You can also search here for meetings of the Ethics Advisory Group, and Ethics Ambassadors.
You can also search here for meetings of the Ethics Advisory Group, and Ethics Ambassadors.
As part of the Complex Systems Toolkit, supported by Quanser, we will be exploring assessing systems thinking through student-created AI simulations. This free to attend workshop will be delivered by Dr. Stuart Grey, and will demonstrate a new assessment approach, developed from his teaching at the University of Glasgow and shared through the EPC’s Complex Systems Toolkit.Students create and debug interactive AI simulations of complex sociotechnical scenarios, making their understanding of stakeholders, relationships, feedback and trade-offs visible through their design decisions and documented revisions.
Participants will try the activity themselves, examine what students’ work can reveal about their understanding, and explore how to set, support and mark this type of assessment in their own disciplines.
When and where
Date: 12 Nov 2026
Time: 12:30 – 13:30 GMT
Location: Online via Zoom
Attendees will gain:
Go to the event and register now!
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
The Engineering Professors’ Council is developing the second edition of its Enterprise Collaboration Toolkit, building on the highly successful first edition which brought together examples of effective collaboration between engineering academics and industry. Submissions close 30th November 2026.We are seeking short case studies from engineering academics across the UK that demonstrate how universities and industry partners can work together successfully to create impact for research, innovation, education, industry, graduates and society.
The environment for university–industry collaboration has changed significantly in recent years. Artificial intelligence, the growing importance of defence and security, global supply-chain disruption and manufacturing reshoring, climate change, energy security and decarbonisation, and shortages of engineering skills are all creating new challenges—and new opportunities—for collaboration.
We therefore particularly welcome case studies that demonstrate innovative, effective and transferable approaches to working with industry in this changing and challenging environment.
Your case study does not need to describe a large project or a major funding award. We are interested in the approach to collaboration, what made it work, the challenges encountered and the lessons that other engineering academics, especially early career, could learn from your experience.
We equally welcome honest accounts of collaborations that only partly succeeded, stalled or came to an end. What did not work, and why, is often the most useful lesson for others.
We welcome contributions from different types of universities (e.g. research-intensive, regional, specialist and newer institutions), from a wide range of engineering disciplines and different industrial sectors.
We are looking for examples that:
Case studies may involve a single company, multiple industry partners, SMEs, large corporations, public-sector organisations or industry networks.
Please submit your case study using the template provided below before 30th November 2026.
Contributions should be concise and accessible to engineering academics who may have little previous experience of working with industry.
The EPC’s Research, Innovation and Knowledge Transfer Committee will review submissions and select a balanced collection of case studies for inclusion in the second edition of the Enterprise Collaboration Toolkit. Selected contributors may be contacted to clarify or edit their submission before publication.
We particularly encourage contributions from early-career academics and colleagues who have developed successful collaborations without the benefit of large established research-industry networks.
The aim is to build a practical resource that helps engineering academics answer a simple question:
What can I learn from others that will help me build a successful relationship with industry?
We intend to publish the second edition of the Enterprise Collaboration Toolkit in the summer of 2027.
Each case study should be submitted under one primary category. Contributors may also identify one or two secondary categories where appropriate.
1. Building and developing industry partnerships
Examples include:
Key question: What did you do to establish and develop a productive longer-term relationship?
2. Collaborative research, innovation and R&D
Examples include:
Key question: How did the partnership turn academic expertise into research, innovation or value for industry?
3. Knowledge exchange and innovation ecosystems
Examples include:
Key question: What mechanisms and structures enabled knowledge and expertise to flow effectively between university and industry?
4. Industry-informed education, curriculum and student projects
Examples include:
Key question: How did industry collaboration improve engineering education and student learning?
5. Skills, employability and talent pipelines
Examples include:
Key question: How did the collaboration help address current or future engineering skills needs?
6. Responding to major industrial and societal challenges
This category is deliberately broad and should capture collaborations responding to major changes affecting engineering and industry.
Examples include:
Key question: How did university–industry collaboration help address a significant emerging challenge?
7. Impact, regional development and economic growth
Examples include:
Key question: How did collaboration create value beyond the individual university and company?
8. New models and approaches to university–industry collaboration
This category is intended for genuinely novel approaches that do not fit comfortably elsewhere. Examples include:
Key question: What was different and novel about the way the collaboration was organised, and why did it work?
