You can now view the recording of the ACE-Lab and agentic engineering workflows webinar, as part of the Complex Systems Toolkit, supported by Quanser. The webinar was live on Monday 29th June 2026, 11am-1pm BST.
This free webinar introduces practical engineering workflows, from requirements capture through to verification and validation. These concepts are demonstrated using the ACE-Lab, a low-cost, hands-on engineering learning platform, alongside MATLAB and Simulink to illustrate key stages of the workflow. The webinar also explores the emerging role of agents in engineering workflows. Through practical examples and demonstrations, it shows how agent-enabled approaches can support engineers in solving problems more effectively. During this webinar we also launched a new call providing you with an opportunity for your content to be featured in the Complex Systems Toolkit.
During this webinar we also launched a new call for contributions, providing you with an opportunity for your content to be featured in the Complex Systems Toolkit.
For more about the webinar, including the event programme and speaker bios, click here.
To enable closed captions on Vimeo, click the CC button in the video player.
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.
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.
What and why?
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?”.
Contributors sought to develop resources on Intelligent Robotics for inclusion in the toolkit
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:
Knowledge articles: These are resources that users can access to improve their knowledge or find more information. These are intended to provide theoretical and practical background on intelligent robotics concepts and tools such as modelling or decision-making approaches. While guidance articles focus on “how”, knowledge articles focus on “what”.
Guidance articles: These are resources that users can access to learn how to do something. These are intended to provide practical advice on subjects such as how to explain intelligent robotics to students, or how to assess for skills and competencies in this area. While knowledge articles focus on “what”, guidance articles should focus on “how”.
Teaching activities: These are resources that users can access to help them know what to integrate and implement. These include use cases/case studies which provide examples of intelligent robotics which can be directly utilised in teaching with the suggested tools, as well as other classroom activities such as coursework, project briefs, lesson plans, demonstration simulations, or other exercises.
We’re also looking for experts in intelligent robotics and physical AI to join us as reviewers and working group members.
We are seeking content on the following topics
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.
Robotics and autonomous systems
Human-robot interaction
Swarm systems and distributed intelligence
Edge AI and embedded intelligence
Cyber-physical systems
Simulation and digital twins
Safety, resilience, and uncertainty
Systems thinking for Intelligent Robotics or Physical AI
Teaching approaches and assessment methods
Read more about the specific content we are looking for (click on the arrows to expand the sections)
Submit a knowledge article
Submit a knowledge article
As well as choosing a topic, you will need to choose an angle for your resource.
For knowledge articles. contributors might consider one of the following:
What it is: explaining the topic and its relation to complex systems.
Why educators should teach it / students should learn it.
Why it should be integrated into engineering education.
An angle of your own choosing.
These articles should connect the why (why must teaching about the topic be present in engineering education?) to the how (how can this be done efficiently and effectively?). Through these tools, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems concepts and intelligent robotics topics into their engineering teaching.
Step 1: Read the guidance for submitting a knowledge article
Research:
Knowledge articles are resources that users can access to improve their knowledge or find more information. These are intended to provide theoretical and practical background on complex systems concepts and tools such as modelling or decision-making approaches. While guidance articles focus on “how”, knowledge articles focus on “what”.
Knowledge articles are meant to be overviews that a reader with no prior knowledge of the topic could refer to in order to develop a baseline understanding and learn where to look for additional information (they can reference other sources). They should be understandable to students as well: imagine that an educator might excerpt content from the article to provide their students context on a project or learning activity.
They should be approximately 500-1000 words (although they can be more in depth if necessary) and reference relevant online open-source resources.
Overview:
The articles are meant to be able to stand on their own as a piece of knowledge on a topic; they are also meant to work alongside other articles so that taken together they form a sort of complex systems in engineering handbook.
Purpose:
Each article should inform, explain, and provide knowledge on the topic. Put yourself in the perspective of an engineering educator who is new to the topic.
Content:
The content of the article should be organised and well developed. That is, it should be presented in a logical way and thoroughly explained.
References and resources:
Where additional explanation could be given, it might point to other resources, and where information is presented from another source, it needs to be properly referenced using Harvard referencing.
Format:
Knowledge articles should follow this format:
Premise;
Body of article, divided up into headed sections as necessary;
Knowledge articles should be submitted in Word file format (.doc or .docx).
Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.
Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.
To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.
These articles should also connect the why (why must teaching about this topic be present in engineering education?) to the how (how can this be done efficiently and effectively?). Through these tools, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems concepts and intelligent robotics topics into their engineering teaching.
Step 1: Read the guidance for submitting a guidance article
Research:
Guidance articles are resources that users can access to learn how to do something. These are intended to provide practical advice on subjects such as how to explain complex systems to students, or how to assess for skills and competencies in complex systems. While knowledge articles focus on “what”, guidance articles should focus on “how.”
Guidance articles aim to help situate our teaching resources in an educational context and to signpost to additional research and resources on complex systems theory and tools.
They should be approximately 500-1000 words (although they can be more in depth if necessary) and reference relevant online open-source resources.
Overview:
Guidance articles are meant to be able to stand on their own as a piece of guidance on a topic; they are also meant to work alongside other articles so that taken together they form a sort of complex systems in engineering handbook.
Purpose:
Each article should inform, explain, and provide guidance on the topic. Put yourself in the perspective of an engineering educator who is new to the topic.
Content:
The content of the article should be organised and well developed. That is, it should be presented in a logical way and thoroughly explained.
References and resources:
Where additional explanation could be given, it might point to other resources, and where information is presented from another source, it needs to be properly referenced using Harvard referencing.
Format:
Guidance articles should follow this format:
Premise;
Body of article, divided up into headed sections as necessary;
Guidance articles should be submitted in Word file format (.doc or .docx).
Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.
Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.
To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.
As well as choosing a topic, you will need to choose an angle for your resource.
For activities, contributors might consider one of the following:
Case studies that, through a real-world situation, illustrate the topic and its relation to complex systems, use cases for the tools that can be used to model / simulate this, techniques that promote development and use of systems architecture, and effects such as trade-offs, emergent properties, impacts, or unintended consequences. Case studies could also reference the implications for risk, security, ethics, sustainability, teamwork, and communication.
Demonstrator simulations that provide examples of how systems can be modelled.
This could include:
Examples of how the topic relates complex systems
Interactive examples showing how well-intentioned action can lead to failure
Interactive examples showing the best approaches to handling complexity
Teaching/learning activities, coursework, project briefs, lesson plans, modelling or simulation exercise/activities, technical content related to complex systems, worksheets, slides, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other teaching materials.
