Toolkit: Complex Systems Toolkit.

Authors: Dr. Natalie Wint (University College London); Dr. Mohammad Hassannezhad (University College London); Dr. Manoj Ravi (University of Leeds).

Topic: Complex systems competencies.

Title: Understanding complex systems competencies required in engineering graduates. 

Resource type: Knowledge article.

Relevant disciplines: Any.

Keywords: Systems thinking; Problem-solving; Critical thinking; Digital literacy; Modelling and simulation; Design; Project management; Life cycle; Risk; Collaboration; Communication; Professional conduct; Social responsibility.

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

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

Learning and teaching resources:

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. 

AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4). 

 

Premise:

This article outlines the core competencies required for engineering students to effectively engage with complex systems. Such systems involve a range of technical and non-technical components that interact in non-linear and unpredictable ways. Working effectively with such complex systems requires collaboration across engineering disciplines, as well as other fields and stakeholder groups.  

Within AHEP4, complex problems are referred to as those which “have 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” (p.26). The ability to work productively with complex systems is therefore essential for engineers and helps them address problems increasingly experienced in business and society, which have many interdependent components and lack clear or stable solutions.  

The aim of this article is to provide a foundational framework that integrates the knowledge, skills and attitudes necessary for undergraduate and graduate engineering students to navigate complexity. In so doing, it serves educators, curriculum designers, and students seeking to develop the mindset and skills required to tackle the challenges of the 21st century within an increasingly volatile, uncertain, complex, and ambiguous (VUCA) world (SEFI, 2025).  

This knowledge article, informed by the INCOSE Competency Framework for Systems Engineering (INCOSE, 2018), categorises complex systems competencies into eight core competencies. These competencies encompass mindset and foundations, technical methods and tools, management and delivery, and attributes and behaviours. The description of each competency references learning outcomes (LOs) outlined in AHEP4 (Engineering Council, 2025) and the International Engineering Alliance (IEA) Graduate Attributes (2021) to establish a common baseline for all engineering graduates (see Appendix for mapping).  

 

The eight core complex systems competencies:

1. Systems thinking and problem framing 

The ability to take a holistic approach, to consider a problem from multiple perspectives and to understand how a system’s parts interact to produce emergent behaviour.  

Students must be able to understand what makes a system ‘complex’ and move beyond narrow problem-solving to identify root causes. This involves understanding fundamental Systems thinking concepts including hierarchies and interfaces (structural dimension), holism and cause-effect (dynamic dimension), lifecycles (time dimension), and multiple perspectives (perception dimension).  

Systems thinking enables engineers to anticipate ripple effects, emergent behaviours, and trade-offs, designing solutions that remain robust under uncertainty. AHEP4 requires students to “formulate and analyse complex problems to reach substantiated conclusions” (LO2) and to “apply an integrated or systems approach to the solution of complex problems” (LO6).  

2. Critical thinking 

The ability to question assumptions, evaluate evidence, apply logical reasoning, and justify decisions based on reasoned arguments and evidence.  

Navigating complex systems involves working with a variety of (often conflicting) goals, information, and data types from across discipline and stakeholder groups. Critical thinking is thus necessary to enable engineers to identify biases, avoid oversimplification and flawed reasoning, and to make ethical, transparent and evidence-informed decisions with consideration for unintended consequences. AHEP4 requires graduates to “critically evaluate technical literature and other sources of information to solve complex problems” (LO4). 

3. Simulation, modelling and data literacy 

The ability to apply scientific, mathematical, and engineering principles to model, test, and improve complex systems.  

Working with complex systems involves a range of resources including people, data and information, tools and appropriate technologies. Students must be able to create, apply and validate system models (as physical, mathematical, or logical representation of systems) and demonstrate competence in simulation and data literacy to address uncertainty and complexity at scale. This may involve using models and data to justify assumptions, explore scenarios, predict the consequences of actions, solve difference equations, conduct sensitivity and stability analysis, and predict the probability of risk.  

This aligns with several AHEP4 outcomes: “apply mathematics, statistics, and engineering principles to solve complex problems” (LO1); “apply computational and analytical techniques while recognising limitations” (LO3); and “select and critically evaluate technical literature and other data sources” (LO4).  

4. Design for complexity and changeability 

The ability to design adaptable, robust, and resilient systems across their lifecycle.  