9. Other
Use this category only where the case study does not fit meaningfully into any of the categories above.
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Embedding equity, diversity and inclusion is essential for success and capability strengthening, writes Stella Fowler, Policy and Research Director at the Engineering Professors’ Council, and Karen Grayson, Senior Manager at the Royal Academy of Engineering.
This guest blog explores why inclusive engineering outcomes are central to building a more innovative, ethical and representative engineering profession.
Read the full blog on the Royal Academy of Engineering website.
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Engineering has the power to shape the world we live in. But as societies become increasingly diverse, technical expertise alone is not enough. Future engineers must design solutions that are accessible, equitable and inclusive, ensuring innovation benefits everyone. Embedding inclusive design thinking into engineering education helps graduates work responsibly, creatively and fairly.
Many engineering educators did not receive formal training in fostering inclusive mindsets, or on how to embed inclusion into engineering practice, yet they are expected to prepare students for complex, real-world challenges. Without a focus on equity, diversity, and inclusion (EDI), engineering solutions risk unintentionally disadvantaging certain groups. Diverse perspectives, on the other hand, lead to stronger, more ethical, and adaptable outcomes.
The Inclusive Engineering Toolkit is being developed to address this need, curating valuable and practical resources and tools, that help educators embed inclusive principles in their teaching and equip students to become ethical, collaborative, and globally aware engineers.
Its development is guided by an Expert Working Group comprised of academic, industry, and professional organisation experts.
Please register your interest in developing a resource by completing this form by 11th July 2026.
If you have already registered an interest and we are expecting your submission, the deadline to submit first drafts is 8th August 2026. Submit your Inclusive Engineering Toolkit Contribution here. Co-authors should complete this form.
If you would like to become a reviewer for the toolkit (with structured review cycles running from late July to September 2026, and light input into October where needed), please complete this form (and tick ”Inclusive Engineering Toolkit”). We will take a flexible and iterative approach where possible. Where submissions are received earlier and reviewer capacity allows, review activity may begin ahead of the formal window to support workload distribution across the summer period.
These resources will fit into three categories:
To ensure accessibility and inclusivity in practice, contributors should also consider:
We recommend allowing around 15–20 minutes to review the Call for Contributions guidance and relevant brief(s) before preparing your submission. This will help ensure your contribution aligns with the toolkit aims and requirements.
We welcome suggestions of existing resources that support inclusive engineering education, practice and outcomes.
If you would like to suggest links to pages or online resources that we can add to the Inclusive Engineering Toolkit Resource Library, please submit this through the resource suggestion route by emailing Crystal Nwagboso.
If you would like your existing resource to be considered as a featured contribution within the toolkit, please review the Call for Contributions briefs and submit it through the relevant contribution route.
Please register your interest in developing a resource by completing this form by 11th July 2026.
If you have already registered an interest and we are expecting your submission, the deadline to submit first drafts is 8th August 2026. Submit your Inclusive Engineering Toolkit Contribution here. Co-authors should complete this form.
If you would like to become a reviewer for the toolkit (with structured review cycles running from late July to September 2026, and light input into October where needed), please complete this form (and tick ”Inclusive Engineering Toolkit”). We will take a flexible and iterative approach where possible. Where submissions are received earlier and reviewer capacity allows, review activity may begin ahead of the formal window to support workload distribution across the summer period.
In undertaking this work, contributors will become part of the growing community of educators who are helping to ensure that tomorrow’s engineering professionals have the inclusive engineering knowledge and skills that they need to provide a better future for us all. Contributors will be fully credited for their work on any relevant Toolkit materials, and will be acknowledged as authors should the resources be published in any form. Developing these resources will provide the chance to work with a dynamic, diverse and passionate group of people leading the way in expanding engineering teaching resources, and may help in professional development, such as preparing for promotion or fellowship. We are unable to offer payment for contributions at this time. However, if you would otherwise be interested in developing a resource but would not currently be able to commit the required time, please let us know by emailing Crystal Nwagboso and we would be happy to keep in touch with you about opportunities in a future phase of the project.
As the Toolkit develops, further updates on additional resources and activities will be shared with the community.
Those interested in contributing to future phases of the Inclusive Engineering Toolkit should fill out this form and we will be in touch.
Learn more about the Inclusive Engineering Toolkit, here.
