Toolkit: Complex Systems Toolkit.

Author: Nafiseh M. Aftah, PhD Candidate (University of Kansas).

Topic: Why integrate complex systems in engineering education? 

Title: Complex systems in a transformational era.

Resource type: Knowledge article.

Relevant disciplines: Any.

Keywords: Available soon.

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

Downloads: Available soon.

Who is this article for?: This article should be read by educators at all levels in higher education who are seeking an overall perspective on teaching approaches for integrating complex systems in engineering education. 

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded. 

This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies. 

AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).  

 

Premise:

Engineering education is undergoing a fundamental transformation. The convergence of technological, social, and environmental challenges demands that future engineers move beyond procedural problem-solving toward complex thinking – a mindset capable of navigating uncertainty, interdependence, and dynamic change. This shift has been accelerated by advances in Artificial Intelligence (AI), which have redefined both the nature of engineering practice and the competencies students must develop to thrive in it. 

For scientists and engineers, understanding complex systems is critical for the ability to apply knowledge and techniques across diverse contexts. This is particularly visible in fields such as bioengineering, which depends on advances in chemistry, physics, computing, and other engineering disciplines. Such integration requires designing subsystems where engineering expertise can be meaningfully applied. Complex systems also involve human interaction, introducing unpredictability, feedback loops, and uncertainty. Modern AI-enabled systems—ranging from autonomous vehicles to smart grids and biomedical devices—cannot be fully understood through a single traditional discipline. These systems are not simply complicated; they are interconnected, dynamic, and often nonlinear (Jakobsson, 2025). 

 

What this means for engineering education and educators:

Across the globe, educators have turned to Problem-Based Learning (PBL) as a central strategy for cultivating systems-oriented thinking. For instance, Tauro et al. (2017) and the case study conducted at Tishk International University demonstrate that integrating PBL within mechatronics education enhances students’ ability to connect theory with practice, encouraging collaboration and creativity in addressing multifaceted engineering problems. Similarly, Watters et al. (2016) show that industry–school partnerships transform classrooms into real-world laboratories, reinforcing the value of experiential learning and knowledge transfer between academia and professional practice. These initiatives reflect a broader movement toward authentic, interdisciplinary engagement, a necessary foundation for understanding and designing complex systems. 

However, adopting PBL and interdisciplinary methods is not only a pedagogical improvement but also an epistemological necessity. As Stegeager et al. (2024) emphasise, educators themselves must evolve from instructors to facilitators, cultivating reflective and adaptive learning environments that mirror the complexity of professional engineering contexts. Mynderse et al. further highlight that when students are given responsibility for solving open-ended problems, they report higher satisfaction and deeper conceptual integration. These outcomes suggest that active learning approaches foster the kind of complex, interconnected reasoning required for contemporary engineering practice. 

In parallel, the AI-driven classroom is transforming the educational landscape. Emerging evidence shows that generative AI tools support personalised learning and immediate feedback, freeing educators to focus on mentorship and creativity (Jaramillo, 2024). Yet this technological advancement also underscores the limits of automation. Machines can model and predict, but they cannot interpret ethical implications, reconcile trade-offs, or integrate human and ecological perspectives. This is where complex thinking becomes indispensable: it enables learners to understand AI not merely as a computational tool but as a component within broader sociotechnical systems. 

The need for complex systems understanding is especially acute in fields such as bioengineering and mechatronics, where technologies intersect with living systems and social contexts. The defining feature of complex systems is the interaction among multiple components that produce emergent, often unpredictable behaviour. For engineering students, grasping these principles means developing the ability to think beyond linear causality and to engage with feedback loops, uncertainty, and adaptive design. 

 

The imperative to transform engineering education:

In traditional engineering education, students get topics presented in discrete classes. They get trained in thermodynamics and fluid mechanics and they often forget what they have learned by the time they are at the control systems course where there is an opportunity to bring together skills from prior knowledge. This modularised model is already losing its effectiveness in preparing the students for encountering real-world problems. As the adage says, “In theory, theory and practice are the same; in practice, they are not”. Understanding the role of noise, measurement errors, simplifying assumptions and computational errors play an essential role. To this end, it is crucial to centre complex system design and embrace interdisciplinarity to develop a competency that supports life-long, adaptive learning.  

As an example, Aalborg University in Denmark stands as a global exemplary of systems-oriented engineering education. Its PBL model is not an add-on; it is the spine of the entire curriculum. Every semester, students tackle a new problem – often tied to societal needs such as urban planning, environmental sustainability, or healthcare. Students must identify relevant knowledge areas, work collaboratively across disciplines, and reflect on both process and outcome. Faculty report that this structure promotes holistic thinking, resilience, and a sense of professional identity early on the students’ journeys (Kolmos et al. 2008). 

On the undergraduate level, capstones are a common part of engineering education which happens at the late stages of the student’s studies. At Rowan University (New Jersey, USA), Engineering Clinics provide a different but equally powerful model. Students work across all four years on interdisciplinary teams, contributing to faculty research or industry-sponsored projects. These clinics are embedded in the curriculum and require students to engage deeply with current research problems, often involving complex technical and human systems. A junior clinic project, for example, might involve the optimisation of a renewable energy system integrating mechanical, electrical, and computer engineering principles. Therefore, students learn to navigate ambiguity, collaborate with experts, and see the relevance of their disciplinary knowledge in a broader context by confronting the messy nature of real data. 

These are two of many examples where systems thinking is cultivated. Students gain exposure to open-ended problems and practice seeking connection across domains as they encounter the limits of their knowledge. In this fast-moving era, crossing disciplines empowers students for lifelong adaptation, allowing them to incorporate their experiences into any new technological developments. It also encourages treating learning as a collaborative social process, rather than a solo race to secure the first job. 

Educators must do more than just deliver content; they also need to act as facilitators and learn alongside their students. By redesigning the curriculum around design-oriented problems that mirror real-world changes, higher education will better prepare future engineers to face upcoming systemic global challenges.  

 

Looking ahead:

As artificial intelligence and automation continue to reshape industry, engineering education must also evolve. Integrating complex systems into teaching offers students the opportunity to engage directly with the data-driven ecosystem they will encounter in practice. The goal is not only to produce technically skilled engineers, but also thoughtful stewards of technology who can navigate its broader social and ethical dimensions. 

One ongoing challenge is that independent projects often vary in quality and can be difficult to assess. Without intentional design, students may default to trial-and-error approaches instead of drawing on knowledge from prior courses. At the same time, the pressure to cover extensive technical material can make it difficult to provide the broader systems context essential for modern engineering. Yet when learning is reinforced across the curriculum, students are better prepared for future careers that demand systems-based thinking. 

Experiential, self-directed projects play a crucial role in this preparation. They allow students to choose their own path while working closely with advisors and industry partners. Whether developing a product, designing a system, or engaging with professionals, students gain a perspective that feels different from traditional coursework. This process offers them a glimpse of what it means to think and act like real engineers, fostering both confidence and adaptability as they transition from the classroom to the workplace.