An angle of your own choosing.
These resources should promote active learning pedagogies and real-world teaching methods by showing how complex systems teaching can be embedded within technical problems and engineering practice. Through these resources, we aim to help upskill UK engineering educators so that they feel capable of and confident in integrating complex systems into their engineering teaching.
Step 1: Read the guidance for submitting a teaching activity/resource
Research:
Teaching activities are resources that users can access to help them know what to integrate and implement. These include use cases/case studies which provide examples of complex systems topics which can be directly utilised in teaching with the suggested tools, as well as other classroom activities such as coursework, project briefs, lesson plans, simulation exercises, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other exercises.
Before you begin, you should review existing Complex Systems Toolkit teaching resources, since we hope that contributions will be fairly consistent in length, style, tone, format and approach. Remember that the audience for these resources is educators seeking to embed complex systems topics within their engineering teaching.
Step 1a: Guidance for submitting a case study
Case studies present real-world scenarios that can be used in teaching about complex systems topics in engineering. They provide students with opportunities to explore complex systems tools, and trade-offs, in authentic contexts, and reflect on decisions made about them.
They are usually based on a real example, although fictionalised cases are acceptable when they are grounded in realistic detail. Case studies should enable students to identify or interpret key features of complex systems topics (feedback loops, interdependence or emergent behaviour) and apply relevant tools or frameworks to make sense of the situation.
Case studies will vary in length depending on scope and resource, but many are around 1500-2000 words. They should reference relevant online open-source resources.
The case study should be presented as a narrative about a complex systems issue in engineering.
Narrative strength: the case should be clearly structured with a compelling and coherent story.
System complexity: it should explore interdependencies, multiple stakeholders and/or competing goals.
Tool integration: systems tools should be mentioned or incorporated (e.g. soft systems methodology, SysML, Agent-based modelling etc).
Activities and Resources: there should be questions, prompts or teaching activities to guide discussion or classroom use.
Authenticity:
Case studies are most effective when they feel like they are realistic, with characters that you can identify or empathise with, and with situations that do not feel fake or staged. Giving characters names and backgrounds, including emotional responses, and referencing real-life experiences help to increase authenticity.
Complexity of issue:
Many cases are either overly complicated so that they become overwhelming, or so straightforward that they can be “solved” quickly. A good strategy is to try to develop multiple dimensions of a case, but not too many that it becomes unwieldy. Additionally, complexity can be added through different parts of the case so that instructors can choose a simpler or more complicated version depending on what they need in their educational context.
Activities and resources:
You should provide a variety of suggestions for discussion points and activities to engage learners, as well as a list of reliable, authoritative open-source online resources, to both help educators prepare and to enhance students’ learning. Where information is presented from another source, it needs to be properly referenced using Harvard referencing.
Educational level and assessment:
Educational level: When writing your case study, you should consider which level it is aimed at. A Beginner level case is aimed at learners who have not had much experience in engaging with this complex systems topic or problem, and usually focuses on only one or two dimensions of a challenge. An Advanced-level case is aimed at learners who have had previous practice in engaging with this complex systems topic or problem, and often addresses multiple challenges. An Intermediate case is somewhere in between.
Assessment: If possible, suggest assessment opportunities for activities within the case, such as marking rubrics or example answers.
Format:
The case study should follow the following format:
Teaching notes (with learning objectives, time needed, materials): This is an overview of the case and its dilemma, and how it relates to AHEP4 and INCOSE competencies.
Learning and teaching resources: A list of reliable, authoritative, open-source online resources that relate to the case and its dilemma. These can be from a variety of sources, such as academic institutions, journals, news websites, business, and so on. We suggest a minimum of five sources that help to provide context to the case and its dilemmas.
Summary of system or context.
Narrative of the case (presenting the complexity).
Questions and activities. This is where you provide suggestions for discussions and activities related to the case and the dilemma.
Further discussion or challenge (optional). Some case studies are sufficiently complex at one dilemma, but if the case requires it you can provide further parts (up to a maximum of three).
Step 1b: Read the guidance for submitting a different teaching activity
Purpose & outcomes:
Teaching activities/tools are intended to support educators’ ability to apply and embed complex systems concepts and topics within their engineering teaching.
Educators need to quickly and easily find help with:
Adapting and integrating existing complex systems resources to their disciplinary context.
Implementing new and different pedagogies that support complex systems learning.
Structuring lessons, modules, and programmes so that complex systems skills and outcomes are central themes.
Thus, these teaching activities/tools will provide crucial guidance for those who may be teaching complex systems related material for the first time, or who are looking for new and different ways to integrate complex systems concepts or topics into their teaching.
Teaching activities/tools may take the form of learning activities, coursework, project briefs, lesson plans, modelling or simulation exercise/activities, technical content related to complex systems, worksheets, slides, robotics labs, swarm behaviour activities, system mapping exercises, hardware-in-the-loop demonstrations, digital twin exercises, or other similar teaching materials.
Imagine that you are an engineering educator who is new to teaching complex systems concepts or topics. You turn to this teaching tool to help you apply and embed these in your module.
Does this resource help introduce or develop concepts related to complex systems or systems thinking so that learners can engage with these topics in the context of engineering?
If not, what is needed to make this possible?
Presentation and clarity:
Depending on the resource, you may choose to provide worksheets, slides, problem sets, narrative prompts, etc.
Is the resource explained in such a way that someone new to teaching complex systems could understand how to use it?
Is the material clearly introduced and described?
Resources and guidance:
Depending on the topic, educators may need additional resources or guidance to support their use of the material. For instance, background information may be required or a technical topic explained.
Have you provided sufficient material so that educators can easily employ the resource?
The teaching activity/tool should follow this format:
Overview:
Short description of what the resource is and what it aims to do.
States how it is related to complex systems or systems thinking topic(s), referring to external content such as INCOSE Competencies and AHEP 4.
Provides an overview of the activity, suggesting how it might be implemented and in what contexts, how long it might take, and any other relevant delivery information.
Details any specific materials or software required for the activity, as well as any modelling or simulation tools to be used.
Lists any learning and teaching resources recommended in order to undertake the activity, including suggested pre-reading or other references.
Explains the activity in as much detail as is required (this will vary depending on the type of material the resource addresses.)
If relevant, provides assessment guidance–marking rubrics, sample answers, etc.