Learn more about the members of the Inclusive Engineering Toolkit Expert Working Group, here.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
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We are seeking further contributions for the Complex Systems Toolkit. Please register your interest in developing a resource by 12th July 2026. Note: This deadline has been extended to 26th July.
In November 2025 the EPC, with support from Quanser, launched a new Complex Systems Toolkit, aimed at providing accessible, practical resources for embedding complex systems concepts into engineering education.
The Toolkit launched with an abundance of resources, allowing educators and industry professionals to dive into the ‘what’ and ‘how’ of complex systems with knowledge and guidance articles, discover ready-to-use teaching resources including case studies and other classroom activities, and hear directly from the creators and partners who helped shape the Toolkit with a well-attended launch webinar (now available to watch on demand).
These resources have been well used in their first six months, but we’re not stopping there. We want to add further resources, on topics that are emerging as being of vital importance to students as they graduate and seek work. The first of the topics that we want to cover is intelligent robotics.
Intelligent robotics, and the more recent applications to physical AI, generally refers to artificial intelligence systems that are embedded in and interact directly with the physical world, rather than operating purely in digital environments. This includes technologies like robots, autonomous vehicles, and drones that can perceive their surroundings through sensors, process that information using AI models, and take real-world actions. Unlike traditional software-based AI, intelligent robotics applications deal with real-time constraints, uncertainty, and complex environments, requiring tight integration between hardware (like sensors and actuators) and decision-making algorithms.
For engineering students, learning about intelligent robotics and physical AI workflows matters because it sits at the intersection of software, hardware, and real-world problem solving. It forces students to grapple with uncertainty, noisy sensor data, timing constraints, and safety considerations, which are unavoidable in real systems like robots or autonomous vehicles. That experience builds practical intuition about how algorithms behave outside ideal conditions. Engineers who understand this are better equipped to design systems that are robust, adaptive, and resilient. Industries are moving rapidly toward automation, robotics, and intelligent infrastructure, so familiarity with intelligent robotics and physical AI workflows opens doors in fields like manufacturing, healthcare technology, and transportation. It helps engineers think holistically: not just “does the code work?” but “does the system behave safely and effectively in the real world?”.
We are seeking experts in intelligent robotics, from academia, industry, and engineering organisations, to develop resources on this topic for publication in the Complex Systems Toolkit. These resources will inform, guide and aid educators to embed teaching on intelligent robotics into their engineering lessons, modules or courses.
We invite contributors to develop resources in three areas:
We’re also looking for experts in intelligent robotics and physical AI to join us as reviewers and working group members.
Resources should reference the topic’s relationship to complex systems and engineering education/graduate skills. We are particularly interested in resources that help engineering educators teach these topics effectively.
In undertaking this work, contributors will become part of the growing community of educators who are helping to ensure that tomorrow’s engineering professionals have the complex systems skills, knowledge, and attributes that they need to provide a better future for us all. Contributors will be fully credited for their work on any relevant Toolkit materials and will be acknowledged as authors should the resources be published in any form. Developing these resources will provide the chance to work with a dynamic, diverse and passionate group of people leading the way in expanding engineering teaching resources, and may help in professional development, such as preparing for promotion or fellowship.
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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.
Peter Martin, Director of Research & Development at Quanser, and co-chair of the Complex Systems Toolkit Working Group, reflects on the importance of engineers understanding complex systems when working in the field of intelligent robotics.
“In late 2024 I had the opportunity to join the EPC Complex Systems Toolkit team as co-chair of the working group. At the time I felt a little fraudulent, as the intricacies of complex systems thinking was new to me. I had brushed up against complex systems numerous times over the years as I had studied and worked in the world of robotics for over 20 years. However, I had never discovered the world of formal complex systems analysis. Looking back, this is a perfect validation for the need to create a toolkit to better prepare students for careers like mine. As I have learned more over the last 18 months about the tools and techniques that systems engineers employ to model and manage complexity, the critical value that these techniques offer engineers in the world of intelligent robotics has become obvious. As we hear often in the field of engineering lab equipment for the academic space, “I wish I’d had this when I was at university”.