Changes (both planned and unplanned) are inherent in complex systems. Long-term success of a system therefore requires design for resilience to first hand/internal (by the system), second hand/external (to the system) or third hand (around the system) change. Design for complexity and changeability ensures systems can evolve and integrate new capabilities across their lifecycle.  

AHEP4 requires engineers to be able to innovatively “design solutions that meet a combination of societal, user, business and customer needs” (LO5). This may involve designing systems that deliver required functions over time, including evolution, adaptability, and integration across subsystems (capability engineering), and supports evaluation of alternatives, balance competing objectives, and justify transparent decisions (decision management).  

5. Project and lifecycle management 

The ability to plan and deliver engineering activities across the system lifecycle, ensuring outcomes are delivered on time, on cost, and with integrity.  

Complex systems involve many subsystems with various purposes and lifecycles. This necessitates effective coordination and delivery processes and a focus on early planning and lasting systemic impacts. Project and lifecycle management allows for concurrent engineering (parallelisation of tasks), and verification and validation of tasks in dynamic environments. Graduates must “apply knowledge of engineering management principles, commercial context, project and change management” (AHEP4, LO15).  

This aligns with the Engineering Attribute of Project Management and Teamwork and the INCOSE Framework competencies in Lifecycle Processes, Integration, and Project Management, emphasising coordinated delivery and long-term value creation across socio-technical systems. Lifecycle awareness prevents short-term optimisation and emphasises aspects such as maintainability, whole-life value delivery and total expenditure (TOTEX) thinking, all of which support efforts towards sustainability and net-zero.  

6. Risk and uncertainty management 

The ability to identify, assess, and manage technical, social, environmental, and ethical risks at multiple levels of complex systems.  

Complex systems are inherently uncertain, with cascading risks that must be anticipated and managed proactively. Risk management enables students to quantify source and impact of uncertainties where possible and apply precaution where uncertainty is irreducible, ensuring safety, sustainability, and governance.  

AHEP4 requires graduates to “use a structured risk management process to identify, evaluate and mitigate risks (the effects of uncertainty)” (LO9), ranging from project-specific challenges to systemic threats, which need to “adopt a holistic and proportionate approach to the mitigation of security risks” (LO10).  

7. Collaboration and communication 

The ability to work effectively across disciplines, boundaries, and cultures, while conveying complex insights clearly to technical and non-technical audiences. 

Complex systems challenges cannot be solved by individuals alone and include consideration for stakeholders across industry, policy and society. Such collaborative processes involve participatory problem-solving, learning from others, inclusive communication, and negotiation and persuasion strategies, all of which necessitate emotional intelligence.  

AHEP4 expects graduates to “function effectively as an individual, and as a member or leader of a team, being able to evaluate own and team performance” (LO16). They must be able to influence stakeholder decisions, foster alignment, and shape outcomes across industry, policy, and society (AHEP4, LO17).  

8. Professional responsibility 

The ability to apply professional and societal responsibilities in decision-making, with awareness of ethical implications and long-term impacts and unintended consequences of engineered systems.  

Engineers increasingly work on complex systems that shape lives, societies, and ecosystems. Ethical responsibility ensures that technical competence aligns with social good and involves consideration for trade-offs between factors including environmental impact, affordability and social acceptance. This aligns with AHEP4, IEA, and INCOSE principles on ethics, professionalism, and leadership, ensuring engineers act responsibly within complex systems and contribute positively to society and sustainability. AHEP4 requires graduates to “identify and analyse ethical concerns and make reasoned ethical choices informed by professional codes of conduct” (LO8) and “evaluate the environmental and societal impact of solutions to complex problems” (LO7).  

 

Conclusions:

This article defines a set of eight integrated competencies that prepare engineering graduates to navigate complex systems. Together, they combine knowledge (what graduates must know), skills (what they can do), and attitudes (how they behave and think). Embedding these competencies requires project-based learning, interdisciplinary collaboration, and reflective exercises, while assessment should include portfolios, teamwork, and scenario analysis. Employers and professional bodies can reinforce these competencies through mentoring, internships, and early career development. 

By aligning with INCOSE, AHEP4, and IEA GA frameworks (see Appendix for mapping), this guidance provides an internationally consistent foundation that can be adapted to local contexts, equipping engineering graduates to address complex, interdependent challenges of the 21st century with competence, integrity, and resilience.  