Step 2b: Before you submit, review this checklist:
Does this resource help introduce or develop concepts/topics related to complex systems or systems thinking so that learners can engage with these topics in the context of engineering?
Is the resource explained in such a way that someone new to teaching complex systems could understand how to use it?
Is the material clearly introduced and described?
Have you provided sufficient material so that educators can easily employ the resource?
Step 3: Submitting your teaching activity/resource
Teaching resources should be submitted in Word file format (.doc or .docx).
Also submit any additional resources such as spreadsheets, handouts etc., and ensure that they are in an editable format. Please clarify where in the resource these should be embedded or linked.
Any corresponding images should be submitted in either .jpeg, .jpg or .png format. We need these to be uploaded separately from the Word file, as we will be embedding them in a web page. Please ensure that they are of high resolution and adequate size (we suggest a minimum of 800 pixels wide); that you have the right or permission to use them (bearing in mind they will be published under a Creative Commons license); and that you have added any permissions, sources, credits or other details for them in the body of the document that you are submitting.
To ensure that everyone can use and adapt the Toolkit resources in a way that best fits their teaching or purpose, this work will be licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Under this licence users are free to share and adapt this material, under terms that they must give appropriate credit and attribution to the original material and indicate if any changes are made.
Please register your interest in developing a resource by12th July 2026. NOTE:This deadline has been extended to 26th July. Apply here.
If you have already registered an interest and we are expecting your submission, the deadline to submit first drafts is 31st August 2026. NOTE: This deadline may be extended for those registering interest after the initial 12th July deadline. Submit your Complex Systems Toolkit Contribution here. Co-authors should complete this form.
We are also seeking experts on intelligent robotics/physical AIto join the Complex Systems Toolkit Working Group. Please apply here.
We are also seeking experts on intelligent robotics/physical AI to review resources for the toolkit. Please apply here.
If you would like to suggest links to pages or online resources that we can add to our database of engineering education resources for complex systems teaching, please email Wendy Attwell.
Additional information
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.
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.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Systems Modelling and Analysis, Integration, and Technical Leadership INCOSE competencies.
AHEP4 mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems).
Educational level: Beginner; Intermediate.
Learning and teaching notes:
Modern engineering is increasingly digital, interconnected, and system oriented. To prepare students for this evolving landscape, the Automatic Control Engineering (ACE) Model offers a systems-driven, application-focused framework for practical control engineering education. Developed through a MathWorks-funded project launched in the summer of 2025, the ACE-Model unifies three complementary components that together cultivate systems thinking and model-based systems engineering competence:
ACE-Box: The Toolkit
ACE-CORE: The Processes
ACE-Apply: Real-World Application
Learners have the opportunity to:
Engage with the entire ACE-CORE (Comprehend – Operate – Refine – Engineer) framework.
Experience a welcoming and accessible introduction to ACE, without an early overemphasis on mathematics. This stands in contrast to the traditional approach, where topics such as Laplace transforms are introduced early on, often creating unnecessary barriers to engagement (Abou-Hayt and Dahl, 2023).
Develop systems awareness and motivation.
Develop confidence, engagement, and curiosity.
Gain the technical knowledge and systems integration mindset required to thrive in the complex, adaptive landscape of digital engineering.
Teachers have the opportunity to:
Introduce control theory topics in a way that addresses the concern of students finding it difficult to link abstract control theory with the world of control engineering practice(Rossiter, 2022; Badau, et al., 2024).
Introduce industry-standard systems processes such as the V-diagram and model-based design workflows.
Progressively link theory to practice.
Support AHEP4 expectations for developing graduates who can apply integrated systems approaches to solving complex problems and the INCOSE Systems Engineering Competency areas of systems thinking, integration, and technical leadership.
What does the ACE-Model consist of?:
Figure 1:The ACE-Model: Integrating the Toolkit (ACE-Box), with the Processes (ACE-CORE), to Lead to the Real-Life Application (ACE-Apply) to build a progressive mastery in Automatic Control Engineering (ACE).
The ACE-Model is closely aligned with Bloom’s Taxonomy, see (Anderson and Krathwohl, 2001)and Figure 2(a) providing a structured pathway for students to progress through the cognitive hierarchy, while developing capabilities across multiple levels of system abstraction. Figure 2(b) offers a schematic view of the three stages of the ACE-Model, as introduced in Figure 1. An initial overview of the ACE-Model is presented here, with further details provided in the following sections.
The ACE-Box is a portable, self-contained hardware tool that brings ACE to life beyond the traditional costly, full-scale laboratories. All that is required is a laptop and the ACE-Box. Designed to support the ACE-CORE methodology, ACE-Box can be set up on a desk, in a classroom, or even at home. MATLAB and Simulink serve as the primary platforms for model-based design, enabling system modelling, control system development, and the deployment of control algorithms to physical hardware (e.g. Arduino Uno) through code generation tools.
ACE-CORE guides learners through successive levels of Bloom’s framework:
Comprehend aligns with Remember and Understand on the Bloom Taxonomy, enabling students to grasp the applications of control engineering before advancing.
Operate corresponds to Apply on the Bloom Taxonomy, as students engage directly with control systems, supported by Stage 2, making use of the ACE-Box (hardware or virtual).
Refine maps to both Analyse and Evaluate on the Bloom Taxonomy, where learners diagnose performance, compare outcomes, and adapt solutions to meet stakeholder requirements.
Engineer extends this process to system-level design and synthesis, making use of modelling and simulation tools such as MATLAB and Simulink. At this stage, students revisit the full cycle (Remember through to Evaluate), but at a higher level of integration with the use of control theory, again supported by the ACE-Box.
At each stage of CORE, learners move from recognising system components to synthesising complex interactions, mirroring the systems engineering lifecycle from requirement capture through verification and validation. This alignment supports AHEP4’s emphasis on analytical and problem-solving competence and INCOSE’s System Definition and Integration competencies.
Finally, learners progress to Create, the highest stage of Bloom’s Taxonomy, by applying their knowledge to design complete control systems for real-world applications such as drones, vehicles, and automation systems. In this way, the ACE-Model scaffolds learning in parallel with Bloom’s progression, from foundational comprehension to advanced problem-solving, design and innovation.
Together, these three pillars form a cohesive learning ecosystem: the toolkit, the process, and the application.
Figure 2:Bloom’s Taxonomy (Anderson and Krathwohl, 2001)(a) and the ACE-Model Three Stages (b).
