Toolkit: Complex Systems Toolkit.

Author: Onyekachi Nwafor (KatexPower).

Topic: Emergence in complex systems.

Title: Understanding emergence in complex engineering systems.

Resource type: Knowledge article.

Relevant disciplines: Any.

Keywords: Emergence; Boundaries; Unpredictability; Instability; System dynamics model; Digital engineering tools.

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

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

Who is this article for?: This article should be read by educators at all levels in higher education who are seeking to 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).  

 

Learning and teaching resources:

 

Premise:

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.

 

2. Understanding emergence:

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: 

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: 

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:

Engineers can identify emergence by looking for: 

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: 

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:

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. 

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. 

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. 

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: 

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:

 

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

Toolkit: Complex Systems Toolkit.

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

Topic: Why integrate complex systems in engineering education? 

Title: Complex systems in a transformational era.

Resource type: Knowledge article.

Relevant disciplines: Any.

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

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

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

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

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

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

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

 

Premise:

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

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

 

What this means for engineering education and educators:

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

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

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

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

 

The imperative to transform engineering education:

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

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

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

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

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

 

Looking ahead:

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

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

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

 

References:

 

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

Toolkit: Complex Systems Toolkit.

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

Topic: Student created interactive simulation for complex sociotechnical systems.

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

Resource type: Teaching activity.

Relevant disciplines: Any.

Keywords: 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. 

Downloads: 

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

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

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

Education level: Intermediate.

 

Learners have the opportunity to: 

Teachers have the opportunity to: 

 

Overview:

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

  

Purpose and use:

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

 

Why and how: 

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

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

 

Delivery options at a glance:

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

 

Materials and software:

 

Delivery modes:

Mode A — Studio activity (3–5 sessions) 

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

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

 

Assessment:

Studio portfolio — rubric (suggested weighting):

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

 

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

 

Scenario options: 

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

 

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

Title: Complex Systems Simulator — [Scenario] 

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

Setup: 

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

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

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

State rules: 

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

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

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

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

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

 

Appendix B — Debugging and play‑test checklist: 

Functional coherence 

Robustness 

User experience and clarity 

Report 

 

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

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

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

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

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

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

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

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

 

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

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

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

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

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

 

Master the art of applying for engineering computing jobs

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

Video summary:

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

Key insights:

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

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

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

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

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

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

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

 

AI and Applications

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

Further links to look at:

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

 

CV and Covering Letter

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

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

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

 

Aptitude and Abstract Reasoning Test 

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

Further links to look at:

 

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

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

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

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


Objectives: This activity 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.  

Further links to look at:

 

Neurodiversity   

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.  

Further links to look at:

 

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

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

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

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


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

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

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

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

 

An immersive experience

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

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

Video summary: 

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

Key insights: 

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

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

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

Video summary:

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

Key insights:

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

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

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

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

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

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

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

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

Key insights:

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

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

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

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

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

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

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

 

Resources

 

Underrepresented groups preparing for virtual assessment centres 

 

How to PASS an assessment centre UK

The video offers tailored guidance specifically for international students.

 

Acing virtual assessment centres: future you webinar: 

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

 

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

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

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

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

We’re excited to share with you that we are starting work on a Complex Systems Toolkit, aimed at supporting educators in their teaching of the subject. Toolkit development will start in early 2025. The Complex Systems Toolkit is supported by Quanser. Read on to learn more and find out how you can get involved.

WHY is the EPC developing a Complex Systems Toolkit?

WHAT is a Complex Systems Toolkit?

HOW will the Toolkit be developed?

WHO is involved in Toolkit development?

 

This post is also available here. 

Authors: Dr Gilbert Tang; Dr Rebecca Raper (Cranfield University). 

Topic: Considering the SDGs at all stages of new robot creation. 

Tool type: Guidance. 

Relevant disciplines: Computing; Robotics; Electrical; Computer science; Information technology; Software engineering; Artificial Intelligence; Mechatronics; Manufacturing engineering; Materials engineering; Mechanical engineering; Data. 