 

Appendix:  

Mapping between Eight Core Competencies and Standard frameworks 

Proposed Core Competency   INCOSE * AHEP4 ** IEA GA *** 
Systems Thinking & Problem Framing ST LO2, LO6 WA2
Critical Thinking   CT LO4 WA4, WA11 
Simulation, Modelling & Data Literacy  IM, SM  LO1, LO3, LO4  WA1, WA4, WA5
Design for Complexity & Changeability  CP, DM, DF LO5  WA3 
Project & Lifecycle Management   LC, PL, CE, CP  LO15  WA10 
Risk & Uncertainty Management  CE, PL, RO  LO9, LO10 –
Collaboration & Communication   CC, TD, TL, EI  LO16, LO17  WA8, WA9 
Professional Responsibility  EI, EP  LO7, LO8  WA6, WA7 

 

* INCOSE Competency Framework, 2nd edition (2018) 

** AHEP4 Learning Outcome (LO) (2025) 

*** International Engineering Alliance (IEA) Graduate Attributes (GA) (2021) 

 

CC = Communications 

CE = Concurrent Engineering  

CP = Capability Engineering 

CT = Critical Thinking 

DF = Design For … 

DM = Decision Management 

EI = Emotional Intelligence 

EP = Ethics and Professionalism 

IM = Information Management 

 LC = Life Cycle 

LO = Learning Outcome 

PL = Planning 

RO = Risk and Opportunity Management 

TD = Team Dynamics 

TL = Technical Leadership 

SM = Systems Modelling and Analysis 

ST = Systems Thinking 

WA = Washington Accord 

 

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: 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: Interdisciplinarity; Problem-solving; Problem-based learning; Active learning; Professional development; Collaboration; Real world; Artificial Intelligence; Trade offs.

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

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

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

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

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

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

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

Educational level: Beginner; intermediate.

 

Acknowledgement: 

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

 

Learning and teaching notes: 

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

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

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

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

 

Summary of context:

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

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

 

Learning objectives:

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

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

 

Teachers have the opportunity to: 

 

Downloads: 

 

Learning and teaching resources:

 

Time required: 

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

1. Context (1–2 hours) 

2. Analysis and insights (1–2 hours) 

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

 

Materials required:

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

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

2. Case study pack: Queensland Reconstruction Authority flood response

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

3. Facilitator’s guide: (Appendix A)

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

4. Timeline touchpoints: (Appendix B)

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

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

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

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

 

Assessment:

 

Narrative of the case:

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

1. Context module: 

2. Analysis & insights module: 

3. Discussion & transfer learning module: 

 

Interactive learning design:

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

 

Why this approach adds value: 

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

 

Guided questions and activities: 

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

 

Opportunities for extension: 

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

The activity is designed to be scalable and adaptable: 

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

 

References:

 

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

Toolkit: Complex Systems Toolkit.

Author: 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: Learning outcomes; Pedagogy; Curriculum; Curriculum map; Critical thinking; Problem-solving; Life cycle; Decision-making . 

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. 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: Problem solving; Feedback loops; Decision-making; VUCA; Optimisation; Public health and safety; Risk; Sustainability; Ethics; Responsible design; Life cycle; Societal impact; Enterprise and innovation.

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.  

Objectives: To equip learners with the skills to successfully navigate digital and traditional recruitment processes for engineering roles. This includes demonstrating EDI, technical, and employability skills using the STAR framework; tailoring CVs for AI and Applicant Tracking Systems (ATS); and preparing for aptitude and abstract reasoning tests through targeted practice to enhance problem-solving and analytical abilities.

Introduction: Large national and international employers use digital application processes to recruit graduates. These digital applications aim to capture personal details, education, and work experience. Reflect on your experiences to demonstrate your EDI, employability, and technical skills applied using the STAR (Situation, Technique, Action, and Result) framework. Smaller and medium enterprises typically seek cover letters and CVs. 

Topic: Navigating digital recruitment in engineering: CVs, AI, and aptitude tests.