 

References:

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Rhythima Shinde (KLH Sustainability).

Topic: Applying Cynefin framework for climate resilience.  

Title: Managing floods in urban infrastructure.

Resource type: Teaching – Case study.

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

Keywords: Available soon.

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

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness.  A free spreadsheet version of the framework can be downloaded.

This resource relates to the Systems Thinking, Requirements Definition, Communication, Design For, and Critical Thinking INCOSE Competencies. 

AHEP4 mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses the themes of Sustainability and Communication. 

Educational level: Beginner; intermediate.

 

Acknowledgement: 

The case study underpinning this teaching activity was developed by Prof. Kristen MacAskill (University of Cambridge). The Module was ​first ​developed and implemented in teaching by TEDI- London, led ​by a team​ of learning technologists, Ellie Bates, Laurence Chater, Pratishtha Poudel, and academic member, Rhythima Shinde. This work was carried out in collaboration with the Royal Academy of Engineering through its Engineering X programme — a global partnership that supports safer, more sustainable engineering education and practice worldwide. With critical support from Professor Kristen MacAskill and involvement of Ana Andrade and Hazel Ingham, Aisha Seif Salim. This was a collective effort involving many individuals across TEDI-London and RAEng (advisors and reviewers), and while we cannot name everyone here, we are deeply grateful for all the contributions that made this module possible. 

 

Learning and teaching notes: 

This case study introduces a structured, systems-thinking–based teaching resource. It provides educators with tools and frameworks—such as the Cynefin framework and stakeholder mapping—to analyse and interpret complex socio-technical challenges. By exploring the case of the Queensland, Australia floods, it demonstrates how engineering decisions evolve within interconnected technical and social systems, helping students link theory with practice. 

The Cynefin framework (Nachbagauer, 2021; Snowden, 2002), helps decision-makers distinguish between different types of problem contexts—simple, complicated, complex, chaotic, and disordered. In an engineering context, this framework guides learners to recognise when traditional linear methods work (for simple or complicated problems) and when adaptive, experimental approaches are required (for complex or chaotic systems). 

Within this teaching activity, Cynefin is used to help students understand how resilience strategies evolve when facing uncertainty, incomplete information, and changing stakeholder dynamics. By mapping case study events to the Cynefin domains, learners gain a structured way to navigate uncertainty and identify appropriate modes of action. 

This case study activity assumes basic familiarity with systems concepts and builds on this foundation with deeper application to real-world socio-technical challenges.  

 

Summary of context:

The activity focuses on a case study of 2010–2011 floods in Queensland, Australia, which caused extensive damage to urban infrastructure. The Queensland Reconstruction Authority (QRA) initially directed resources to short-term asset repairs but subsequently shifted towards long-term resilience planning, hazard management, and community-centred approaches. 

The case resonates with global engineering challenges, such as flood, fire, and storm resilience, and can be easily adapted to local contexts. This case therefore connects systems thinking theory directly to engineering and governance decisions, illustrating how frameworks like Cynefin can support engineers in navigating uncertainty across technical and institutional domains. 

 

Learning objectives:

Aligned with AHEP4 (Engineering Council, 2020) – Outcomes 6, 10, and 16 on systems approaches, sustainability, and risk – this activity emphasises systems thinking, stakeholder engagement, problem definition, and decision-making under uncertainty. 

This teaching activity introduces learners to the principles and practice of systems thinking by embedding a real-world case study into engineering education (Godfrey et al., 2014; Monat et al.,2022). The objectives are to: 

 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

Time required: 

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

1. Context (1–2 hours) 

2. Analysis and insights (1–2 hours) 

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

 

Materials required:

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

This dedicated platform hosts the interactive modules designed for this teaching activity. Students progress through three modules — Context, Analysis and Insights, and Discussion and Transferable Learning. Each module includes animations, narrative-driven content, scenario prompts, and interactive tasks. The platform ensures flexibility: it can be used in fully online, hybrid, or face-to-face settings. All necessary digital assets (readings, maps, videos, and quizzes) are embedded, so learners have a “one-stop” environment.

2. Case study pack: Queensland Reconstruction Authority flood response

The core teaching narrative is anchored in this Engineering X case study. It documents the evolution of the Queensland Reconstruction Authority (QRA) from a short-term flood recovery body to a long-term resilience institution. This resource provides students with authentic socio-technical detail — including stakeholder conflicts, institutional learning, and systemic barriers — which they then interrogate using systems thinking frameworks.

3. Facilitator’s guide: (Appendix A)

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

4. Timeline touchpoints: (Appendix B)

This resource provides a suggested delivery schedule for facilitators. It maps when live sessions, asynchronous tasks, and group discussions should occur, ensuring students remain engaged over the course. It also indicates where key reflective points and assessments (both formative and summative) can be integrated.

5. Pre- and post-module assessment form: (Appendix C)

This tool evaluates students’ systems thinking learning outcomes. It includes:

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

 

Assessment:

 

Narrative of the case:

Learners are introduced to the case via a fictional guide, “Bernice,” who frames the scenario and supports navigation through the material. Students work through three stages that progressively apply the Cynefin framework and other systems tools to understand how resilience emerges through evolving governance and engineering responses: 

1. Context module: 

2. Analysis & insights module: 

3. Discussion & transfer learning module: 

 

Interactive learning design:

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

 

Why this approach adds value: 

Although rooted in social-technical interactions, the activity explicitly connects systems thinking to core engineering design competencies—problem framing, stakeholder analysis, and iterative solution development under uncertainty 

 

Guided questions and activities: 

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

 

Opportunities for extension: 

In addition to the Queensland floods and Sakura Cove examples, educators may draw parallels with urban heat planning in London, wildfire adaptation in Australia, or storm resilience in the Netherlands. These comparative cases allow learners to generalise systems insights beyond one event or geography. 

The activity is designed to be scalable and adaptable: 

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

 

References:

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

Toolkit: Complex Systems Toolkit.

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

Topic: Student created interactive simulation for complex sociotechnical systems.

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

Resource type: Teaching activity.

Relevant disciplines: Any.

Keywords: Available soon.

Licensing: This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It is based upon the author’s article “Enhancing Ethical Reasoning in Engineering Education through Student-Created Interactive Ethical Scenarios Using Generative AI,” 2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025, pp. 1-5, doi: 10.1109/EDUCON62633.2025.11016531. 

Downloads: 

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness.  A free spreadsheet version of the framework can be downloaded.

This resource relates to the Systems Thinking, Life Cycle, Configuration Management, Requirements Definition, Verification, and Validation INCOSE Competencies. 

AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses AHEP themes of Ethics and Communication. 

Education level: Intermediate.

 

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

Overview:

This resource enables engineering students to create, run, and debug a text‑based, interactive simulation of a complex sociotechnical system using a Large Language Model (LLM). It is intentionally flexible and may be delivered as a multi‑session studio activity (including assessment) or used solely as a compact assessment.