Keywords: SDGs; AHEP; Sustainability; Design; Life cycle; Local community; Environment; Circular economy; Recycling or recycled materials; Student support; Higher education; Learning outcomes. 

Sustainability competency: Systems thinking; Anticipatory; Critical thinking.

AHEP mapping: This resource addresses two of the themes from the UK’s Accreditation of Higher Education Programmes fourth edition (AHEP4): The Engineer and Society (acknowledging that engineering activity can have a significant societal impact) and Engineering Practice (the practical application of engineering concepts, tools and professional skills). To map this resource to AHEP outcomes specific to a programme under these themes, access AHEP 4 here  and navigate to pages 30-31 and 35-37.  

Related SDGs: SDG 9 (Industry, innovation, and infrastructure); SDG 12 (Responsible consumption and production). 

Reimagined Degree Map Intervention: Adapt and repurpose learning outcomes; More real-world complexity.

Who is this article for? This article is for educators working at all levels of higher education who wish to integrate Sustainability into their robotics engineering and design curriculum or module design. It is also for students and professionals who want to seek practical guidance on how to integrate Sustainability considerations into their robotics engineering. 

 

Premise:  

There is an urgent global need to address the social and economic challenges relating to our world and the environment (Raper et al., 2022). The United Nations Sustainable Development Goals (SDGs) provide a framework for individuals, policy-makers and industries to work to address some of these challenges (Gutierrez-Bucheli et al., 2022). These 17 goals encompass areas such as clean energy, responsible consumption, climate action, and social equity. Engineers play a pivotal role in achieving these goals by developing innovative solutions that promote sustainability and they can use these goals to work to address broader sustainability objectives. 

Part of the strategy to ensure that engineers incorporate sustainability into their solution development is to ensure that engineering students are educated on these topics and taught how to incorporate considerations at all stages in the engineering process (Eidenskog et al., 2022). For instance, students need not only to have a broad awareness of topics such as the SDGs, but they also need lessons on how to ensure their engineering incorporates sustainable practice. Despite the increased effort that has been demonstrated in engineering generally, there are some challenges when the sustainability paradigm needs to be integrated into robotics study programs or modules (Leifler and Dahlin, 2020). This article details one approach to incorporate considerations of the SDGs at all stages of new robot creation: including considerations prior to design, during creation and manufacturing and post-deployment. 

 

1. During research and problem definition:

Sustainability considerations should start from the beginning of the engineering cycle for robotic systems. During this phase it is important to consider what the problem statement is for the new system, and whether the proposed solution satisfies this in a sustainable way, using Key Performance Indicators (KPIs) linked to the SDGs (United Nations, 2018), such as carbon emissions, energy efficiency and social equity (Hristov and Chirico, 2019). For instance, will the energy expended to create the robot solution be offset by the robot once it is in use? Are there long-term consequences of using a robot as a solution? It is important to begin engagement with stakeholders, such as end-users, local communities, and subject matter experts to gain insight into these types of questions and any initial concerns. Educators can provide students with opportunities to engage in the research and development of robotics technology that can solve locally relevant problems and benefit the local community. These types of research projects allow students to gain valuable research experience and explore robotics innovations through solving problems that are relatable to the students. There are some successful examples across the globe as discussed in Dias et al., 2005. 

 

2. At design and conceptualisation:

Once it is decided that a robot works as an appropriate solution, Sustainability should be integrated into the robot system’s concept and design. Considerations can include incorporating eco-design principles that prioritise resource efficiency, waste reduction, and using low-impact materials. The design should use materials with relatively low environmental footprints, assessing their complete life cycles, including extraction, production, transportation, and disposal. Powered systems should prioritise energy-efficient designs and technologies to reduce operational energy consumption, fostering sustainability from the outset. 