Keywords: Equity Diversity and Inclusion; Employability and skills; Problem solving; Assessment criteria or methods and tools; CVs and cover letters; Digitalisation; Artificial intelligence; Information and Digital literacy; Communication; Technical integration; Writing skills; Inclusive or Responsible design; Neurodiversity; Curriculum or Course; Computer science; Computing; Engineering professionals; Professional development; Recruitment; Digital engineering tools; Business or trade or industry; Workplace culture

 

Master the art of applying for engineering computing jobs

In the video below, Professor Anne Nortcliffe explains how to develop expertise in securing engineering computing positions by demonstrating technical proficiency and employability skills through well-supported, evidence-based responses.

Video summary:

Master the art of applying for engineering computing jobs by showcasing both technical and employability skills through evidence-based responses. 

Key insights:

⚙️AI in hiring: Understanding that many companies use AI for initial screenings emphasizes the need for clear, evidence-based answers in applications. 

✏️Individual contributions: Highlighting personal achievements rather than team efforts showcases leadership and initiative, key traits employers seek. 

💡Interpersonal skills: Employers value teamwork and leadership; demonstrating how you’ve influenced others highlights your potential as a valuable team member. 

💬Diversity matters: Bringing unique social perspectives into projects can lead to more inclusive solutions, making your application stand out. 

⭐STAR methodology: Using the STAR method helps structure your experiences into compelling narratives, making it easier for employers to assess your qualifications. 

🗒️Tailored applications: Customising your CV and cover letter for each job application reflects your genuine interest and ensures relevance to the employer’s needs. 

📚Professional etiquette: Ending your application with gratitude and a clear call to action maintains professionalism and shows your enthusiasm for the role. 

 

AI and Applications

To navigate digital recruitment, it’s crucial to understand AI’s role in candidate screening. Tailor your CV to pass AI and Applicant Tracking Systems (ATS) using resources that provide insights into keywords, formatting, and strategies. This enhances your visibility and competitiveness in the digital recruitment process. 

Further links to look at:

Please note that after clicking these links, you will need to create a free account on the external website to access the materials.

 

CV and Covering Letter

CV templates to support students and graduates to stand out and highlight their engineering and technology capabilities, especially when applying to Small and Medium Enterprises (SMEs) that do not use AI recruitment tools.

  1. CV template – Word 
  2. CV template – Publisher 
  3. CV template – Publisher with Advice 

For applications to large corporations that use AI recruitment tools, it is recommended:

 

Aptitude and Abstract Reasoning Test 

If your digital application is successful you will be typically invited to complete an aptitude and abstract reasoning tests to evaluate candidates. To excel, practice brain training exercises and brain teasers to enhance problem-solving, critical thinking, and analytical skills. Regular practice with similar questions boosts confidence and performance, improving your chances of passing these tests and standing out in the recruitment process. 

Further links to look at:

 

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.

Please note: Discussions around discrimination, prejudice and bias are highly complex and part of a much wider national and international debate, including contested histories. As such, we have limited the scope of our resources to educating and supporting students.

The resources that the EPC and its partners are producing in this area will continue to expand and, if you feel there is an issue that is currently underrepresented in our content, we would be delighted to work with you to create more. Please get in touch.


Objectives: This activity is our guide to navigating assessment centres, offering tips and strategies tailored to empower underrepresented groups and help you be prepared, authentic self, stand out and succeed. 

Introduction: Assessment centres have been a key part of graduate recruitment since the 1950s, originally developed to evaluate leadership potential in military officers. Today, they are widely used by employers to assess candidates through group tasks, interviews, and individual exercises. This activity serves as a practical guide to help you navigate assessment centres with confidence. With a focus on empowering underrepresented groups, it provides tips and strategies to help you prepare effectively, present your authentic self, and stand out in a competitive selection process.

Topic: Standing out with confidence at assessment centres: a guide to preparation, authenticity, and success.

Keywords: Problem solving; Employability and skills; Communication; Leadership or management; Collaboration; Digitalisation; Professional development; Writing Skills; Equity, Diversity and Inclusion; Neurodiversity; Inclusive or Responsible design; Recruitment; Business or trade or industry; Workplace culture; Information and Digital literacy; Artificial Intelligence.

 

An immersive experience

Getting startedWhat to expect An employer’s guide What are assessment centre activities?

Click on each accordion tab to explore videos that guide you through navigating assessment centres, offering tips and strategies designed to empower underrepresented groups and help you prepare, be your authentic self, stand out, and succeed.