  

Purpose and use:

In both modes, students design a robust text prompt, test it with a user, document changes, and submit auditable artefacts that evidence learning. The key activity is interrogating their own thinking on how complex systems should be modelled by making judgements as to how their game does and does not capture the system dynamics. 

 

Why and how: 

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

The submission is a text LLM prompt with tracked changes, which allows students to demonstrate system design and debugging, produce transparent process evidence, and scale to large cohorts with minimal infrastructure. 

 

Delivery options at a glance:

Audience Undergraduate Years 2–4 and taught MSc, any engineering discipline 
Modes Studio activity (3–5×2 h + independent study) or Assessment‑only (prompt‑only; 1–2×2 h + 4–6 h)
Teams 3–4 students (solo permitted for assessment‑only) 
Assessment Portfolio (studio) or prompt‑plus‑change‑log (assessment‑only) 
Platforms Institutional Copilot licences successful; encourage exploration of free tools (students record model/version)

 

Materials and software:

 

Delivery modes:

Mode A — Studio activity (3–5 sessions) 

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

In both modes, module leaders may supply a predefined scenario(s) to standardise scope and simplify marking. A ready‑to‑use example is provided in Appendix C. 

 

Assessment:

Studio portfolio — rubric (suggested weighting):

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

 

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

 

Scenario options: 

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

 

Appendix A — Prompt template (simulation + debug‑ready): 

Title: Complex Systems Simulator — [Scenario] 

Purpose: Run a turn‑based interactive simulation of a complex sociotechnical system. Track named state variables, apply feedback and delays, and let the player’s decisions drive non‑linear outcomes. 

Setup: 

  1) Offer three roles (distinct authority/constraints). 

  2) Introduce 3–5 NPCs with clear goals and plausible interventions. 

  3) Show a dashboard of [STATE_VARIABLES] each turn with short context. 

State rules: 

Commands: status, talk [npc], inspect [asset], implement [policy], pilot [intervention], advance time, review log. 

Debug commands (for testing): trace on/off (print update logic), why (state which loops/delays drove the change), show variables (print current state table), revert (roll back one turn), reseed (slight exogenous shock). 

Realism and ethics: Allow all plausible actions and report consequences neutrally. If unsafe in the real world, refuse, propose safer alternatives, and continue with plausible systemic effects. 

LLM pitfalls to avoid: Do not invent new variables; ask clarifying questions rather than guessing; keep outputs concise; summarise trajectory every five turns. 

Begin: Greet the player, state the scenario, ask for a role, and wait. 

 

Appendix B — Debugging and play‑test checklist: 

Functional coherence 

Robustness 

User experience and clarity 

Report 

 

Appendix C — Predefined scenario (Urban Heatwave Response, UK city): 

Boundary: One UK local authority area during the July–August heatwave period. Focus on public health, energy demand, and community resilience. 

Roles: (1) Local Authority Resilience Lead; (2) NHS Trust Capacity Manager; (3) Distribution Network Operator (DNO) Duty Engineer. 

Stakeholders: Residents (with a focus on vulnerable groups), care homes, schools, SMEs, DNO, local NHS Trust, emergency services, voluntary/community groups, Met Office (for alerts), and local media. 

State variables (examples): Heat‑health alert level (0–4); Emergency Department occupancy (%); Electricity demand/capacity (% of peak); Indoor temperature exceedance hours (hrs > 27 °C); Public trust (0–100); Budget (£); Equity index (0–100). 

Events/shocks: Red heat alert; substation fault; procurement delay; misinformation spike on social media; transport disruption; community centre cooling failure. 

KPIs and stop conditions: Heat‑related admissions; unserved energy; cost variance; equity gap across wards. Stop if alert level 4 persists >3 days, budget overspends >10%, or trust <25. 

Notes for assessors: Using a standard, predefined scenario simplifies marking and ensures comparable complexity across teams, while still allowing for diverse strategies and outcomes. 

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

Toolkit: Complex Systems Toolkit.

Author: Mariam Makramalla, PhD, FRSA (New Giza University).

Topic: Integrating complex systems learning outcomes in engineering curricula.

Title: How to scaffold complex systems learning outcomes across a curriculum.

Resource type: Guidance article.

Relevant disciplines: Any.

Keywords: Available soon. 

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

Downloads:

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

 

Premise:  

Teaching and learning engineering carries with it a double layer of complexity. On the one hand, this complexity is connected to the growing interdisciplinary nature of engineering itself. On the other hand, the complexity is connected to the growing diversity of engineering students that are often present in one project team. This multifaceted complexity requires a re-envisioned understanding of the role and purpose of the engineering educator.  

With the growing trend of a global classroom reality, we often find that learners in the classroom are representing different cultures, which in turn are rooted in them unconsciously carrying historical and socio-cultural baggage relating to these cultures. Thus, it becomes crucial to unpack the challenge and potential that such a diverse collective intelligence can offer to an engineering learning experience.  

As our understanding of the engineering discipline gets more rooted and interconnected with the precarious reality that our world is witnessing today, it becomes essential that the engineering education community would take up a proactive role in actively contributing to the formation of engineering citizenship. In other words, every engineering student should be educated as a citizen that has mastered the engineering cross-cutting fields in such a way that they are free to create and solve problems of the present and the future.  

With this in mind, it becomes very clear that the one-size-fits all model of a single discipline engineering classroom can no longer sustain itself. It does not factor in the richness that a diverse student body can offer, and it dilutes the value and potential of an engineering learner to think clearly or solve problems. It is therefore imperative that engineering educators grasp the complex reality of an integrated engineering discipline and address it in a way that fosters scaffolding of diverse knowledge. Some students might specialise in one core technical discipline. Yet, future projections for most students showcase the need to have a wide level of exposure to broader competency development. Students need to learn to understand the field of engineering at large and to develop system thinking skills that enable them to exist, challenge and have an impact on the system that they are a part of.  

 

How to scaffold learning outcomes in a complex engineering curriculum:

The below table has been designed for embedding Complex Systems Learning Outcomes across an engineering curriculum. It maps against competencies and suggests scaffolding techniques across educational levels. It is also important to note, that efforts need to be made to align to the relevant AHEP requirements or other accreditation standards. Table 1 presents the different strands of the Complex Systems Engineering Curriculum, colour coded in line with the INCOSE Competency Framework outline (INCOSE, 2025). Table 2 presents a practical guide for educators to scaffold Complex Systems learning outcomes across a curriculum. The intention is for the scaffolding framework to compare the trade-offs between different elements of the competency group. For example, system modelling and analysis as an element from the core competency and planning from the management competency. The table suggests activities that would integrate different competencies together in a scaffolded approach.  

Table 1. Competency Areas for Complex Systems (INCOSE, 2025).

Table 1 presents Competency Areas for Complex Systems. As mentioned, the skills range to include a wide variety of competencies, thereby enabling a solid and grounded systems thinking approach for students. As students approach their learning, they go through a series of development stages that gradually build up student level of expertise until they reach the stage of what the INCOSE competency framework refers to as a lead practitioner role. Building on the competencies of the complex system toolkit presented in Table 1, Table 2 presents a potential outline for a scaffolding framework that maps varying threads of the framework in a way that enables scaffolded activities at every developmental stage for learners. Depending on the learning context and educational level, educators can choose which level of attainment is appropriate to their curriculum.  

