance about safety.
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.
Visualises cause-effect relationships, interdependencies, and failure paths.
Encourages discussion of assumptions and uncertainties.
Questions and activities:
Discussion prompts:
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.
Classroomactivities:
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:
Fault Tree Diagram (30%) – accuracy, depth, clarity.
Presentation and defence (20%) – clarity, stakeholder awareness, handling questions.
Reflective summary (20%) – insight into uncertainty, assumptions, systems thinking.
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.
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.
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.
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!
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.
Author: Mike Murray BSc (Hons) MSc PhD AMICE SFHEA (Senior Teaching Fellow in Construction Management, Department of Civil & Environmental Engineering, University of Strathclyde).
Topic: Links between education for sustainable development (ESD) and intercultural competence.
Tool type: Teaching.
Engineering disciplines: Civil; Any.
Keywords: AHEP;Sustainability; Student support; Local community; Higher education; Assessment; Pedagogy; Education for sustainable development; Internationalisation; Global reach; Global responsibility; EDI.
Sustainability competency: Self-awareness; Collaboration; Critical thinking.UNESCO has developed eight key competencies for sustainability that are aimed at learners of all ages worldwide. Many versions of these exist, as are linked here*. In the UK, these have been adapted within higher education by AdvanceHE and the QAA with appropriate learning outcomes. The full list of competencies and learning outcome alignment can be found in the Education for Sustainable Development Guidance*. *Click the pink ''Sustainability competency'' text to learn more.
AHEP mapping: This resource addresses two of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): The Engineer and Society (acknowledging that engineering activity can have a significant societal impact) and Engineering Practice (the practical application of engineering concepts, tools and professional skills). To map this resource to AHEP outcomes specific to a programme under these themes, access AHEP 4 here and navigate to pages 30-31 and 35-37.
Related SDGs: SDG 4 (Quality education); SDG 16 (Peace, justice, and strong institutions).
Reimagined Degree Map Intervention: More real-world complexity; Active pedagogies and mindset development; Authentic assessment.The Reimagined Degree Map is a guide to help engineering departments navigate the decisions that are urgently required to ensure degrees prepare students for 21st century challenges. Click the pink ''Reimagined Degree Map Intervention'' text to learn more.
Educational level: Beginner.
Learning and teaching notes:
This resource describes a coursework aligned to three key pedagogical approaches of ESD. (1) It positions the students as autonomous learners (learner-centred); (2) who are engaged in action and reflect on their experiences (action-oriented); and (3) empowers and challenges learners to alter their worldviews (transformative learning). Specifically, it requires students to engage in collaborative peer learning (Einfalt, Alford, and Theobald 2022; UNESCO 2021). The coursework is an innovative Assessment for Learning” (AfL) (Sambell, McDowell, and Montgomery, 2013) internationalisation at home (Universities UK, 2021) group and individual assessment for first-year civil & environmental engineers enrolled on two programmes (BEng (Hons) / MEng Civil Engineering & BEng (Hons) / MEng Civil & Environmental Engineering). However, the coursework could easily be adapted to any other engineering discipline by shifting the theme of the example subjects. With a modification on the subjects, there is potential to consider engineering components / artifacts / structures, such as naval vessels / aeroplanes / cars, and a wide number of products and components that have particular significance to a country (i.e., Swiss Army Knife).
Learners have the opportunity to:
Engage in collaborative peer learning and socialise with students from different countries.
Gain knowledge related to the design and construction of civil engineering buildings and structures.
Develop a ‘global engineering mindset.’
Teachers have the opportunity to:
Promote, recognise, and reward intercultural engagement and the development of intercultural competence (IC).
Raise student awareness of an engineer’s role in the UNSDGs.
There have been several calls to educate the global engineer through imbedding people and planet issues in the engineering curriculum (Bourn and Neal, 2008; Grandin and Hirleman 2009). Students should be accepting of this practice given that prospective freshers are ‘positively attracted by the possibility of learning alongside people from the rest of the world’ (Higher Education Policy Unit, 2015:4). Correspondingly, ‘international students often report that an important reason in their decision to study abroad is a desire to learn about the host country and to meet people from other cultures’ (Scudamore, 2013:14). Michel (2010:358) defines this ‘cultural mobility’ as ‘sharing views (or life) with people from other cultures, for better understanding that the world is not based on a unique, linear thought’.