Video summary: 

This video was produced by The Careers Chat, a platform associated with Warwick University, provides an overview of assessment centres used by graduate recruiters. It discusses various tasks designed to evaluate candidates’ skills in action, offering insights into the selection process and tips for preparation.  

Key insights: 

🌟 Always be mindful that you’re being assessed – from the moment you arrive until you leave. Maintain a professional and approachable demeanor to leave a lasting positive impression. 

🤝 View fellow candidates as collaborators, not competitors. Respect their perspectives and engage in teamwork; remember, it’s possible that everyone could be offered a role. 

💼 Keep in mind that the tasks are tailored to the role you’re applying for. Be authentic, and the skills you’ve already highlighted in your application will naturally stand out. 

Video summary:

Assessment centres are crucial for graduate recruitment, involving various tasks to evaluate candidates’ skills through collaborative activities.

Key insights:

🎓 Real-time evaluation: Assessment centres provide an opportunity for recruiters to observe candidates in action; skills, interpersonal dynamics and teamwork.

📅 Duration and format flexibility: Be prepared and mentally ready for either a half-day or full-day assessment face to face or online.

📝 Diverse assessment tasks: Wide range of tasks, from essays to presentations, means candidates should practice and be adaptable to showcase different skills.

🤝 Collaboration over competition: Viewing fellow candidates as collaborators rather than competitors can foster a supportive atmosphere, better outcomes for everyone.

🌈 Authenticity matters: Presenting genuine skills and authentic experiences rather than trying to fit a mould can make candidates stand out and connect with recruiters.

🚪 Professionalism is key: From the moment you arrive until you leave, maintaining a professional demeanour leaves a lasting impression, and suitability for the role.

💡 Preparation is essential: Familiarising oneself with the specific tasks related to the job application can boost confidence and performance, and draw upon relevant skills.

Video summary:
An assessment centre evaluates candidates through various exercises to assess teamwork, problem-solving, and fit within the company culture.

Key insights:

🔍 Assessment centres are designed to simulate real work environments, helping employers see how candidates fit into team dynamics and your ability to collaborate.

🧠 Psychometric tests may be retaken during the assessment, so candidates should be prepared to demonstrate their logical reasoning and numerical skills in person.

🗣️ Group exercises focus on problem-solving as a team, the process is more important than the outcome, opportunity to show your communication and leadership skills.

🎤 Presentations, whether in groups or individually, evaluate public speaking and the ability to synthesize complex information into clear solutions.

🎭 Role-play exercises test candidates’ client-handling skills and ability to provide solutions under pressure, highlighting their problem-solving approach.

🤝 Lunch and breaks are part of assessment, are an opportunity to network, and demonstrate your informal communication skills that could influence your success

📊 You need to demonstrate understanding and applying the company’s core values and meeting their desired competencies effectively throughout the process.

 

Resources

 

Underrepresented groups preparing for virtual assessment centres 

 

How to PASS an assessment centre UK

The video offers tailored guidance specifically for international students.

 

Acing virtual assessment centres: future you webinar: 

As part of their Future You webinar series, Prospects hosted a session titled Acing Virtual Assessment Centres on Tuesday, 20th April 2021. The webinar offers valuable insights, practical tips, and expert guidance to help students confidently navigate virtual assessment centres. Watch the video below to gain useful strategies and boost your preparation. Aldi, Arcadis and Police Now Recruiters advice for preparing for Virtual Assessment centres.

 

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.

Please note: Discussions around discrimination, prejudice and bias are highly complex and part of a much wider national and international debate, including contested histories. As such, we have limited the scope of our resources to educating and supporting students.

The resources that the EPC and its partners are producing in this area will continue to expand and, if you feel there is an issue that is currently underrepresented in our content, we would be delighted to work with you to create more. Please get in touch.


Objectives: EDI Quest is an interactive game designed to deepen your understanding of Equality, Diversity, and Inclusion (EDI) in the workplace. This immersive experience consolidates EDI concepts into a single adventure, challenging you to reflect and apply your knowledge to solve real-world scenarios. 

Introduction: This interactive learning experience brings Equality, Diversity, and Inclusion (EDI) principles to life through gameplay. As you navigate real-world workplace scenarios, you’ll be challenged to apply your knowledge, make thoughtful decisions, and reflect on the impact of inclusive practices. This activity is designed to make learning about EDI engaging, practical, and memorable.