Table 2. Scaffolding Complex Systems Learning Outcomes across the curriculum 

 

Discussion and next steps:

As we are approaching the fuzzy front end to complexity in engineering pedagogy, as educators we need to be constantly toggling between devising frameworks, being informed by literature, contextualising ideas, validating these in our classrooms and repeating this cycle to continually fine-tune our complex teaching navigational complexity framework. The invitation is open for all educators who would like to connect as we continue to explore different ways of developing responsible engineers who leave a lasting and sustainable mark transforming their stationed realities.  

 

References:

   

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

 

Toolkit: Complex Systems Toolkit.

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

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

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

Resource type: Teaching – Case study.

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

Keywords: Available soon.

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

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness.   A free spreadsheet version of the framework can be downloaded.

This resource relates to the Systems Thinking, Systems Modelling and Analysis, Ethics and Professionalism, Technical Leadership and Critical Thinking INCOSE Competencies.

AHEP4 mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses AHEP themes of Design, Ethics and Communication. 

Educational level: Intermediate; Advanced.

 

Learning and teaching notes:

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

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

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

 

Summary of the system or context:

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

 

Complex system features: 

 

Narrative of the case:

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

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

 

The dilemma: 

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

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

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

Activity flow: 

1. Introduce case and assign roles. 

2. Construct initial fault trees using evidence. 

3. Peer-review across groups. 

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

5. Individual reflection on complexity and uncertainty. 

 

Why use Fault Tree Analysis (FTA):

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

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

Advantages in this case: 

 

Questions and activities: 

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

 

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

 

Further challenge:

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

 

Assessment opportunities:

 

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

 

Toolkit: Complex Systems Toolkit.

Author: Dr Raja Toqeer CEng, MIET, CMgr, FCMI, FHEA, iPEER (University of Sheffield).

Topic: Methods and tools used to embed complex systems in engineering education. 

Title: High-level overview of complex systems methods and tools.

Resource type: Guidance article.

Relevant disciplines: Any.

Keywords: Available soon.

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

Who is this article for?: This article should be read by educators at all levels in higher education who are seeking an overall perspective on teaching approaches for integrating complex systems in engineering education. 

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded. 

This resource relates to the Systems Thinking, 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). Additionally, this resource addresses the Problem Analysis theme.  

 

Downloads: Available soon.

Learning and teaching resources:

 

Premise:

From smart cities and power grids to global supply chains, complex systems undeniably form the backbone of modern engineering challenges, integrating diverse technical and human domains to deliver resilient solutions that are capable of addressing emerging global demands. Traditional engineering approaches are limited in their ability to address increasingly complex and nonlinear problems, as they often fail to consider systems holistically. Complex systems exhibit dynamic behaviours and patterns that emerge from interactions within the whole, offering insights that go beyond what can be deduced from individual components (Martin, 2025).  

However, recognising complexity alone is insufficient. To engage meaningfully with such systems, engineers and educators require systematic methods and analytical tools that make the structure, behaviour, and evolution of complex systems more transparent and tractable. Methods such as system dynamics, network analysis, agent-based modelling and causal loop mapping enable the identification of affected points, feedback mechanisms and unintended consequences providing a structured way to explore “what if” scenarios and support informed decision making. Without these tools, understanding remains largely intuitive and fragmented, limiting the capacity to model interactions, predict emergent behaviours or design resilient interventions.  

There are many different ways to model complex systems, each suited to exploring particular types of interactions, timeframes, or behaviours. The following sections outline several commonly used tools and illustrate the contexts in which they can be effectively applied within engineering education. This guidance therefore focuses on the practical application and pedagogical integration of key complex systems methods and tools, with the aim of equipping engineering educators to embed systems thinking effectively in their teaching and practice. 

 

Systems thinking and mapping tools:

Systems thinking provides a holistic perspective for students to explore the interdependencies, feedback loops, and emergent behaviours that characterise complex engineering challenges. A range of mapping and modelling tools can be used to visualise and analyse system structures and behaviours. These tools can be broadly categorised into three categories: qualitative mapping tools (such as rich pictures and influence diagrams) that support shared understanding and problem framing; causal modelling tools (such as causal loop diagrams) that reveal feedback structures and dynamic behaviour; and quantitative simulation tools (such as system dynamics models) that enable experimentation and testing of hypotheses. 

Rich pictures, influence diagrams and causal loop diagrams are adaptable for both conceptual exploration and analytical modelling in engineering education. Each offers distinct advantages and limitations. Rich pictures are highly flexible, enabling diverse stakeholders to collaboratively capture multiple perspectives of a system. Their visual and narrative style promotes inclusivity and creativity but can lack analytical precision and consistency between users. Influence diagrams provide a more structured representation by showing directional relationships between variables, supporting clearer causal reasoning and decision making. However, they do not capture feedback or temporal dynamics, which limits their use in modelling evolving systems. Causal loop diagrams offer an advantage as they explicitly map, reinforcing and balancing feedback loops, giving powerful insights into system behaviour over time. However, these can become complex and difficult to interpret without adequate guidance and their qualitative nature may oversimplify quantitative relationships. When used in sequence, these tools can scaffold students’ systems thinking skills from exploratory mapping (rich pictures), through structural reasoning (influence diagrams), to dynamic analysis (causal loop diagrams). Embedding this progression in engineering education not only enhances students’ critical and reflective capabilities but also enables them to identify leverage points, anticipate unintended consequences and design resilient solutions that respond effectively to the complexity of real-world complex systems. 

Figure 1 presents a product causal loop diagram illustrating how product quality, sales, investment and profitability interact through reinforcing and balancing feedback loops. Two reinforcing loops (R1 and R2) show how profitability and product quality can drive self-sustaining growth: higher profits enable reinvestment in sales, while improved quality enhances customer satisfaction and market demand, both improving overall performance. In contrast, two balancing loops (B1 and B2) act as stabilising forces. When rapid sales growth strains production capacity, quality declines, prompting corrective investment to restore standards (B1). Meanwhile, as quality improves, it eventually reaches a maximum threshold where further gains lead to diminishing returns (B2), reflecting real-world technological and resource limits. Together, these loops demonstrate the dynamic interaction between growth and constraint in complex systems. The model highlights how feedback processes shape organisational performance and underscore the value of systems thinking for anticipating unintended consequences and supporting sustainable decision making in educational contexts where understanding system dynamics enhances learning and design practice. 