 

3. During creation and manufacturing:

The robotic system should be manufactured to prioritise methods that minimise, mitigate or offset waste, energy consumption, and emissions. Lean manufacturing practices can be used to optimise resource utilisation where possible. Engineers should be aware of the importance of considering sustainability in supply chain management to select suppliers with consideration of their sustainability practices, including ethical labour standards and environmentally responsible sourcing. Robotic systems should be designed in a way that is easy to assemble and disassemble, thus enabling robots to be easily recycled, or repurposed at the end of their life cycle, promoting circularity and resource conservation. 

 

4. Deployment:

Many robotic systems are designed to run constantly day and night in working environments such as manufacturing plants and warehouses. Thus energy-efficient operation is crucial to ensure users operate the product or system efficiently, utilising energy-saving features to reduce operational impacts. Guidance and resources should be provided to users to encourage sustainable practices during the operational phase. System designers should also implement systems for continuous monitoring of performance and data collection to identify opportunities for improvement throughout the operational life. 

 

5. Disposal:

Industrial robots have an average service life of 6-7 years. It is important to consider their end-of-life and plan for responsible disposal or recycling of product components. Designs should be prioritised that facilitate disassembly and recycling (Karastoyanov and Karastanev, 2018). Engineers should identify and safely manage hazardous materials to comply with regulations and prevent environmental harm. Designers can also explore options for product take-back and recycling as part of a circular economy strategy. There are various ways of achieving that. Designers can adopt modular design methodologies to enable upgrades and repairs, extending their useful life. Robot system manufacturers should be encouraged to develop strategies for refurbishing and reselling products, promoting reuse over disposal. 

 

Conclusion: 

Sustainability is not just an option but an imperative within the realm of engineering. Engineers must find solutions that not only meet technical and economic requirements but also align with environmental, social, and economic sustainability goals. As well as educating students on the broader topics and issues relating to Sustainability, there is a need for teaching considerations at different stages in the robot development lifecycle. Understanding the multifaceted connections between sustainability and engineering disciplines, as well as their impact across various stages of the engineering process, is essential for engineers to meet the challenges of the 21st century responsibly.  

 

References: 

Dias, M. B., Mills-Tettey, G. A., & Nanayakkara, T. (2005, April). Robotics, education, and sustainable development. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation (pp. 4248-4253). IEEE. 

Eidenskog, M., Leifler, O., Sefyrin, J., Johnson, E., & Asplund, M. (2023). Changing the world one engineer at a time–unmaking the traditional engineering education when introducing sustainability subjects. International Journal of Sustainability in Higher Education, 24(9), 70-84.  

Gutierrez-Bucheli, L., Kidman, G., & Reid, A. (2022). Sustainability in engineering education: A review of learning outcomes. Journal of Cleaner Production, 330, 129734. 

Hristov, I., & Chirico, A. (2019). The role of sustainability key performance indicators (KPIs) in implementing sustainable strategies. Sustainability, 11(20), 5742. 

Karastoyanov, D., & Karastanev, S. (2018). Reuse of Industrial Robots. IFAC-PapersOnLine, 51(30), 44-47. 

Leifler, O., & Dahlin, J. E. (2020). Curriculum integration of sustainability in engineering education–a national study of programme director perspectives. International Journal of Sustainability in Higher Education, 21(5), 877-894. 

Raper, R., Boeddinghaus, J., Coeckelbergh, M., Gross, W., Campigotto, P., & Lincoln, C. N. (2022). Sustainability budgets: A practical management and governance method for achieving goal 13 of the sustainable development goals for AI development. Sustainability, 14(7), 4019. 

SDG Indicators — SDG Indicators (2018) United Nations (Accessed: 19 February 2024) 

 

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

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

 

To view a plain text version of this resource, click here to download the PDF.

Authors: Ahmet Omurtag (Nottingham Trent University); Andrei Dragomir (National University of Singapore / University of Houston).

Topic: Data security of smart technologies.

Engineering disciplines: Electronics; Data; Biomedical engineering.

Ethical issues: Autonomy; Dignity; Privacy; Confidentiality.

Professional situations: Communication; Honesty; Transparency; Informed consent; Misuse of data.

Educational level: Advanced.