Topic: An interactive game-based resource that helps students explore and apply Equality, Diversity, and Inclusion (EDI) principles through real-world workplace scenarios.

Keywords: Equity, Diversity and Inclusion; Inclusive or Responsible design; Communication; Employability and skills; Professional development; Problem solving; Digitalisation; Information and Digital literacy.

How it works: In EDI Quest, you’ll face challenges and scenarios mirroring real-life workplace situations. Each level tests your EDI knowledge, offering instant feedback and learning opportunities. For an optimal experience, we encourage you to engage with this academic game alongside others. It is designed to be played collaboratively, so we recommend involving a friend, colleague, professor, or even a parent. Playing in pairs or groups will enhance your learning experience and provide valuable perspectives and insights that you might not gain when playing in isolation

System requirements: EDI Quest is accessible on most web browsers and devices. For the best experience, use the latest version of Chrome, Firefox, or Safari on mobile, desktop, or laptop. 

How to access the game: Displayed below is the “Level Up EDGE” page. To access the game, please navigate to the “Interactive” tab within the page interface. To enhance your gameplay experience, adjust your browser’s zoom level as needed.

 

EDI quest


We’re excited to share that the EDI Quest game is currently being enhanced. Please check back soon to experience the new and improved version! In the meantime, you can download the game in Word format and dive straight into the scenarios. It’s a hands-on way to explore the activities and put your learning into practice while we put the finishing touches on the interactive version. Simply click this banner to get started.

 

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.

Please note: Discussions around discrimination, prejudice and bias are highly complex and part of a much wider national and international debate, including contested histories. As such, we have limited the scope of our resources to educating and supporting students.

The resources that the EPC and its partners are producing in this area will continue to expand and, if you feel there is an issue that is currently underrepresented in our content, we would be delighted to work with you to create more. Please get in touch.

Authors: Dr. Kieran Higgins(Ulster University); Dr. Alison Calvert (Queen’s University Belfast).

Topic: Integrating Education for Sustainable Development (ESD) into higher education curricula.

Type: Guidance

Relevant disciplines: Any.

Keywords: Curriculum design; Global responsibility; Sustainability; SDGs; Course design; Higher education; Pedagogy;

Sustainability competency: Anticipatory; Integrated problem-solving; Strategic; Systems thinking.

Related SDGs: SDG 4 (Quality education); SDG 13 (Climate action).

Reimagined Degree Map Intervention: Adapt and repurpose learning outcomes; Authentic assessment; Active pedagogies and mindset development.

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

Link to resource: AdvanceHE’s Education for Sustainable Development Curriculum Design Toolkit

 

Learning and Teaching Notes:
Supported by AdvanceHE, this Toolkit provides a structured approach to integrating Education for Sustainable Development (ESD) into higher education curricula. It uses the CRAFTS methodology and empowers educators to enhance their modules and programs with sustainability competencies aligned with UN Sustainable Development Goals.

Key Features:
• Five-Phase Process: Analyse stakeholder needs, map current provision, reflect on opportunities for development, redesign with an ESD focus, and create an action plan for continuous enhancement.
• Practical Tools: Includes templates for stakeholder analysis, module planning, active learning activities, and evaluation.
• Flexible Implementation: Designed for use at both module and programme level.
• Competency-Based: Focuses on developing authentic learning experiences across cognitive, socio-emotional, and behavioural domains.

Benefits
• Identify stakeholder sustainability needs
• Map existing ESD elements in your curriculum
• Reflect on opportunities to enhance ESD integration
• Redesign modules with active learning approaches of ESD
• Create actionable plans for implementation and evaluation

Click here to access the Toolkit.

Read more here.

 

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

Theme: Research, Collaborating with industry for teaching and learning, Knowledge exchange

Author: Prof Balbir Barn (Middlesex University), Prof Tony Clark (Aston University), Vinay Kulkarni (TCS) and Dr Souvik Barat (TCS)

Keywords: Digital Twin, Model Driven Engineering, Inclusive Innovation

Abstract: Researchers at Middlesex University initiated a collaboration in 2011 with Tata Consultancy Services Research in India based on their research on lightweight methods for enterprise modelling. Since 2014, that initial introduction has developed into a sustained and ongoing collaborative research programme in programming languages and environments to support model based decision making in complex and uncertain scenarios. The research programme has supported annual sabbatical visits to the TCS research labs in India; a PhD studentship; and regular workshop/advanced tutorials at international conferences. The continuing programme is an example of industry based research problems driving academic collaboration in an international context that has led to over 30 research outputs, an Impact Case Study submitted to REF2021, a TCS software product and the establishment of the London Digital Twin Research Centre at Middlesex.