Figure 1. Product causal loop diagram (Credit: Creately)

 

System dynamics modelling:

System dynamics (SD) models simulate system behaviour over time by representing key elements such as stocks, flows, feedback loops, and time delays. This approach is particularly useful for understanding long-term patterns and testing interventions in complex contexts, such as modelling energy demand, tracking carbon emissions, or optimising supply chain dynamics. By using accessible tools like Stella, Vensim, or Insight Maker, educators can create interactive learning experiences that allow students to experiment with ‘what-if’ scenarios, deepen their understanding of dynamic behaviours, and develop the skills needed to make informed, data-driven decisions. Figure 2 illustrates a dynamic stock-and-flow diagram of a model for new product adoption. The diagram demonstrates how stock and flow structures can capture accumulations and delays within a system, providing insights into how adoption rates evolve over time in response to feedback processes. 

Figure 2. Dynamic stock and flow diagram of model New product adoption (taken from Wikipedia: model from article by John Sterman 2001 - True Software) 

 

Agent-based modelling:

Agent-Based Modelling (ABM) analyses complex systems by simulating the actions and interactions of many individual “agents” each following simple behavioural rules. Agents can represent people, vehicles, organisations or even machines depending on the context and their collective behaviour gives rise to larger system patterns that are often unexpected or counterintuitive. For example, in a traffic flow model, each car (agent) follows basic rules for acceleration, braking and lane changing. While these rules are simple in isolation, their combined effects can lead to emergent phenomena such as traffic jams or wave-like congestion patterns, behaviours not explicitly programmed into the system. Similarly, in a disease transmission model, each agent might represent a person whose movement and interactions influence infection spread across a population, providing valuable insight into intervention strategies. 

ABM is particularly useful in systems where differences among agents and local interactions matter. Whereas System Dynamics (SD) captures aggregate feedback through mathematical relationships, ABM reveals the distributional and spatial dimensions of system behaviour by modelling individual actions and decisions. Educators may choose ABM to help students see how microscale decisions lead to macroscale outcomes, reinforcing the concept that system-level order often emerges from local and uncoordinated interactions. Open-source platforms such as NetLogo provide accessible environments for teaching these principles, offering pre-built models that allow students to experiment with agent rules and parameters. Through such interactive exploration, engineering students can observe how small behavioural changes can cascade into large-scale effects deepening their understanding of emergence, adaptability and complexity in real-world complex systems. Figure 3 presents a schematic of an agent-based model, illustrating how interactions among individual agents within an artificial environment can lead to emergent system-wide patterns. 

Figure 3. Schematic of an agent-based model, showing how interactions between agents lead to emergent phenomena within an artificial world (Credit to Agent-Based Modeling and the City: A Gallery of Applications, Crooks, A., et al 2021). 

 

Network analysis and modelling:

Network analysis looks at how the pattern of connections within a system affects how it behaves, performs, and recovers from disruption. Instead of focusing on individual parts, this approach studies the relationships between elements whether they are people, machines, or data points and how these connections shape the overall outcome of the system. In network science, two important ideas help describe how a network is organised: degree distribution and clustering coefficients. Degree distribution shows how many connections (or “links”) each element, known as a node, has. If most nodes have a similar number of links, the network tends to behave in a steady and predictable way. However, if a few nodes have many more connections such as major airports in a flight network, the system can operate very efficiently but may also become more vulnerable if one of those key nodes fails. Clustering coefficients measure how connected a node’s neighbours are to each other. A high clustering coefficient means that a node’s connections are also well connected, forming strong local groups. This structure can improve communication and resilience within the network, though it may also limit flexibility or slow the spread of new information. 

By analysing these features, students learn that the way parts of a system are connected is just as important as the parts themselves. Real-world complex systems examples include power grids, transport networks, and organisational systems, where understanding connectivity helps engineers identify weaknesses and design for greater robustness. Tools such as Gephi and NetworkX make it possible to visualise and measure these network properties, helping turn complex data into clear, interpretable diagrams. Figure 4 shows the structure and properties of a technological network, illustrating how node connectivity and clustering together influence the system’s overall resilience. 

Figure 4. Composition and properties of technological network (Credit to Network Resilience: Definitions, approaches, and applications by Xiaoyu Qi and Gang Mei). 

 

Conclusion:

Understanding and managing complexity is now an essential skill for modern engineers. By gradually introducing students to different systems thinking tools from qualitative mapping to dynamic simulation and network analysis, educators can help them build a deep and transferrable understanding of how complex systems behave. Each tool offers a different perspective: mapping tools encourage exploration and shared understanding, dynamic models reveal feedback and time-based behaviour, and network analysis exposes structural patterns and resilience. Taken together, these approaches form a developmental pathway that strengthens students’ ability to think critically, reason systematically, and make informed design and management decisions. Embedding this progression within engineering education cultivates curiosity, adaptability, and a mindset equipped to tackle the interconnected social, environmental and technological challenges of the future. In doing so, educators prepare graduates not just to work with complex systems, but to improve and transform them.  

 

References: 

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

Toolkit: Complex Systems Toolkit.

Author: Dr. Rebecca Margetts (Nottingham Trent University).

Topic: The importance of teaching and learning about complex systems.

Title: The real world is a complex system.

Resource type: Knowledge article.

Relevant disciplines: Any.

Keywords: Available soon.

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

Downloads: 

Learning and teaching resources:

Who is this article for?: This article should be read by educators at all levels in higher education who are seeking an overall perspective on teaching approaches for integrating complex systems in engineering education. 

Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.

This resource relates to the Systems Thinking and Critical Thinking INCOSE competencies.

AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4):  Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). 

 

Premise: 

We live in a complex world. Complexity is a key challenge, captured in leadership terms by the VUCA framework: volatile, uncertain, complex and ambiguous (Lanucha 2024). Engineers have the privilege of creating products and processes for humans to use in this landscape. Each of these likely has numerous parts which interact, as well as interacting with the environment, people, and needing to meet a host of safety, quality, sustainability, ethics, and financial obligations. Traditionally, engineers analyse problems by breaking them down into simple parts. This helps understanding and makes calculations feasible, but it’s easy to lose understanding of the whole system. Any change can easily create a problem elsewhere. From a technical viewpoint, engineers need to understand this interconnectedness in order for their creations to work. In a wider sense, ‘systems thinking’ is a skill central to engineering quality and management techniques, which seek to rationalise the complexity of entire organisations and their ever-changing market pressures.  

 

The case for understanding systems: 

Systems is perhaps one of the most misunderstood words in engineering. It is often found combined with mathematical modelling or control – topics often perceived as challenging – and is used in other fields like Computer Science, where tools and models are different. In all cases, the idea revolves around a group of interacting or interrelated elements which form a unified whole. Those elements can be physical or information, hardware or software, or any combination of mechanical, electrical, and other engineering domains. Thinking in terms of systems can therefore be thought of as a holistic approach.  

The Engineering Council UK’s AHEP criteria include a systems approach: C/M6 – “Apply an integrated or systems approach to the solution of complex problems.” Several other AHEP criteria also reference complexity and complex problems, which they define as having “no obvious solution and may involve wide-ranging or conflicting technical issues and/or user needs that can be addressed through creativity and the resourceful application of engineering science. The Systems Thinking Alliance (2025) gives a broader definition of complexity as referring to “the condition of systems, objects, phenomena, or concepts that are challenging to understand, explain, or manage due to their intricate and interconnected nature. It involves multiple elements or factors that interact in unpredictable ways, often requiring significant information, time, or coordinated efforts to address.” For these, there is no ‘one-size-fits-all solution’ (Ellis 2025). This is the reality that engineers need to manage by understanding the potential effects on all parts of the system. 