The other reassuring aspect of my experience, for me, is that I’m not alone. A growing need for better approaches to managing complexity has emerged in industry over the last couple of decades as robotics and their governing systems have become increasingly integrated into society. This transition of robotics out of the structured environment of the factory floor and into direct contact with both the dynamic and unstructured world and the public, has introduced a high degree of non-linear predictability, complex interactions with multiple robotic agents, and emergent behaviours as the decision-making algorithms that dictate robotic behaviour adapt. All of these elements are central to the world of complex systems analysis.
At a high level, modern robotics systems no longer represent technical engineering challenges in the narrow, discipline-specific sense that engineers would traditionally have seen in higher education. They are complex adaptive systems that routinely demonstrate behaviours that emerge from interactions with their environment rather than being fully specified in advance. A robot navigating a hospital corridor, a swarm coordinating warehouse logistics, or a surgical assistant adjusting in real time to tissue variability represent challenges in undefined, non-linear, and largely unpredictable spaces. Students, and later robotics engineers who lack a complex systems vocabulary are essentially tasked with trying to understand emergence without the tools to describe it.
An example that I like to use is one that we encountered a couple of years ago: a team of mobile robots transporting parts around a manufacturing space. In many cases, the agents (ground robots, arms, etc.) in this scenario are programmed with independent control and decision-making code to govern their behaviour, with some overarching supervisory code to manage tasks and assignments. The ground robots would have algorithms to localise, path plan, navigate, and avoid obstacles while communicating with other complementary agents and central task management. However, as I have learned, complexity lies in the emergence of unexpected interactions between the agents and their environment. How they avoid each other and the environment while achieving their tasks is largely a complex non-linear system where conflicts can routinely delay or disrupt their operation. Introducing more sources of disruption such as humans, unstructured environments, weather conditions etc. only makes dealing with unpredictable scenarios more and more complicated using traditional techniques.
Luckily, many of the tools and techniques that are highlighted in the toolkit have direct applications to the challenges faced by engineers in the world of robotics. Causal Loop Diagrams (CLDs) are an excellent way to model the feedback dynamics that are at play in adaptive control systems. When a robot’s perception system updates its world model based on changes in what the sensors can perceive, that leads to changes in its action policy that when executed create a feedback loop. These diagrams are a great way to visualise and analyse these loops. Agent-Based Modelling (ABM) is directly relevant to the scenario I described above where swarms of robot must be coordinated or manage human-robot interaction scenarios. Using these simulation tools, engineers can test and manage emergent fleet behaviour without hardware. If things do go sideways, Fault Tree Analysis is a common approach to mapping causes and evaluating data to help develop robots that work in safety-critical applications. Finally, for long-term operations such as field robotics missions, Systems Dynamics Modelling can be a useful tool for predicting and managing a robot’s resource consumption (battery, compute, bandwidth) depending on the required task performance over time.
In addition to these considerations, there is a whole world of network modelling and the management of behaviour stemming from machine learning and applied AI algorithms that also overlaps quite closely with complex systems. Engineers that understand emergence, feedback loops, and attractors are far better equipped to reason about why a robot does something unexpected, than students who only have a component-driven technical understanding of the behaviour of an intelligent robot. Beyond the decisions, at an actual component level there are critical decisions that need to be made for efficient deployment of physical and edge AI algorithms. What data is processed locally and what goes to the cloud, when models are updated and how decision making is distributed across a robot swarm are exactly the kind of questions that systems thinking trains engineers to answer. Systems tools are ready to help, including influence diagrams to manage information exchange and action planning.
Overall, the field of complex systems introduces a set of tools, techniques, and mental models that are increasingly essential to robotics engineers that seek to prepare their agents to be effective in performing complicated tasks in increasingly complex systems.”
Intelligent robotics, and the more recent applications to physical AI, generally refers to artificial intelligence systems that are embedded in and interact directly with the physical world, rather than operating purely in digital environments. For engineering students, learning about intelligent robotics and physical AI workflows matters because it sits at the intersection of software, hardware, and real-world problem solving. It helps engineers think holistically: not just “does the code work?” but “does the system behave safely and effectively in the real world?”.
We are seeking experts in intelligent robotics, from academia, industry, and engineering organisations, to develop resources on this topic for publication in the Toolkit. These resources will inform, guide and aid educators to embed teaching on intelligent robotics into their engineering lessons, modules or courses.
This post is also available here.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.

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We’ll keep you updated on the latest events associated with the Complex Systems Toolkit.
2026
2025