Collaborative community:
The ACE-Model ‘sits’ within the ACE-Lab, a collaborative community of academics and industry professionals committed to developing, validating, and disseminating open-access systems education resources. The ACE-Lab approach embodies complex adaptive systems principles, where the community evolves through continuous feedback, iteration, and co-design. Membership to the ACE-Lab is open to anyone who shares our vision of advancing control engineering teaching tools and practices. Through this approach, the ACE-Model equips graduates with the knowledge and hands-on skills required to excel in modern ACE careers. Find out more about the ACE-Lab through the following website:www.ace-lab.co.uk
As an evolving community, ACE-Lab continually expands its open-access content through the active contributions of its members. New materials are regularly developed and shared, ensuring the resources remain current and relevant. Through this dynamic, collaborative approach, embodied in the ACE-Model, students not only gain technical knowledge but also develop the capacity to understand, navigate, and work effectively with complex, interconnected engineering systems.
ACE-Box: The toolkit:
The ACE-Box is based on the early development work of Control-Lab-in-a-Box(Pickering, 2023; 2025). CLB integrates sensors, actuators, and microcontroller to allow students to experience dynamic behaviour, and feedback control.
For now, two ACE-Box kits have been developed:
1. Base and sense
2. Actuate
The ACE-Box (base and sense) is illustrated in Figure 3, with the 15 key components labelled, along with an exploded view of the main parts in Figure 4. The ACE-Boxesintegrate the essential microcontrollers, electronics, sensors, and actuators needed to design, implement, and test elements of digital control algorithm development, e.g. control algorithms in real time. It bridges the gap between theory and practice, allowing learners to see how abstract concepts behave in physical systems. The ACE-Box is also available as an open-access resource, with laboratory exercises included, with details provided later in this article. The ACE-Box (labelled (1) in Figure 3) and the tray (labelled (2) in Figure 3) are manufactured using 3D printing, with the necessary files available on the project website referenced above. A list of the required components and their sources is also provided on the project website, corresponding to labels (3) to (15) in Figure 3. Due to the open-source design of ACE-Lab, the library of exercises will continue to expand, supported by contributions from both academia and industry. The ACE-Box (Actuate) is illustrated in Figure 5, with the key actuator components detailed in (a), along with some typical lab set-ups (b, c and d). Figure 6 illustrates both the ACE-Box (Base + Sense) and also ACE-Box (Actuate).
Figure 3:The ACE-Box (Base and Sense).
Figure 4: Assemble of the 3D Printed ACE-Box (Base and Sense).
Figure 5:The ACE-Box (Actuate).
Figure 6:ACE-Box (Actuate) Alongside the ACE-Box (Base + Sense).
ACE-CORE: The methodology:
ACE-CORE is a four-step framework designed to scaffold learning from components to system-of-systems understanding:
Comprehend: Recognise the interdependencies between components within a feedback control system.
Operate: Discover how to operate a control system from understanding system requirements to testing and validation.
Refine: Diagnose, analyse, and optimise performance using feedback principles; students apply system verification and validation approaches.
Engineer: Apply mathematics and modelling to synthesise control algorithms and architectures that achieve desired system behaviours.
The methodology explicitly develops systems thinking, and integrationcompetencies, core to both AHEP4 and INCOSE frameworks.
ACE-CORE is intentionally designed to offer a scaffolded learning experience, allowing students to build confidence step by step as they deepen their understanding. Due to its flexible structure, students can also follow a completely practical route, i.e. avoiding the modelling and simulation. The emphasis is not on rote memorisation of theory, but on progression through understanding the fundamentals of control engineering, e.g. the components that form a feedback control system.These routes enable learners to apply concepts in practical control engineering contexts and develop genuine competence.
ACE-Apply: Real-world application:
ACE-Apply is the project stage, where the skills and knowledge gained from ACE-Box and ACE-CORE are consolidated by tackling authentic challenges aligned with the expectations of industry and professional engineers, see Figure 2(b). At this stage, learners prove their mastery by addressing engineering application problems that reflect the standards of industry practice. The focus is on:
Applying the ACE-CORE methodology to practical control application challenges across domains such as robotics, automotive systems, drones, and industrial automation.
Bridging theory, simulation, and hardware with confidence and agility using industry standard tools and processes.
This stage reinforces AHEP4 Themes 3 and 5, particularly:
Applying integrated systems approaches to complex, real-world problems.
Managing system lifecycle activities including requirements capture, design, testing, and validation.
It also strengthens INCOSE competencies in System Realisation, Integration, and Technical Project Management, encouraging students to act as systems integrators capable of managing interfaces and dependencies across mechanical, electrical, and software domains.
By bridging theory, simulation, and hardware using industry-standard digital tools, ACE-Apply nurtures the ability to navigate complex adaptive systems, anticipate emergent behaviour, and work collaboratively within multidisciplinary engineering ecosystems.
ACE-Box activities:
Upon visiting the ACE-Lab website (www.ace-lab.co.uk), under the tab ‘ACE-Box’, the following tabs exist (with the links provided):
The “What is the ACE-Box?” page introduces educators and students to the ACE-Box platform, outlining its purpose, key features, and practical considerations such as sourcing components and 3D-printing enclosure parts.
The “Prior Exercises” page provides essential onboarding material designed to help users become familiar with MATLAB and Simulink. This includes links to the relevant OnRamp courses, guidance on installing the required software packages, and short tutorial videos that introduce the MATLAB and Simulink graphical user interfaces (GUIs).
The “Base + Sense” section contains a set of introductory tutorial exercises that use the ACE-Box (Base + Sense configuration). These activities help users get started with Simulink code generation for the Arduino Uno, while working with a range of basic sensors and electronic components.
Finally, the “Base + Sense + Actuate” section builds on the previous material by introducing actuation hardware. Using both the Base + Sense and Actuate modules, students and educators learn how to interface with and control devices such as DC motors, servomotors, and stepper motors. This section is designed to familiarise users with actuator integration and reinforce practical control engineering workflows.
Example use of ACE-Box (Base + Sense):
To demonstrate the use of the ACE-Box (Base + Sense), an introductory activity is provided, i.e. the on-off blinking of an LED. Prior to this activity, through ACE-CORE, students should receive a short introduction to microcontrollers covering key concepts such as digital input/output pins, analogue pins, and pulse-width modulation (PWM). Once students are familiar with these fundamentals, they progress to the initial exercise detailed here, which is aligned with defined learning outcomes.