Educational aim: Practising Ethical Analysis: engaging in a process by which ethical issues are defined, affected parties and consequences are identified, so that relevant moral principles can be applied to a situation in order to determine possible courses of action.

 

Learning and teaching notes:

This case involves Aziza, a biomedical engineer working for Neuraltrix, a hypothetical company that develops Brain-computer interfaces (BCI) for specialised applications. Aziza has always been curious about the brain and enthusiastic about using cutting-edge technologies to help people in their daily lives. Her team has designed a BCI that can measure brain activity non-invasively and, by applying machine learning algorithms, assess the job-related proficiency and expertise level of a person. She is leading the deployment of the new system in hospitals and medical schools, to be used in evaluating candidates being considered for consultant positions. In doing so, and to respond to requests to extend and use the BCI-based system in unforeseen ways, she finds herself compelled to weigh various ethical, legal and professional responsibilities.

This case study addresses two of AHEP 4’s themes: The Engineer and Society (acknowledging that engineering activity can have a significant societal impact) and Engineering Practice (the practical application of engineering concepts, tools and professional skills). To map this case study to AHEP outcomes specific to a programme under these themes, access AHEP 4 here and navigate to pages 30-31 and 35-37.

The dilemma in this case is presented in three parts. If desired, a teacher can use the Summary and Part one in isolation, but Parts two and three develop and complicate the concepts presented in the Summary and Part one to provide for additional learning. The case allows teachers the option to stop at multiple points for questions and/or activities as desired.

Learners have the opportunity to:

Teachers have the opportunity to:

 

Learning and teaching resources:

Legal regulations:

Professional organisations:

Philanthropic organisations:

Journal articles:

Educational institutions:

 

Summary:

Brain-computer interfaces (BCIs) detect brain activity and utilise advanced signal analysis to identify features in the data that may be relevant to specific applications. These features might provide information about people’s thoughts and intentions or about their psychological traits or potential disorders, and may be interpreted for various purposes such as for medical diagnosis, for providing real-time feedback, or for interacting with external devices such as a computer. Some current non-invasive BCIs employ unobtrusive electroencephalography headsets or even optical (near-infrared) sensors to detect brain function and can be safe and convenient to use.

Evidence shows that the brains of people with specialised expertise have identifiable functional characteristics. Biomedical technology may translate this knowledge soon into BCIs that can be used for objectively assessing professional skills. Researchers already know that neural signals support features linked to levels of expertise, which may enable the assessment of job applicants or candidates for promotion or certification.

BCI technology would potentially benefit people by improving the match between people and their jobs, and allowing better and more nuanced career support. However, the BCI has access to additional information that may be sensitive or even troubling. For example, it could reveal a person’s health status (such as epilepsy or stroke), or it may suggest psychological traits ranging from unconscious racial bias to psychopathy. Someone sensitive about their privacy may be reluctant to consent to wearing a BCI.

In everyday life, we show what is on our minds through language and behaviour, which are normally under our control, and provide a buffer of privacy. BCIs with direct access to the brain and increasing capability to decode its activity may breach this buffer. Information collected by BCIs could be of interest not only to employers who will decide whether to hire and invest in a new employee, but also to health insurers, advertising agencies, or governments.

 

Optional STOP for questions and activities:

1. Activity: Risks of brain activity decoding – Identify the physical, ethical, and social difficulties that could result from the use of devices that have the ability to directly access the brain and decipher some of its psychological content such as thoughts, beliefs, and emotions.

2. Activity: Regulatory oversight – Investigate which organisations and regulatory bodies currently monitor and are responsible for the safe and ethical use of BCIs.

3. Activity: Technical integration – Investigate how BCIs work to translate brain activity into interpretable data.

 

Dilemma – Part one:

After the company, Neuraltrix, deployed their BCI and it had been in use for a year in several hospitals, its lead developer Aziza became part of the customer support team. While remaining proud and supportive of the technology, she had misgivings about some of its unexpected ramifications. She received the following requests from people and institutions for system modifications or for data sharing:

1. A hospital asked Neuraltrix for a technical modification that would allow the HR department to send data to their clinical neurophysiologists for “further analysis,” claiming that this might benefit people by potentially revealing a medical abnormality that might otherwise be missed.