 

Introduction

This case study describes the outcomes of an ongoing collaboration between Middlesex University with Tata Consultancy Services Research, India’s premier software research centre. The collaboration initiated in 2011, was triggered by a research paper published by Clark, Barn and Oussena [3]. The research proposed a precise, lightweight framework for Enterprise Architecture that views an organization as an engine that executes in terms of hierarchically decomposed communicating components. Following a visit to the TCS Research Labs (TRDDC) in Pune, India, a joint research programme between TCS and Middlesex was established to further the notion of the “Model Driven Organisation”. A key feature of the collaboration was the notion of inclusive innovation, from problem location to shared mutual benefits. The research programme has supported annual sabbatical visits to the TCS research labs in India; a PhD studentship; and regular workshops/advanced tutorials at international conferences. The continuing programme is an example of industry-based research problems driving academic collaboration in an international context that has led to over 30 research outputs, an Impact Case Study submitted to REF2021, a TCS software product and the establishment of the London Digital Twin Research Centre at Middlesex.

Systemising a model for collaboration

In 2011, developing strong, sustained and inclusive model of collaboration with industry was seen as an important element of reputation building activities for Middlesex University as it set out to establish an overseas campus in India. The goal was that Middlesex should be seen to delivering impact both to project outcomes but also as value to the geographical setting of the collaboration.  Thus, in 2011, two senior academics, Prof. Balbir Barn and Prof Tony Clark embarked on a visit to India’s leading IT research centres including the Tata Research and Development Centre (TRDDC), IBM Research, Microsoft Research, Accenture Research, HCL Research, Infosys, Cognizant and others. At these visits, the senior academics were able to showcase Middlesex Computer Science research activities leading to two memorandums of cooperation with Accenture and TRDDC. Middlesex CS had also decided to establish a strong presence at India’s premier Software Engineering conference(ISEC) through research papers, tutorials, and the organising of workshops aimed at capacity building of Indian academia (Value in the process).

Further meetings with chief scientist – Vinay Kulkarni from TRDDC in 2012 at ISEC, led to the idea of collaboration around the notion of the “Model Driven Organisation” where an enterprise can be represented symbolically by a model that draws its information/data from range of software artefacts used by the enterprise in its daily operations. Executives are then able to use this model representation as a decision-making aid.

The collaboration was seen as a shared vision that would be beneficial to both partners (TRDDC and MDX) so at the outset, we agreed to make our joint research publicly available with both partners retaining the option to productise any research outputs. However, there was This collaboration can also be seen as a model for Inclusive Innovation in that the research roadmap references a problem from the “wild”, where key stakeholders are engaged equally from research problem formulation, through to research publications and where there are mutual benefits.

The collaboration also developed a way of working that was critical to its subsequent success. TRDDC supported travel and subsistence of Barn and Clark to its research labs in Pune on annual two week “mini-sabbaticals”. These visits which have run since 2012 to now (only coming to pause due to COVID-19) are linked to the ISEC conference where papers, tutorials and workshops have been regularly presented. There has been a strong focus on development of young academics in India at this conference, further establishing the impact of our inclusive innovation approach by generating value in the setting. While the primary interaction is with the TRDDC Software Engineering Laboratory, seminars and other research exploration opportunities are made possible by meetings with other laboratories (such as Psychology). Some of the annual meetings have been supplemented by further meetings at Middlesex. Each annual visit is an intensive research meeting from which emerges the research plan for the year alongside a publication and impact plan. Very early on, we recognised the potential for an impact case study for the periodic research evaluation exercise conducted in the UK.

 

Figure 1: Research Roadmap

 

Outcomes

The collaboration has proved to be singularly successful in delivering concrete outcomes. Our regularly updated research roadmap (see Figure 1.) has evolved from our initial concept of the Model Driven Organisation, through to a practical language (ESL) and execution environment for enterprise simulation and now to advances to methodologies for digital twin design.