Since MATLAB and Simulink are the primary software tools used with the ACE-Box, students are first guided through installing the Simulink Support Package for Arduino Hardware. After the hardware and software setup is complete, they assemble a simple circuit, see Figure 7(a), and configure a Simulink model for the first exercise, see Figure 6(b). This initial activity requires students to control the state of a digital output pin on the Arduino, switching it on and off. The Simulink model, provided in Figure 7(b), enables students to quickly build the exercise using a visual programming approach. To run the activity, they follow a sequence of steps that includes code generation, which compiles the Simulink model into embedded C code and deploys it onto the Arduino Uno microcontroller. Once completed, the LED connected to the circuit blinks on and off according to the settings of the Simulink pulse generator. A visual of the complete set-up for this initial exercise can be found in Figure 8. At this stage, students are encouraged to experiment with the pulse generator parameters in real-time, observing how changes to the signal properties immediately affect the LED’s behaviour. Scopes can also be used (see Figure 7(b)) to visualise the pulse generator’s square-wave output, including its amplitude, period, and pulse width. This hands-on interaction reinforces the link between the initial set-up and hardware implementation while deepening their understanding of microcontrollers.
Figure 7:LED Simple Circuit (a) and Simulink for Code Generation for the on-off Blinking of an LED.
Figure 8:LED Simple Circuit Set-Up using Simulink for Code Generation for on-off Blinking of an LED.
The initial exercise is designed to familiarise students with the ACE-Box and the use of Simulink’s code generation tools. This type of activity is typical for introducing students to a new software and hardware environment. The next exercise involves using pulse width modulation (PWM) to vary the brightness of the LED. This exercise involves using additional blocks in Simulink, see Figure 8, where multiple scopes are used to visualise the signals in real-time. Once students understand the fundamental building blocks of Simulink, they can quickly progress to developing feedback control systems that meet a variety of application requirements. In the authors’ view, student familiarity with Simulink makes it a more accessible platform for designing advanced control algorithms, particularly when working with sub-systems.
Figure 9:LED Simple Circuit Set-Up using Simulink for Code Generation Varying Brightness of an LED using Pulse Width Modulation (PWM).
Building on this foundation, a wide range of laboratory exercises can be developed using the electronic components involved in ACE-Box (Base + Sense), as illustrated in Figure 3, with the option to expand further by incorporating additional components. Examples of extended exercises include:
Analogue sensing and calibration with a temperature sensor
LDR characterisation and linearisation using a voltage divider
Analogue sensing and calibration with a potentiometer sensor
Digital sensing using an ultrasonic sensor
Distance-reactive LED control with proportional feedback (human-in-the-loop plant)
Closed-loop brightness control using LDR feedback and LED PWM
LED–LDR plant control experiments
In addition to sensing activities, the ACE-Box (Actuate) provides four actuators: a servomotor, a DC motor with encoder, a stepper motor, and a DC motor fan. This unit can be used independently or in combination with the Base and Sense ACE-Box to enable more advanced control experiments, such as DC motor speed control or motor control based on light intensity measurements from an LDR.
The flexibility of the ACE-Box system ensures that the number of possible exercises is effectively unlimited, as new experiments can be designed by combining existing sensors and actuators or by integrating additional measurement devices. This also allows unique coursework assignments to be created.
Summary:
The ACE-Model provides a systemic and holistic framework for practical control engineering education that:
Fosters systems thinking and model-based design literacy aligned with INCOSE and AHEP4 competencies.
Connects abstract control theory to complex, real-world systems through accessible hands-on experiences.
Encourages progression from component-level comprehension to system integration.
Builds confidence and motivation through authentic engagement with digital and physical systems, preparing graduates for engineering practice in a complex, interconnected world.
Acknowledgements:
Dr James E. Pickering gratefully acknowledges the support from MathWorks, whose funding made this project possible. He also extends his sincere thanks to Hari Sudeskkumar for his exceptional engineering design contributions and 3D-printing work. The authors would like to thank the Project Advisory Group (PAG) for their valuable guidance throughout the development of this work.
References:
Abou-Hayt, I. and Dahl, B., 2023. A Critical Look at the Laplace Transform Method in Engineering Education. IEEE Transactions on Education, 67(4), pp.542-549.
Anderson, L.W. and Krathwohl, D.R., 2001. A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives: complete edition. Addison Wesley Longman, Inc..
Badau, N.E., Popescu, T.M., Mihai, M., Dulf, E.H. and Muresan, C.I., 2024. Bridging the gap between control theory and practice: From simple controller design to a practical microcontroller implementation. IFAC-PapersOnLine, 58(26), pp.124-129.
Pickering, J.E., 2023. Control-Lab-in-a-Box: Bridging the Gap between Control Theory and Engineering Practice. In UK and Ireland Engineering Education Research Network Conference Proceedings 2023.
Pickering, J.E., 2025. Leveraging Control-Lab-in-Box (CLB) to Teach Control Engineering on Future Vehicle Technologies MSc. IFAC-PapersOnLine, 59(7), pp.31-35.
Rossiter, J.A., 2022. Future trends for a first course in control engineering. Frontiers in Control Engineering, 3, p.956665.
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.
Downloads: A PDF of this resource will be available soon.
Who is this article for?: Thisarticle should be read by educators at all levels in higher education who are seekingto provide students with an overall perspective on complex systems in engineering.
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).
Engineering systems today are increasingly complex, interconnected, and adaptive. To understand and manage them effectively, engineers must move beyond reductionist thinking where systems are broken into isolated parts and adopt systems thinking, which views systems as wholes made up of interacting components.
At the heart of this perspective lies emergence, a defining characteristic of complex systems. Emergence refers to properties or behaviours that arise from interactions among components but cannot be predicted or understood by examining those components in isolation. Appreciating emergence helps engineers anticipate how individual design decisions can produce system-level outcomes, sometimes beneficial, sometimes negative and unintended.
This article introduces the concept of emergence as one key characteristic of complex systems, situates it within systems thinking, and provides practical guidance for recognising and managing emergent behaviours in engineering practice.
1. What is a system?:
A system can be defined as “a set of interconnected elements organised to achieve a purpose” (Meadows, 2008). Systems possess structure (components), relationships (interactions), and purpose (function). Engineering systems such as aircraft, power grids, transport networks, or data infrastructures are composed of numerous subsystems that depend on each other.