Civil Engineering is an expansive industry with projects across many subdisciplines (i.e. Bridges, Buildings, Coastal & Marine, Environmental, Geotechnical, Highways, Power including Renewables. In a group students are required to consult with an international mentor and investigate civil engineering (buildings & structures) in the mentor’s home country. Each student should select a different example. These can be historical projects, current projects or projects planned for the future, particularly those projects that are addressing the climate emergency. Students will then complete two tasks:
Task 1: Group International Poster (10% weighting)
a. Reasoning for coursework with reference to transnational engineering employers and examples of international engineering projects and work across national boundaries.
b. Links between engineering, people, and planet through the example of biomimicry in civil engineering design (Hayes, Desha, & Baumeister, 2020) or nature-based solutions in the context of civil engineering technology (Cassina and Matthews ,2021).
c. Existence of non-governmental organisations (NGOs) such as RedR UK (2023) Water Aid (2023) and Bridges to Prosperity (2023).
d. The use of corporate social responsibility (CSR) to address problematic issues such as human rights abuses (Human Rights Watch, 2006) and bribery and corruption (Stansbury and Stansbury) in global engineering projects.
2. Assign students to groups:
a. Identify international mentors. After checking the module registration list, identify international students and invite them to become a mentor to their peers. Seek not to be coercive and explain that it is a voluntary role and to say no will have no impact on their studies. In our experience, less than a handful have turned down this opportunity. The peer international students are then used as foundation members to build each group of four first-year students. Additional international student mentors can be sourced from outside the module to assist each group.
3. Allow for group work time throughout the module to complete the tasks (full description can be found in the complete brief).
Assessment criteria:
The coursework constitutes a 20% weighting of a 10-Credit elective module- Engineering & Society. The submission has two assessed components: Task 1) a group international poster with annotated sketches of buildings & structures (10% weighting); and Task 2) A short individual reflective writing report (10% weighting) that seeks to ascertain the students experience of engaging in a collaborative peer activity (process), and their views on their poster (product). Vogel et al, (2023, 45) note that the use of posters is ‘well-suited to demonstrating a range of sustainability learning outcomes’. Whilst introducing reflective writing in a first-year engineering course has its challenges, it is recognised that reflective practice is an appropriate task for ESD- ‘The teaching approaches most associated with developing transformative sustainability values stimulate critical reflection and self-reflection’ (Vogel et al, 2023, 6).
The coursework has been undertaken by nine cohorts of first-year undergraduate civil engineers (N=738) over seven academic sessions between 2015-2024. To date this has involved (N=147) mentors, representing sixty nationalities. Between 2015-2024 the international mentors have been first-year peers (N=67); senior year undergraduate & post-graduate students undertaking studies in the department (N=58) and visiting ERASMUS & International students (N =22) enrolled on programmes within the department.
Whilst the aim for the original coursework aligns with ESD (‘ESD is also an education in values, aiming to transform students’ worldviews, and build their capacity to alter wider society’ -Vogel et al ,2023:21) the reflective reports indicate that the students’ IC gain was at a perfunctory level. Whilst there were references to ‘a sense of belonging, ‘pride in representing my country’, ‘developing friendships’, ‘international mentors’ enthusiasm’ this narrative indicates a more generic learning gain that is known to help students acquire dispositions to stay and to succeed at university (Harding and Thompson, 2011). The coursework brief fell short of addressing the call ‘to transform engineering education curricula and learning approaches to meet the challenges of the SDGs’ (UNESCO,2021:125). Indeed, as a provocateur pedagogy, ‘ESD recognises that education in its current form is unsustainable and requires radical change’ (Vogel et al ,2023, 4).
Given the above it is clear that the coursework requirement for peer collaboration and reflective practice aligns to three of the eight key competencies (collaboration, self-awareness, critical thinking) for sustainability (UNESCO, 2017:10). Scudamore (2013:26) notes the importance of these competencies when she refers to engaging home and international students in dialogue- ‘the inevitable misunderstandings, which demand patience and tolerance to overcome, form an essential part of the learning process for all involved’. Moreover, Beagon et al (2023) have acknowledged the importance of interpersonal competencies to prepare engineering graduates for the challenges of the SDG’s. Thus, the revised coursework brief prompts students to journey ‘through the mirror’ and to reflect on how gaining IC can assist their knowledge of, and actions towards the SDG’s.
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.
Authors: Siara Isaac; Valentina Rossi; Joelyn de Lima.