In order to analyse, engineers dissect complexity into manageable components, and educators teach these simple components before moving onto more complex systems. For example, students initially learn basic electrical components, simple beams, rigid bodies, etc. before bringing these together in case studies, and then moving onto topics like mechatronic systems. Historically, engineers specialised on graduation, perhaps becoming a stress engineer or fluid dynamicist in dedicated offices and functional teams.  A design decision by one team could have unintended consequences for another, as well as additional uncertainty. The advent of cross-functional project and ‘matrix’ organisations mitigated against this, and companies have moved towards attribute teams which can consider the balance of behaviour. Even so, some uncertainty remains in the form of assumptions in calculations, changes in material properties with temperature or stress, or small variations in composition and manufacturing tolerances, which can all accumulate. Any parts which are bought ‘off-the-shelf’ or made by other companies under license must be carefully specified. Relationships can be nonlinear – or even chaotic – and contain feedback loops which can amplify changes (Kastens et al 2009). This all increases the risk of a product’s comfort, performance, and safety being impacted in ways that weren’t anticipated. Any problem that doesn’t come to light until the testing phase – late in the design process – represents costly redesigns and delays. In the unlikely event that a problem isn’t captured during testing either, the outcome could be disastrous. 

Systems engineers will bring the product together and establish these complex behaviours through models and testing. Identifying potential problems early in the design phase can save significant money and facilitate better designs. This can be challenging, especially for systems using novel materials or operating in extreme environments, which aren’t accurately captured by standard calculations. Models may be linearised, neglect external forcing, or be derived for an assumed air density or ambient temperature which may not be valid. In recent decades, the engineering industry has moved towards model-based design and virtual prototyping, facilitated by advances in computer tools. These are increasingly sophisticated, but models still need to be built by engineers with an appreciation of complexity and the mechanisms by which a problem could arise. As humans develop new materials and technologies, and explore the limits of what is possible, engineering techniques and calculations need constant revision, and software tools are frequently updated to facilitate this.  

That holistic view of problems has benefits outside of designing engineering artefacts. The manufacturing process is itself a complex system with potentially long supply chains. As is the organisation, which is comprised of numerous people operating in a landscape of financial pressures, employment law, politics and culture. Quality guru William Deming’s 14 Points for Management (Deming 2018) can be viewed as a systems approach to handling this complexity, by breaking down barriers between departments and instigating continuous improvement. Once a product is produced, it exists in a wider world and continues to interact with it. From a sustainability viewpoint, this can be the user and surrounding community, the environmental impact over a product’s lifecycle, and the financial markets which dictate whether a product is viable. It can also be the social, political, and legal landscapes: these can place direct constraints in the forms of laws governing safety and emissions (such as the UK’s legally binding target of net zero by 2050), or through embargos, tariffs, and subsidies. Each country has its own regulations, which can necessitate multiple variations of a product: a good example is cars, which need to be produced in both left- and right-hand drive, satisfy varying safety and emissions regulations, and cater for differing personal and cultural preferences for size, noise, usage and driving styles. Even when not legislated, a company might choose to support fair trade, lead the way in sustainable practices, or refuse to do business with suppliers or regimes they find objectionable – potentially making this a key part of their brand.  

An engineer’s ability to appreciate and understand the wider social and business landscape is a reason why finance and management consultancy companies can often be seen recruiting engineers at student careers fairs. The Sainsbury Management Fellowship (SMF) scheme notably develops UK engineers as industry leaders, and fellows have made a major contribution to the UK’s economic prosperity (RAEng 2025). 

 

Conclusions:

Complex systems are the “real world” that engineers attempt to understand and design for. They are complicated, interconnected, changing, and uncertain. The well-known part of engineering is analysis: breaking systems into understandable parts. There needs to be a parallel operation where those parts are assembled or integrated into a whole, and that whole interacts with everything around it. This is where unforeseen problems can occur. Systems models and a holistic systems thinking approach can mitigate this risk. A systems approach and ability to manage complexity is a key skill for engineers, and positions them well for other fields like management.   

 

References:

 

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.  

Teaching ethics and wondering how to tie learning outcomes to accreditation criteria? Look no further!

The Ethics Learning Landscape, part of the Engineering Ethics Toolkit‘s interactive Ethics Explorer, illustrates in table form the relationship between learning outcomes, AHEP criteria, graduate attributes, and possible locations for inclusion within a course or module.  

Whilst the Ethics Learning Landscape is best viewed as part of the Ethics Explorer, which replaced the static engineering ethics curriculum map published in 2015, there is also a printable version available in PDF form, that summarises content from the interactive Explorer.

The Ethics Explorer is designed to help engineering educators navigate the landscape of engineering ethics education, finding their own path through what can sometimes seem like a wilderness. The Ethics Explorer is part of the Engineering Ethics Toolkit, an open access resource designed to help engineering educators embed ethics in their teaching.

Access our latest Ethics Toolkit content, and learn how to get involved here.

 

This post is also available here.

Case study example: Water wars: managing competing water rights

Activity: Assessment. This example demonstrates how the questions provided in Assessing ethics: Rubric can be used to assess the competencies stipulated at each level.

Authors: Dr. Natalie Wint (UCL); Dr. William Bennett (Swansea University).

Related content:

 

Water wars: managing competing water rights 

This example demonstrates how the questions provided in the accompanying rubric can be used to assess the competencies stipulated at each level. Although we have focused on ‘Water Wars’ here, the suggested assessment questions have been designed in such a way that they can be used in conjunction with the case studies available within the toolkit, or with another case study that has been created (by yourself or elsewhere) to outline an ethical dilemma. 

Year 1 

Personal values: What is your initial position on the issue? Do you see anything wrong with how DSS are using water? Why, or why not?

Professional responsibilities: What ethical principles and codes of conduct are relevant to this situation?

Ethical principles and codes of conduct can be used to guide our actions during an ethical dilemma. How does the guidance provided in this case align/differ with your personal views? (This is a question we had created in addition to those provided within the case study to meet the requirements stipulated in the accompanying rubric.)

What are the moral values involved in this case and why does it constitute an ethical dilemma? (This is a question we had created in addition to those provided within the case study to meet the requirements stipulated in the accompanying rubric.)

What role should an engineer play in influencing the outcome? What are the implications of not being involved? (This is a question we had created in addition to those provided within the case study to meet the requirements stipulated in the accompanying rubric.)

Year 2 

Formulate a moral problem statement which clearly states the problem, its moral nature and who needs to act. (This is a question we had created in addition to those provided within the case study to meet the requirements stipulated in the accompanying rubric.)

Stakeholder mapping: Who are all the stakeholders in the scenario? What are their positions, perspective and moral values?