Crucially, systems thinking emphasises interdependence and feedback. The behaviour of the whole cannot be fully explained by the behaviour of the parts alone. Properties such as resilience, adaptability, and emergence result from interactions within the system’s structure and environment. Recognising these relationships is essential to understanding how system-level behaviours arise.
Emergence describes the appearance of new patterns, properties, or behaviours at the system level that are not present in individual components. These properties are often irreducible: they cannot be explained solely by analysing each part separately (Holland, 2014).
Researchers distinguish between:
Weak emergence – behaviours that are theoretically predictable if all component interactions were known but are practically impossible to compute due to complexity (e.g. traffic flow patterns).
Strong emergence – properties that are fundamentally novel and irreducible to component-level descriptions (e.g., consciousness in biological systems).
In engineering, most emergent behaviours are weakly emergent: complex yet explainable with sufficient data and computational tools such as agent-based modelling or system dynamics.
A key caveat is that emergence depends on perspective and system boundaries. What seems emergent at one scale (e.g., the stability of a power grid) might appear straightforward when viewed at another. Therefore, engineers must define boundaries and assumptions clearly when analysing emergence.
3. Why emergence matters in engineering:
Emergence shapes how engineering systems behave, evolve, and sometimes fail. It can produce both desired outcomes (like adaptability or resilience) and undesired ones (like instability or cascading failure).
Understanding emergence enables engineers to:
anticipate how local interactions scale up to global system behaviour;
design feedback loops and architectures that promote stability; and
identify potential points for intervention when emergent behaviour becomes undesirable.
For instance, in cyber-physical systems, emergent coordination can enhance efficiency, but it may also create unpredictable vulnerabilities if feedback loops reinforce errors. Engineers therefore must not only observe emergence but learn how to influence it through design and governance.
4. Recognising and managing emergent behaviour:
Recognising emergence
Engineers can identify emergence by looking for:
System-level patterns that do not trace directly to any single component (e.g. global traffic flow or collective oscillations in a power grid).
Unexpected behaviours, such as new failure modes or self-organising phenomena.
Scale-dependent properties, where behaviour changes qualitatively as the system grows or interacts with its environment.
Adaptive or learning responses, where the system adjusts without explicit central control.
Intervening in emergent systems
Not all emergence is beneficial. Engineers often need to mitigate unwanted emergent behaviours such as instability or inefficiency while reinforcing desirable ones. Effective approaches include:
Redesigning interactions rather than individual components, focusing on how feedback and connectivity shape outcomes.
Introducing constraints or buffers to dampen runaway feedback loops.
Enhancing diversity and modularity so subsystems can adapt locally without propagating failures globally.
Monitoring system states continuously, using sensors, data analytics, or digital twins to detect emergent behaviour early.
Managing emergence requires humility: complex systems cannot be fully controlled, only influenced. The goal is to guide system dynamics toward safe and productive outcomes.
5. Illustrative examples of emergence in engineering systems:
Network systems
The Internet exemplifies emergence: billions of devices follow simple communication protocols, yet collectively create a resilient, adaptive global network. No single node dictates its performance; instead, routing efficiency and viral content propagation arise from local interactions among routers and users.
Transportation systems
Urban traffic patterns such as congestion waves, spontaneous lane formation, and adaptive rerouting emerge from individual driver behaviour and infrastructural design. Traffic engineers use simulation models to study how simple decision rules generate complex city-wide flows.
Energy systems
Electrical grids maintain frequency and voltage stability through distributed interactions among generators, loads, and controllers. Emergent synchronisation enables reliability, but loss of coordination can cause cascading blackouts showing both beneficial and harmful emergence.
Manufacturing systems
In smart factories, machines and sensors collaborate autonomously, producing system-wide optimisation in scheduling and quality control. Adaptive algorithms and feedback loops create emergent flexibility beyond what central planning alone could achieve.
6. Practical guidance for engineers and educators:
For engineers, the key is to design with emergence in mind:
focus on local rules that encourage desirable global behaviour;
incorporate feedback and sensing to detect changes early; and
use modular, diverse architectures to enhance resilience.
For educators, teaching emergence provides an opportunity to bridge theory and practice. Software such as NetLogo and Insight Maker allows students to visualise emergent behaviour through agent-based and system-dynamics models. Linking engineering examples to ecological, social, or digital systems helps learners appreciate the universality of emergence.
Conclusion:
Emergence is not an anomaly to be avoided but a natural attribute of complex systems. It challenges traditional engineering by revealing that system behaviour often arises from relationships, not components.
Understanding emergence equips engineers to recognise interdependencies, design adaptive solutions, and work with complexity rather than against it. By embracing systems thinking, engineers can create technologies that are not only functional but resilient, sustainable, and aligned with real-world dynamics.
References:
Holland, J.H. (2014). Complexity: A Very Short Introduction. Oxford: Oxford University Press.
Johnson, S. (2001). Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York: Scribner.
Mitchell, M. (2009). Complexity: A Guided Tour. Oxford: Oxford University Press.
Bar-Yam, Y. (2003). Dynamics of Complex Systems. Cambridge, MA: Perseus Publishing.
Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51-59.
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.
Downloads: A PDF of this resource will be available soon.
Who is this article for?: Thisarticle 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:
Jakobsson, Eric et al. (1999) ‘Complex systems: Why and what?’, New England Complex Systems Institute. Available at: https://necsi.edu/complex-systems-why-and-what (Accessed: 16 July 2025).
Stegeager, N., Traulsen, S., Carvalho Guerra, A., Telléus, P., Du, X. (2024) ‘Do good intentions lead to expected outcomes? Professional learning amongst early career academics in a problem-based program’, Education Sciences, 14(2), p. 205. Available at: https://doi.org/10.3390/educsci14020205.
Tauro, F., Cha, Y., Rahim, F., Rasul, M.S., Osman, K., Halim, L., Dennisur, D., Esner, B., Porfiri, M. (2017) ‘Integrating mechatronics in project-based learning of Malaysian high school students and teachers’, International Journal of Mechanical Engineering Education. Available at: https://journals.sagepub.com/doi/full/10.1177/0306419017708636 (Accessed: 1 August 2025).
Watters, J., Pillay, H. and Flynn, M. (2016) ‘Industry-school partnerships: A strategy to enhance education and training opportunities. Australia’, Queensland University of Technology. Available at: https://eprints.qut.edu.au/98390/ (Accessed: 30 July 2025).
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.
Keywords: Artificial Intelligence; Large Language Model; Sociotechnical systems; Ethics; Modelling or simulation; Emergence.