Along the way, a TCS Research Scientist (Souvik Barat) has completed a doctoral study in the design of a modelling language to support enterprise decision making. This language would later contribute to work by Dr Souvik Barat to design a sociotechnical digital twin of the City of Pune, to support non-pharmaceutical interventions during the Covid-19 pandemic. 

The ESL Language (lead Prof Tony Clark) developed as a TRL-5 prototype through the collaboration has formed the basis of a TCS TwinX™ software product developed by TCS and is now being used by TCS consulting.

The collaborative research programme has generated over 30 research publications at leading computing conferences and journal publications. Representative publications are listed [2,4,5,6]. The team has also generated impact and knowledge transfer through the production of advanced tutorials and workshops at conferences. The collaboration has also produced an edited book [7].

Recognising the importance of outcomes to the two respective organisations, the research has contributed to executing the research strategy of TCS Research (see strategy document) and has led directly to an impact case study submitted to REF2021.

Further value derived from our inclusive innovation approach has led to developing research publication preparation skills at TCS and even wider social impact through the pandemic planning activities in Pune City [1]. See the video: https://www.youtube.com/watch?v=x48G7-bOvPY).

In 2019, as our research work has steadily shifted towards Digital Twin technologies, Middlesex established the London Digital Twin Research Centre (LDTRC). The centre combines the software engineering research with cyber-physical systems and telecommunications research to present a means of showcasing a range of externally funded Digital Twin research projects. The focus of the centre has been brought to the attention of EPSRC and it holds regular business facing workshops.

Lessons learnt

Developing a strategic collaboration requires: investment from universities; a spirit that places collaboration and not competition at its heart, and willingness from academics to look for long-term benefit. Two senior academics spent three weeks touring Indian IT research labs with no guarantee of success. Hence, alignment with university strategy is critical.

Systemising this model of cooperation should be considered a strategic objective of UK Research and Innovation. A recognition that such success can be found in all our universities is imperative. While the EPSRC and RAE have “visiting academic-industrial collaborator” schemes they could generate much greater outcomes if their scale was smaller and they were genuinely accessible to all academics at all institutions.

References

  1. Barat, Souvik, Ritu Parchure, Shrinivas Darak, Vinay Kulkarni, Aditya Paranjape, Monika Gajrani, and Abhishek Yadav. “An Agent-Based Digital Twin for Exploring Localized Non-pharmaceutical Interventions to Control COVID-19 Pandemic.” Transactions of the Indian National Academy of Engineering 6, no. 2 (2021): 323-353.
  2. Barat, S., Kulkarni, V., Clark, T., Barn, B. (2019) An Actor Based Simulation Driven Digital Twin for Analyzing Complex Business Systems. Proceedings of the 2019 Winter Simulation Conference, 2019, Maryland, USA.(doi: 10.1109/WSC40007.2019.9004694)
  3. Clark, T., Barn, B.S. and Oussena, S., 2011, February. LEAP: a precise lightweight framework for enterprise architecture. In Proceedings of the 4th India Software Engineering Conference (pp. 85-94). ACM. (doi:10.1145/1953355.1953366)
  4. Clark, T., Kulkarni, V., Barn, B., France, R., Frank, U. and Turk, D., 2014, January. Towards the model driven organization. In 2014 47th Hawaii International Conference on System Sciences (pp. 4817-4826). IEEE. (doi:10.1109/HICSS.2014.591)
  5. Clark, T., Kulkarni, V., Barat, S. and Barn, B., 2017, June. ESL: an actor-based platform for developing emergent behaviour organisation simulations. In International Conference on Practical Applications of Agents and Multi-Agent Systems (pp. 311-315). Springer, Cham. (doi: https://doi.org/10.1007/978-3-319-59930-4_27 )
  6. Kulkarni, V., Barat, S., Clark, T. and Barn, B., 2015, September. Toward overcoming accidental complexity in organisational decision-making. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 368-377). IEEE. (doi:10.1109/MODELS.2015.7338268)
  7. Kulkarni, Vinay and Sreedhar Reddy, Tony Clark, and Balbir S. Barn, eds. Advanced Digital Architectures for Model-Driven Adaptive Enterprises. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0108-5

 

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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