2. An Artificial Intelligence research group partnering with Neuraltrix requested access to the data to improve their signal analysis algorithms.

3. A private health insurance company requested Neuraltrix provide access to the scan of someone who had applied for insurance coverage; they stated that they have a right to examine the scan just as life insurance agencies are allowed to perform health checks on potential customers.

4. An advertising agency asked Neuraltrix for access to their data to use them to fine-tune their customer behavioural prediction algorithms.

5. A government agency demanded access to the data to investigate a suspected case of “radicalisation”.

6. A prosecutor asked for access to the scan of a specific person because she had recently been the defendant in an assault case, where the prosecutor is gathering evidence of potential aggressive tendencies.

7. A defence attorney requested data because they were gathering potentially exonerating evidence, to prove that the defendant’s autonomy had been compromised by their brain states, following a line of argument known as “My brain made me do it.”

 

Optional STOP for questions and activities: 

1. Activity: Identify legal issues – Students could research what laws or regulations apply to each case and consider various ways in which Neuraltrix could lawfully meet some of the above requests while rejecting others, and how their responses should be communicated within the company and to the requestor.

2. Activity: Identify ethical issues – Students could reflect on what might be the immediate ethical concerns related to sharing the data as requested.

3. Activity: Discussion or Reflection – Possible prompts:

 

Dilemma – Part two:

The Neuraltrix BCI has an interface which allows users to provide informed consent before being scanned. The biomedical engineer developing the system was informed about a customer complaint which stated that the user had felt pressured to provide consent as the scan was part of a job interview. The complaint also stated that the user had not been aware of the extent of information gleaned from their brains, and that they would not have provided consent had been made aware of it.

 

Optional STOP for questions and activities: 

1. Activity: Technical analysis – Students might try to determine if it is possible to design the BCI consent system and/or consent process to eliminate the difficulties cited in the complaint. Could the device be designed to automatically detect sensitive psychological content or allow the subject to stop the scan or retroactively erase the recording?

2. Activity: Determine the broader societal impact and the wider ethical context – Students should consider what issues are raised by the widespread availability of brain scans. This could be done in small groups or a larger classroom discussion.

Possible prompts:

 

Dilemma – Part three:

Neuraltrix BCI is about to launch its updated version, which features all data processing and storage moved to the cloud to facilitate interactive and mobile applications. This upgrade attracted investors and a major deal is about to be signed. The board is requesting a fast deployment from the management team and Aziza faces pressure from her managers to run final security checks and go live with the cloud version. During these checks, Aziza discovers a critical security issue which can be exploited once the BCI runs in the cloud, risking breaches in the database and algorithm. Managers believe this can be fixed after launch and request the engineer to start deployment and identify subsequent solutions to fix the security issue.

 

Optional STOP for questions and activities: 

1. Activity: Students should consider if it is advisable for Aziza to follow requests from managers and the Neuraltrix BCI board and discuss possible consequences, or halt the new version deployment which may put at risk the new investment deal and possibly the future of the company.

2. Activity: Apply an analysis based on “Duty-Ethics” and “Rights Ethics.” This could be done in small groups (who would argue for management position and engineer position, respectively) or a larger classroom discussion. A tabulation approach with detailed pros and cons is recommended.

3. Activity: Apply a similar analysis as above based on the principles of “Act-Utilitarianism” and “Rule-Utilitarianism.”

Possible prompts:

 

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

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

 

Case enhancement: Facial recognition for access and monitoring

Activity: Prompts to facilitate discussion activities. 

Author: Sarah Jayne Hitt, Ph.D. SFHEA (NMITE, Edinburgh Napier University).

 

Overview:

There are several points in this case during which an educator can facilitate a class discussion about relevant issues. Below are prompts for discussion questions and activities that can be used. These correspond with the stopping points outlined in the case. Each prompt could take up as little or as much time as the educator wishes, depending on where they want the focus of the discussion to be. The discussion prompts for Dilemma Part three are already well developed in the case study, so this enhancement focuses on expanding the prompts in Parts one and two.