Licensing:This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. It is based upon the author’s article “Enhancing Ethical Reasoning in Engineering Education through Student-Created Interactive Ethical Scenarios Using Generative AI,” 2025 IEEE Global Engineering Education Conference (EDUCON), London, United Kingdom, 2025, pp. 1-5, doi: 10.1109/EDUCON62633.2025.11016531.
Related INCOSE Competencies: Toolkit resources are designed to be applicable to any engineering discipline, but educators might find it useful to understand their alignment to competencies outlined by the International Council on Systems Engineering (INCOSE). The INCOSE Competency Framework provides a set of 37 competencies for Systems Engineering within a tailorable framework that provides guidance for practitioners and stakeholders to identify knowledge, skills, abilities and behaviours crucial to Systems Engineering effectiveness. A free spreadsheet version of the framework can be downloaded.
This resource relates to the Systems Thinking, Life Cycle, Configuration Management, Requirements Definition, Verification, and Validation INCOSE Competencies.
AHEP mapping: This resource addresses several of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): Analytical Tools and Techniques (critical to the ability to model and solve problems), and Integrated / Systems Approach (essential to the solution of broadly-defined problems). In addition, this resource addresses AHEP themes of Ethics and Communication.
Debug their simulation through playtesting, documenting issue → fix → retest cycles and demonstrating how changes improve coherence.
Explore trade-offs and justify decisions in ethics (e.g. consequences and equity) and complex systems (e.g. resilience vs cost vs emissions).
Evidence learning with transparent artefacts: initial prompt, changes via tracked changes or before/after snippets, tester feedback, and final prompt.
Reflect critically on validity, bias and the limitations of LLMs as simulators, including how to handle unsafe/poor choices by surfacing realistic consequences.
Communicate findings clearly to technical and nontechnical audiences.
Teachers have the opportunity to:
Use this as either a studio activity (3–5 sessions) or a compact assessment only task (1–2 sessions), with clear rubrics for each.
Standardise scope by offering a predefined scenario (e.g., Urban Heatwave Response, UK city), or permit student proposed topics.
Scale marking via artefact based evidence (prompt, change log, feedback, final prompt) rather than long reports.
Deliver with institutional Microsoft Copilot licences or any free web LLM; require students to disclose model and version used.
Adapt quickly to different disciplines by swapping the scenario pack (microgrids, water networks, medical device supply chains, etc.).
Overview:
This resource enables engineering students to create, run, and debug a text‑based, interactive simulation of a complex sociotechnical system using a Large Language Model (LLM). It is intentionally flexible and may be delivered as a multi‑session studio activity (including assessment) or used solely as a compact assessment.
Purpose and use:
In both modes, students design a robust text prompt, test it with a user, document changes, and submit auditable artefacts that evidence learning. The key activity is interrogating their own thinking on how complex systems should be modelled by making judgements as to how their game does and does not capture the system dynamics.
The submission is a text LLM prompt with tracked changes, which allows students to demonstrate system design and debugging, produce transparent process evidence, and scale to large cohorts with minimal infrastructure.
Delivery options at a glance:
Audience
Undergraduate Years 2–4 and taught MSc, any engineering discipline
Modes
Studio activity (3–5×2 h + independent study) or Assessment‑only (prompt‑only; 1–2×2 h + 4–6 h)
Teams
3–4 students (solo permitted for assessment‑only)
Assessment
Portfolio (studio) or prompt‑plus‑change‑log (assessment‑only)
Platforms
Institutional Copilot licences successful; encourage exploration of free tools (students record model/version)
Materials and software:
LLM access: institutional Microsoft Copilot licences (proven) or any reputable free web‑based tool. Students disclose the model and version.
Delivery modes:
Mode A — Studio activity (3–5 sessions)
Session 1: Frame the system — boundary, stakeholders, conflicting goals; sketch a Causal Loop Diagram (CLD) with at least two reinforcing and two balancing loops.
Session 2: Make it playable — define 4–8 state variables and KPIs; draft the prompt (based on Appendix A); specify commands, turn length and stop conditions; add debug controls (`trace`, `why`, `show variables`, `revert`).
Between sessions: Prototype v1 — run 10–15 turns; capture a transcript; log defects (e.g. inconsistent updates, missing delays, moralising responses).
Session 3: Play‑test and iterate — exchange prototypes across teams or test with an external user; record issue → fix → re‑test cycles with evidence (make sure edits are captured in tracked changes).
Session 4: Present and reflect — short demo (6–8 turns); explain how feedback/delays manifest; discuss surprises and limits.
Mode B — Assessment‑only (prompt‑only; 1–2 sessions)
Session 1: Brief and rapid scoping — select a scenario (student‑chosen or predefined); write a one‑paragraph boundary and stakeholders note; draft the initial prompt (based on Appendix A) with role choices, 4–6 state variables, simple commands, and a 12–15 turn cap.
Independent work: Debugging loop — run the prompt; identify faults; edit the prompt (make sure edits are captured in tracked changes); re‑run and capture short snippets demonstrating fixes; test with one peer and collect written feedback.
Session 2: Submission — students submit a single document with the initial prompt, change log (before/after snippets), tester feedback, the final prompt, and a short rationale of innovative choices.
In both modes, module leaders may supply a predefined scenario(s) to standardise scope and simplify marking. A ready‑to‑use example is provided in Appendix C.
Critical medical device supply chain — redundancy vs cost; equitable allocation.
Appendix A — Prompt template (simulation + debug‑ready):
Title: Complex Systems Simulator — [Scenario]
Purpose: Run a turn‑based interactive simulation of a complex sociotechnical system. Track named state variables, apply feedback and delays, and let the player’s decisions drive non‑linear outcomes.
Setup:
1) Offer three roles (distinct authority/constraints).
2) Introduce 3–5 NPCs with clear goals and plausible interventions.
3) Show a dashboard of [STATE_VARIABLES] each turn with short context.
State rules:
Track only these variables (with units/ranges): [list 5–8].
Maintain at least two feedback loops and one delay; keep hidden rule notes consistent across turns.
Each turn: recap; propose 3–5 options (plus free‑text); explain updates; show dashboard; request the following action.
Time step: 5 minutes to 1 week; end after 20–30 turns or on stop conditions.
Commands: status, talk [npc], inspect [asset], implement [policy], pilot [intervention], advance time, review log.
Debug commands (for testing): trace on/off (print update logic), why (state which loops/delays drove the change), show variables (print current state table), revert (roll back one turn), reseed (slight exogenous shock).