Stakeholder  Perspectives/interests  Moral values 
Data Storage Solutions (DSS)  Increasing production in a profitable way; meeting legal requirements; good reputation to maintain/grow customer base.  Accountability; sustainability (primarily economic). 
Farmers’ union  Represent farmers who suffer from economic implications associated with costly irrigation.  Accountability; environmental sustainability; justice. 
Farm  The farm (presumably) benefits from DSS using the land.  Ownership and property; environmental sustainability; justice. 
Local Green Party  Represent views of those concerned about biodiversity. May be interested in opening of green battery plant.  Human welfare; environmental sustainability; justice. 
Local Council  Represent views of all stakeholders and would need to consider economic benefits of DSS (tax and employment), the need of the university and hospital, as well as the needs of local farmers and environmentalists. May be interested in opening of green battery plant.  Human welfare and public health; trust; accountability; environmental sustainability; justice. 
Member of the public  This may depend on their beliefs as an individual, their employment status and their use of services such as the hospital and university. Typically interested in low taxes/responsible spending of public money. May be interested in opening of green battery plant.  Human welfare; trust; accountability; environmental sustainability; justice. 
Stakeholders using DSS data storage  Reliable storage. They may also be interested in being part of an ethical supply chain.  Trust; privacy; accountability; autonomy. 
Non-human stakeholders  Environmental sustainability. 

 

What are some of the possible courses of action in the situation. What responsibilities do you have to the various stakeholders involved? What are some of the advantages and disadvantages associated with each? (Reworded from case study.)

What are the relevant facts in this scenario and what other information would you like to help inform your ethical decision making? (This is a question we had created in addition to those provided within the case study to meet the requirements stipulated in the accompanying rubric.)

 

 

Year 2/Year 3  

(At Year 2, students could provide options; at Year 3 they would evaluate and form a judgement.) 

Make use of ethical frameworks and/or professional codes to evaluate the options for DSS both short term and long term. How do the uncertainty and assumptions involved in this case impact decision making?

Option  Consequences  Intention  Action 
Keep using water  May lead to expansion and profit of DSS and thus tax revenue/employment and supply. 

Reputational damage of DSS may increase. Individual employee piece of mind may be at risk. 

Farmers still don’t have water and biodiversity still suffers which may have further impact long term. 

 Intention behind action not consistent with that expected by an engineer, other than with respect to legality  Action follows legal norms but not social norms such as good will and concern for others. 
Keep using the water but limit further work  May limit expansion and profit of DSS and thus tax revenue/employment and supply. 

Farmers still don’t have water and biodiversity still suffers and may have further impact long term. This could still result in reputation damage. 

 Intention behind action partially consistent with that expected by an engineer.  Action follows legal norms but only partially follow social norms such as good will and concern for others. 
Make use of other sources of water  Data storage continues. 

Potential for reputation to increase. 

Potential increase in cost of water resulting in less profit potentially less tax revenue/employment. 

Farmers have water and biodiversity may improve.

Alternative water sources may be associated with the same issues or worse. 

 Intention behind action seems consistent with that expected by an engineer. However, this is dependent upon 

whether they chose to source sustainable water with less impact on biodiversity etc. 

 This may be dependent on the degree to which DSS proactively source sustainable water. 
Reduce work levels or shut down  Impact on profit and thus tax revenue/employment and supply chain. Farmers have water and biodiversity may improve. 

May cause operational issues for those whose data is stored. 

 Seems consistent with those expected of engineer. Raises questions more generally about viability and feasibility of data storage.  Action doesn’t follow social norms of responsibility to employees and shareholders. 
Investigate other cooling methods which don’t require as much water/don’t take on extra work until another method identified. 
 May benefit whole sector. 

May cause interim loss of service. 

 

 This follows expectations of the engineering profession in terms of evidence-based decision making and consideration for impact of engineering in society.  It follows social norms in terms of responsible decision making. 

 

Downloads:

Assessing ethics: Guidance

Assessing ethics: Rubric

Assessing ethics: Case study assessment example: Water Wars

 

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

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.

Authors: Dr. Natalie Wint (UCL); Dr. William Bennett (Swansea University).

Keywords: Assessment; Accreditation, AHEP, Competencies; Curriculum design; Pedagogy.

Who is this article for?: This article should be read by educators at all levels in higher education who wish to integrate ethics into the engineering and design curriculum or module design.

Related content:

 

Guidance

Premise:

As engineering educators, it is uncommon that we were taught or assessed on ethical thinking within our own degree programmes. Although we may be able to think of plenty of ethical scenarios from our own experience, we may not necessarily be able to identify the best way to assess the ability of a student to engage in ethical thinking in a systematic and robust manner, something which is critical for both the evaluation of learning and teaching (as explained further here).

Furthermore, the complex, ill-structured nature of ethical dilemmas, which often involve a variety of diverse stakeholders, perspectives and cultural norms, necessitates an ability to navigate tensions and compromise. This results in situations in which multiple possible courses of action can be identified, meaning that there is not one single ‘good’ or ‘correct’ answer to ethical questions posed.

It is also necessary to evidence that students are able to meet the criteria outlined by accreditation bodies. Within the UK context, it is the Engineering Council (EC) that is responsible for providing the principal framework which guides engineering course content and sets accreditation threshold standards of competence through AHEP, the Accreditation of Higher Education Programs, as part of The UK Standard for Professional Engineering Competence (UKSPEC).

The knowledge, skills and attributes expected of engineering graduates constantly shifts, and since the advent of AHEP in 2004 there has been increased focus on strengthening design, and consideration for economic, ethical, environmental, legal, and social factors.

In-keeping with a need to assess engineering ethics in a robust manner, this article provides step-by-step considerations for designing assessment and is primarily intended to be used in conjunction with an existing ethics case study, such as those available through the EPC’s Engineering Ethics Toolkit (we later make use of the existing ‘Water Wars’ case study to exemplify the points made).

The guidance and accompanying rubric have been designed in a way that encourages students to grapple with the numerous tensions involved in ethical decision making, and the focus is thus on assessment of the decision-making process as opposed to the ‘answer’ given, the decision made or the outcome of the scenario.

 

Assessment purpose:

The first consideration is the year group you are assessing, and the competencies they have already acquired (for example in the case of Level 5 and Level 6 students). You may want to consider the (partial) learning outcome (LO) as defined by AHEP4 LO8 (Table 1). Whilst this shouldn’t act to limit what you choose to assess, it is a good place to start in terms of the level of ability your students should be demonstrating.

Note that the Engineering Council (EC) claim “This fourth edition of AHEP has reduced the total number of learning outcomes in order to focus attention on core areas, eliminate duplication and demonstrate progression between academic levels of study”. They are thus interested in the differences between level. You are recommended to make this explicit in module specification and associated assessment description. Key differentiations are shown in Table 1. For example, at Level 5 you may be more interested in students’ abilities to identify an ethical situation, whereas at Level 6 you may want them to be able to reason through options or make a judgement.