 

Dilemma Part one – Discussion prompts:

1. Legal Issues. Give students ten minutes to individually or in groups do some online research on GDPR and the Data Protection Act (2018). In either small groups or as a large class, discuss the following prompts. You can explain that even if a person is not an expert in the law, it is important to try to understand the legal context. Indeed, an engineer is likely to have to interpret law and policy in their work. These questions invite critical thinking and informed consideration, but they do not necessarily have “right” answers and are suggestions that can help get a conversation started.

a. Are legal policies clear about how images of living persons should be managed when they are collected by technology of this kind?

b. What aspects of these laws might an engineer designing or deploying this system need to be aware of?

c. Do you think these laws are relevant when almost everyone walking around has a digital camera connected to the internet?

d. How could engineers help address legal or policy gaps through design choices?

2. Sharing Data. Before entering into a verbal discussion, either pass out the suggested questions listed in the case study on a worksheet or project on a screen. Have students spend five or ten minutes jotting down their personal responses. To understand the complexity of the issue, students could even create a quick mind map to show how different entities (police, security company, university, research group, etc.) interact on this issue. After the students spend some time in this personal reflection, educators could ask them to pair/share—turn to the person next to them and share what they wrote down. After about five minutes of this, each pair could amalgamate with another pair, with the educator giving them the prompt to report back to the full class on where they agree or disagree about the issues and why.

3. GDPR Consent. Before discussing this case particularly, ask students to describe a situation in which they had to give GDPR consent. Did they understand what they were doing, what the implications of consent are, and why? How did they feel about the process? Do they think it’s an appropriate system? This could be done as a large group, small group, or through individual reflection. Then turn the attention to this case and describe the change of perspective required here. Now instead of being the person who is asked for consent, you are the person requiring consent. Engineers are not lawyers, but engineers often are responsible for delivering legally compliant systems. If you were the engineer in charge in this case, what steps might you take to ensure consent is handled appropriately? This question could be answered in small groups, and then each group could report back to the larger class and a discussion could follow the report-backs.

4. Institutional Complexity. The questions listed in the case study relate to the fact that the building in which the facial recognition system will be used accommodates many different stakeholders. To help students with these questions, educators could divide the class into small groups, with each group representing one of the institutions or stakeholder groups (college, hospital, MTU, students, patients, public, etc.). Have each group investigate whether regulations related to captured images are different for their stakeholders, and debate if they should be different. What considerations will the engineer in the case have to account for related to that group? The findings can then be discussed as a large class.

 

Dilemma Part two – Discussion prompts:

The following questions relate to macroethical concerns, which means that the focus is on wider ethical contexts such as fairness, equality, responsibility, and implications.

1. Benefits and Burdens. To prepare to discuss the questions listed in the case study, students could make a chart of potential harms and potential benefits of the facial recognition system. They could do this individually, in pairs or small groups, or as a large class. Educators should encourage them to think deeply and broadly on this topic, and not just focus on the immediate, short-term implications. Once this chart is made, the questions listed in the case study could be discussed as a group, and students asked to weigh up these burdens and benefits. How did they make the choices as to when a burden should outweigh a benefit or vice versa?

2. Equality and Utility. To address the questions listed in the case study, students could do some preliminary individual or small group research on the accuracy of facial recognition systems for various population groups. The questions could then be discussed in pairs, small groups, or as a large class.

3. Engineer Responsibility. Engineers are experts that have much more specific technical knowledge and understanding than the general public. Indeed, the vast majority of people have no idea how a facial recognition system works and what the legal requirements are related to it, even if they are asked to give their consent. Does an engineer therefore have more of a responsibility to make people aware and reassure them? Or is an engineer just fulfilling their duty by doing what their boss says and making the system work? What could be problematic about taking either of those approaches?

 

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

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

Let us know what you think of our website