Realism and ethics: Allow all plausible actions and report consequences neutrally. If unsafe in the real world, refuse, propose safer alternatives, and continue with plausible systemic effects.
LLM pitfalls to avoid: Do not invent new variables; ask clarifying questions rather than guessing; keep outputs concise; summarise trajectory every five turns.
Begin: Greet the player, state the scenario, ask for a role, and wait.
Appendix B — Debugging and play‑test checklist:
Functional coherence
Do state variables update consistently with declared logic?
Are reinforcing and balancing feedback identifiable in play?
Robustness
Does the simulation permit negative choices with realistic consequences?
Do trace/why explanations match outcomes?
Are stop conditions respected?
User experience and clarity
Are commands clear? Is turn pacing appropriate?
Are dashboards concise and informative?
Report
Provide three concrete defects with turn numbers, the prompt edits that fixed them, and evidence of the re‑run.
Appendix C — Predefined scenario (Urban Heatwave Response, UK city):
Boundary: One UK local authority area during the July–August heatwave period. Focus on public health, energy demand, and community resilience.
Roles: (1) Local Authority Resilience Lead; (2) NHS Trust Capacity Manager; (3) Distribution Network Operator (DNO) Duty Engineer.
Stakeholders: Residents (with a focus on vulnerable groups), care homes, schools, SMEs, DNO, local NHS Trust, emergency services, voluntary/community groups, Met Office (for alerts), and local media.
State variables (examples): Heat‑health alert level (0–4); Emergency Department occupancy (%); Electricity demand/capacity (% of peak); Indoor temperature exceedance hours (hrs > 27 °C); Public trust (0–100); Budget (£); Equity index (0–100).
Events/shocks: Red heat alert; substation fault; procurement delay; misinformation spike on social media; transport disruption; community centre cooling failure.
KPIs and stop conditions: Heat‑related admissions; unserved energy; cost variance; equity gap across wards. Stop if alert level 4 persists >3 days, budget overspends >10%, or trust <25.
Notes for assessors: Using a standard, predefined scenario simplifies marking and ensures comparable complexity across teams, while still allowing for diverse strategies and outcomes.
Any views, thoughts, and opinions expressed herein are solely that of the author(s) and do not necessarily reflect the views, opinions, policies, or position of the Engineering Professors’ Council or the Toolkit sponsors and supporters.
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.
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.
For applications to large corporations that use AI recruitment tools, it is recommended:
Use a plain text CV.
Include a web link to your LinkedIn profile or personal portfolio showcasing your engineering and technology capabilities.
Digitally watermark all items in your portfolio to protect your intellectual property (IP).
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.
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 aims to equip students with strategies to thrive in video interviews.
Introduction: Our mission is to empower students with tips to excel in video interviews. This interactive challenge provides tailored advice to leverage your strengths and navigate digital recruitment challenges. Get expert guidance for in-person, video, and telephone interviews with recruiters. Learn about optimal lighting, assessment centres, and holistic interview practices.
Topic: Mastering video and virtual interview skills with inclusive preparation strategies.
Keywords: Neurodiversity; Equity Diversity and Inclusion; Interviews; Recruitment; CVs and cover letters; Digitalisation; Communication; Employability and skills; Accessibility; Professional development; Professional conduct; Digital engineering tools; Artificial intelligence; Virtual Learning Environment; Personal or professional reputation; Student support; Technology; Assessment criteria or methods and tools; Bias.
How to optimise your interview setup and presence
Watch our featured video from Wenite (below) for expert tips on optimising your interview setup and presence.
Video summary:
Being well-prepared for job interviews is essential for making strong impressions, boosting confidence, and gaining a competitive edge.
Highlights:
🎯Importance of preparation: Crucial for first impressions and confidence.
👔In-person tips: Dress appropriately, mind body language, and plan travel.
💻Virtual interview prep: Ensure tech works, choose a quiet space, and test the platform.
📞Phone interview strategies: Use notes wisely, maintain vocal clarity, and avoid distractions.
🌟STAR technique: A framework for answering behavioural questions effectively.
🏢Research the company: Align your values and goals with the organisation to show genuine interest.
❓Prepare questions: Have smart, relevant questions ready for the interviewer.
Key insights :
🔍First impressions matter: A strong initial impression can set the tone for the entire interview, making preparation vital.
💪Confidence through practice: Thorough preparation helps articulate thoughts clearly, enhancing confidence during interviews.
🏆Competitive edge: Detailed preparation allows candidates to showcase unique skills and experiences, differentiating them from others.
🎥Adapt to formats: Each interview type requires a tailored approach, from dressing well for in-person to testing tech for virtual formats.
📖Utilise the STAR technique: This adaptable framework helps structure responses to behavioural questions, ensuring clarity and relevance.
🌐Company research is critical: Understanding the company’s values and strategies can help align your responses and demonstrate genuine interest.
❓Engaging questions matter: Thoughtful questions reflect your interest in the role and provide insights into the company culture and expectations.
Lights, camera, action!
A profile picture or video interview is often your first impression on a potential employer. Ensure you convey professionalism, approachability, and confidence, especially with proper lighting for accurate representation. AI tools can optimise your appearance by adjusting lighting and camera settings for accurate colour representation, helping you present your best self.
When preparing for a job interview, ensure the process is accessible to all candidates by requesting reasonable adjustments, like receiving interview questions beforehand. Approach employers with confidence and professionalism, clearly explaining how these adjustments will help you perform at your best. Proactively advocating for such adjustments fosters a more inclusive environment for all applicants.
The following is a mapping of neurodiversity traits to their corresponding strengths mapped to UK Engineering Council Specification of professional engineering skills. This can aid in job applications and interview preparation, as evidence of applied neurodiversity strengths can demonstrate engineering and employability skills: Neurodiversity Strengths Mapping
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
Access our University Career Services Library to connect with your university’s career services and take advantage of employability training opportunities, such as mock assessment centre sessions.
Thornton et al (2019) research concluded to prevent gender and race bias at assessment centres, employers must implement rigorous development and practices to counter both conscious and unconscious biases.
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.
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.
You can now view the recording of the ACE-Lab and agentic engineering workflows webinar, as part of the Complex Systems Toolkit, supported by Quanser. The webinar was live on Monday 29th June 2026, 11am-1pm BST.
You can now view the recording of the ACE-Lab and agentic engineering workflows webinar, as part of the 
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.
Diversity matters: