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Case Study: Supporting Students in Using Generative AI – Dr. Ioannis Georgilas

Dr. Ioannis Georgilas delivering his 'Generative AI and the impact to Engineering Skills' lecture.
Dr. Ioannis Georgilas delivering his ‘Generative AI and the impact to Engineering Skills’ lecture.


Generative Artificial Intelligence (Gen AI) is on the forefront of everyone’s mind. This academic year the University adopted a set of high-level Gen AI principles in education developed by the Russell Group and the Skills Centre has created a module to help students learn more about Gen AI tools and how to use them confidently, effectively, and responsibly in their work. But how are students being supported in the classroom? And how is the use of Gen AI being tailored to their specific discipline?

Despite the prevalent stereotype that students are digital natives and naturally will know how to use and work with Gen AI tools, this isn’t the case and students require scaffolded support and guidance to ensure they leverage the tool in the most beneficial way, and are taught how Gen AI will impact their future in the workplace.

This case study, with Dr. Ioannis Georgilas, looks at the guidance and support on Gen AI he provides the students in his faculty. This semester, Dr. Georgilas has run lectures in his third-year Integrated Design Engineering: Mechatronic Design Project I unit, his fourth-year Integrated Design Engineering: Mechatronic Design Project II unit, and in the first-year Mechanical Engineering, Responsible Engineering Practice (REP) unit.


What made you decide this new approach? What were you hoping to achieve?

“My decision was primarily influenced by the ubiquity and evolving role of Generative AI (GenAI) in the current technological landscape. Recognizing that GenAI’s widespread availability marks a point of no return, I engaged in discussions with leaders in the Software Development Industry. These conversations revealed a consensus that the skills demanded by the industry are rapidly shifting towards the integration of GenAI tools in software development. This shift is not confined to software development alone but is reflective of broader changes across various engineering domains.

Understanding this, my objective was to restructure our students’ approach to learning and engaging with these emerging tools. I aimed to guide them towards systematic and critical engagement with GenAI technologies. Tools like Microsoft Word, for example, offer ease of use for writing but can instill habits that, while initially convenient, may prove detrimental to developing deeper, more disciplined skill sets. Such habits can be hard to unlearn, underscoring the importance of a thoughtful introduction to technology.

My aim was to pivot our focus from merely coding to the approach of framing and modelling problems. This shift underscores the belief that while code is important, the true skill lies in the ability to conceptualize and define the problem space. With this approach, code becomes a tool to implement solutions, not the end goal itself.

Additionally, by incorporating GenAI into our curriculum, I sought to reduce the amount of time spent addressing rudimentary coding questions in my studio. Instead, I envisioned reallocating that time towards discussions on high-level, strategic planning and design. This not only elevates the quality of our academic discourse but also prepares our students for the complexities of modern engineering challenges. By emphasizing problem-solving, critical thinking, and strategic planning, we are preparing our students not just to navigate but to excel in a future where GenAI plays a central role in engineering and beyond.”

– Dr. Ioannis Georgilas, edited by ChatGPT4 😉


4 slides from Dr. Georgilas's presentation with the topic headings:
1. Disruptive Innovations in Engineering (recent)
2. Where is the Catch?
3. The quote: The danger of AI is not that it’s going to rebel against us, it’s that it’s going to do exactly what we ask it to do. So then the trick of working with AI becomes: How do we set up the problem so that it actually does what we want?" - Janelle Shane
4. Correct Text
4 slides from Dr. Georgilas’s presentation.

Dr. Georgilas ran lectures with the different year groups to highlight to students the impact of Gen AI to engineering skills, the positives and negatives of using Gen AI tools – and how this impacts students, how Gen AI fits in with other disruptive innovations in Engineering, and ways students can use Gen AI for coding and report writing.

The sessions emphasised to students the value of their core skills and how Gen AI can be used in collaboration with what they already know. Along with demonstrating to students how Gen AI tools can be used, Dr. Georgilas highlighted to students the University’s Assessment Categories for coursework and Gen AI use, and used time in the session to answer questions from the students so they had a clearer understanding of what was permitted for their assignments.

These sessions provided students with the guidance they needed to see how Gen AI could benefit them and how their engineering skills cannot be replaced, but supported by Gen AI. Dr. Georgilas shared examples of how using Gen AI tools, such as GitHub Copilot, has reduced the amount of time he spends debugging code and has therefore given him more time to design code.


“The inclusion of AI teaching was very useful for the unit and future projects.”

“Useful and relevant, especially stuff on AI”

– Quotes from students taken from OUEs

Overall, students left the lectures with a better understanding of the positives and negatives of using Gen AI in their work, such as the importance of independent thinking and using and knowing how to critically look at the work produced in collaboration with Gen AI.


For those looking to introduce Gen AI into their curriculum, Ioannis does advice that it will take time to plan it, test it and rehearse it. In the future, he also plans to run more tutorial-like sessions, which will be more hand-on so students can try the tools.

During the lecture Dr. Georgilas also incorporated tools such as Mentimeter to get a better understanding of what Gen AI tools students have used before and ways students have used Gen AI in their work.

With consideration to consistency and scaffolding across different units, Dr. Georgilas was asked by colleagues to also run the session to units that he was not the lead teacher in, thus reinforcing a coherent course-wide, team teaching approach to learing content and skills across and between units.

Article published: 08 April 2024

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