Table 1: AHEP4 Learning Outcomes

Year 1
(Level 4)		 Year 2
(Level 5)		 Year 3
(Level 6)		 M Level
(Level 7)
LO8 Apply ethical principles and recognise the need for engineers to exercise their responsibilities in an ethical manner and in line with professional codes of conduct. Identify ethical concerns and make reasoned ethical choices informed by professional codes of conduct. Identify and analyse ethical concerns and make reasoned ethical choices informed by professional codes of conduct. Identify and analyse ethical concerns and make reasoned ethical choices informed by professional codes of conduct (MEng).
Interpretation Awareness of issues, obligations, and responsibilities; sensitising students to ethical issues. Ability to resolve practical problems; identify ethical issues and to examine opposing arguments. Ability to resolve practical problems; identify ethical issues and examine and evaluate/critique opposing arguments. Ability to resolve practical problems; identify ethical issues and examine and evaluate/critique opposing arguments.

 

The final row in Table 1 provides our interpretation of the LO, making use of language similar to that within the EPC’s Ethics Learning Landscape. We believe this is more accessible and more easily operationalised.

The following steps outline the process involved in designing your assessment. Throughout we make reference to an existing EPC case study (Water Wars) to exemplify the points made.

1.) The first consideration is how much time you have and how much of the case study you want to use. Many of the case studies have multiple stages and could be spread over several sessions depending on time constraints.

2.) Linked to this is deciding whether you want to assess any other LOs within the assessment. For example, many of the case studies have technical elements. Furthermore, when using reports, presentations, or debates as methods of assessment you may also want to assess communication skills. Whatever you decide you should be careful to design the assessment in such a way that assesses LO8 in a robust manner, whereby the student could not pass the element without demonstrating they have met the individual LO to the required level (this is a key requirement to meet AHEP4). For example, in an assessment piece where ethics is worth 50% of the grade, a student could still pass the element as a whole (with 40%) by achieving high scores in the other grading criteria without the need to demonstrate their ability to meet LO8.

3.) Once you are aware how much of a case study you have time for and have decided which LOs (other than LO8) you are assessing, you should start to determine which questions are aligned with the level of study you are considering and/or the ability of the students (for example you may query whether students at Level 5 have already developed the skills and competencies suggested for Level 4). At each level you can make use of the accompanying rubric to help you consider how the relevant attributes might be demonstrated by students. As an example, please refer to the accompanying document where we provide our thoughts about how we would assess Water Wars at Levels 4-6.

4.) Once you have selected questions you could look to add any complementary activities or tasks (that do not necessarily have to be assessed) to help the students broaden their understanding of the problem and ability to think through their response. For example, in the Water Wars case study, there are multiple activities (for example Part 1, Q3 and Part 2, Q3, Q4, Q6, Q7) aimed at helping students understand different perspectives which may help them to answer further ethical questions. There are also technical questions (for example Part 1, Q5) which help students understand the integrated nature of technical and social aspects and contextualise scenarios.

5.) Once you have selected your questions you will need to make a marking rubric which includes details of the weightings given for each component of the assessment. (This is where you will need to be careful in selecting whether other LOs are assessed e.g., communication, and whether a student can pass the assessment/module without hitting LO8). You can then make use of the guidance provided in terms of expectations at a threshold and advanced level, to write criteria for what is expected at each grade demarcation.

Although we have focused on ‘Water Wars’ here, the suggested assessment questions within the accompanying rubric have been designed in such a way that they can be used in conjunction with the case studies available within the toolkit, or with another case study that has been created (by yourself or elsewhere) to outline an ethical dilemma.

 

Other considerations:

As acknowledged elsewhere within the toolkit (see here), there are “practical limits on assessment” (Davis and Feinerman, 2012) of ethics, including demands on time, pressure from other instructors or administrators, and difficulty in connecting assessment of ethics with assessment of technical content. These are some other considerations you may wish to make when planning assessment.

• Number of students and/or marking burden: With large student numbers you may be more inclined to choose a group assessment method (which may also be beneficial in allowing students to share perspectives and engage in debate), or a format which is relatively quick to mark/allows automated marking (e.g. a quiz). In the case of group work it is important to find a way in which to ensure that all students within each group meet the LO in a robust manner. Whilst assessment formats such as quizzes may be useful for assessing basic knowledge, they are limited in their ability to ensure that students have developed the higher-level competencies needed to meet the LO at output level.

• Academic integrity: As with any LO there is a need to ensure academic integrity. This may be particularly difficult for large cohorts and group work. You may wish to have a range of case studies or ensure assessment takes place in a controlled environment (e.g. an essay/report under exam conditions). This is particularly important at output level where you may wish to provide individual assessment under exam conditions (although competencies may be developed in groups in class).

• Logistics/resourcing: Many of the competencies associated with ethics are heavily linked to communication and argumentation, and answers tend to be highly individual in nature. Role play, debates, and presentations may therefore be considered the most suitable method of assessment. However, their use is often limited by staffing, room, and time constraints. Many of these methods could, instead, be used within class time to help students develop competencies prior to formal assessment. You may also choose to assess ethics in another assessment which is more heavily resourced (for example design projects or third year projects).

• Staged assessment: The ethical reasoning process benefits from different perspectives. It may therefore be desirable to stage assessment in such a way that individuals form their own answer (e.g. a moral problem statement), before sharing within a group. In this way a group problem statement, which benefits from multiple perspectives and considerations, can be formed. Similarly, individuals may take the role of an individual stakeholder in an ethical dilemma before coming together as a group.

• Use of exams: Whilst we see an increasing movement away from exams, we feel that a (closed book) exam is a suitable method of assessment of ethics based LOs in the situation that:

o There is a need to ensure academic integrity, and that each student meets the LO at output level.

o The exam is assessing competencies (e.g. ethical argumentation) as opposed to knowledge.

o All the relevant information needed is provided and there is limited content for students to learn in advance (aside from argumentation, justification, decision making skills etc developed in class).

Their use may therefore be limited to Level 6.

 

Rubric

This document provides the partial AHEPLO8 at each level. The competences involved in meeting this LO have then been identified, along with what students would need to demonstrate to evidence meeting a threshold level, or advanced level. Example questions are given to show how students may demonstrate their competence at each level. For each question there is an explanation of how the question supports achievement of LO at that level. The rubrics should be used alongside the accompanying guidance document which offers practical suggestions and advice.

Year 1: This year focuses on developing awareness of issues, obligations, and responsibilities, and sensitising students to ethical issues.

Year 2: This year focuses on developing the ability to identify ethical issues and to examine opposing arguments, all of which is needed to examine, analyse, and evaluate ethical dilemmas in Year 3.

Year 3: This year focuses on ensuring that students can satisfy LO8 at an output level in a robust manner.

 

References:

Davis, M. and A. Feinerman. (2012). ‘Assessing graduate student progress in engineering ethics’, Science and Engineering Ethics, 18(2), pp. 351-367.

 

Downloads:

Assessing ethics: Guidance

Assessing ethics: Rubric

Assessing ethics: Case study assessment example: Water Wars

 

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

Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.

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