[This is an excerpt from a longer article by Dr Brett A Becker and Dr Brian MacNamee (pictured), published by the UCD School of Computer Science in February 2024. Dr Becker has sadly passed away since this article was originally published. You can read more about Dr Becker’s life and work here]

Is Generative AI over-hyped or is it going to revolutionise education? In this article Dr Brian MacNamee and Dr Brett A Becker* summarise the potential impacts of GenAI in the domain of education – specifically computing education – in terms of what has been done so far, as well as challenges and opportunities to come. They also ask ChatGPT for its outlook.

In December 2023 Yasmin B. Arafat – a developer of the hugely popular visual programming language Scratch – said in her ACM CompEd2 keynote address that in her time there has never been a conversation that has so ubiquitously dominated the education community as the conversation about GenAI. Perhaps that is not surprising when you consider that PCs took decades to come into homes and revolutionise our lives, and that the internet was also a relatively slow-burner, taking years to truly impact society. Even then, personal computing and the internet largely supported us in doing the same things we’ve always done – communicating, moving money around, getting work done, finding entertainment, etc. In much less time than this, GenAI has eaten the internet for breakfast and accomplished something that the PC and internet could not really do – generate content.

The “assessment apocalypse”, where many educators felt that GenAI should be banned seems to have simmered down – after all cheating is not a new phenomenon and GenAI was just yet another means to the same end. However it is clear that that university policy needs to directly address the impacts of GenAI on policy including academic integrity and that this needs to be clearly articulated to students. Filling the void that the initial panic has left are opportunities where GenAI can seriously impact teaching and learning beyond, but very much including, programming.

For starters we need to understand more about how students interact with GenAI and provide tooling and strategies to achieve effective use. On the classroom practice front, it is very likely that in a few short years GenAI will be powering virtual Teaching Assistants that are always available, always approachable, and never get tired of hearing the same questions over and over. Most likely these will be fine-tuned on a specific module or lecturer’s notes and other materials so that responses are tailored for a specific student’s context. Trials are already underway, for instance in Harvard’s CS50 course.

What about the concerns? A very incomplete list of broad-ranging concerns span bias, ethics, and equity. In education there is also the issue of how GenAI affects existing educational processes and structures, too many of which are (finally, unignorably) no longer fit for purpose.

It is also possible that GenAI will change who studies what (including Computer Science). This could be good, or bad, or both. The economy and media are big factors in influencing student interest, and GenAI has impacted both of these.

New gaps and disparities could arise in terms of equity and participation, such as the development of an AI-divide. Topics such as these are where the conversation is turning to currently.

What do we do if GenAI-assisted students start to ace all of their material? Grade harder? Cover more content more quickly? Completely rethink how assessment in higher education works? Options such as these would have drastic effects on the curriculum as well as raising profound professional, economic, societal, ethical and other issues.

Perhaps GenAI isn’t over-hyped at all when it comes to education. Could it be that GenAI is a saviour, here to catalyse long overdue change in a system that needed a massive overhaul in the first place? Ask ChatGPT. We just did. Here’s what it said – verbatim:

‘It’s an interesting perspective to consider GenAI as a catalyst for change in the education system. The potential of generative AI in education is quite significant. Here are a few ways it could impact the sector:

 Personalized Learning: GenAI can tailor educational content to individual student’s needs, learning styles, and pace. This personalization could revolutionize how students learn, making education more effective and engaging.

 Access to Information: AI can provide students and educators with vast amounts of information and resources, breaking down barriers to education, especially in under-resourced areas.

 Interactive Learning Tools: AI-powered tools can create interactive and immersive learning experiences, making education more engaging and practical.

 Assessment and Feedback: AI can offer more timely and detailed feedback on student work, aiding in faster and more effective learning.

 Teacher Assistance: AI can handle administrative tasks and even assist in creating lesson plans, allowing teachers more time to focus on teaching and personal interactions with students.

 Lifelong Learning: AI facilitates continuous learning outside of traditional education systems, supporting lifelong education and skill development.

 However, it’s also important to approach this with caution. Challenges such as ensuring data privacy, preventing biases in AI algorithms, and maintaining the human element in education are crucial. The integration of GenAI in education should be done thoughtfully to enhance the learning experience without replacing the invaluable human elements of teaching and mentorship.

Not too shabby! Perhaps the future does look bright – but like normal, long-term gain often involves short-term pain.