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GENERATIVE ARTIFICIAL INTELLIGENCE (AI) IN HIGHER EDUCATION IS HERE TO STAY: INCORPORATING THE USE OF LARGE LANGUAGE MODELS (LLMS) IN UNDERGRADUATE NEUROSCIENCE MODULES’ IN-COURSE WORK ASSESSMENT AT KING'S COLLEGE LONDON
King's College London (UNITED KINGDOM)
About this paper:
Appears in: ICERI2024 Proceedings
Publication year: 2024
Page: 6492 (abstract only)
ISBN: 978-84-09-63010-3
ISSN: 2340-1095
doi: 10.21125/iceri.2024.1569
Conference name: 17th annual International Conference of Education, Research and Innovation
Dates: 11-13 November, 2024
Location: Seville, Spain
Abstract:
The relatively recent explosion in the use of generative artificial intelligence (AI) in almost any human field, has led to a wide range of positions on how to deal with it in Higher Education (HE) worldwide. HE institutions went from banning it altogether to trying to embed it in their courses. I am in favour of harnessing its potential and give my students the necessary skills needed to use generative AI wisely and critically. It has been stated that 80% of jobs in the US alone will be affected by ChatGPT and other similar models. UNESCO, which coordinates the Education 2030 Agenda, published in April 2023 a Quick Start Guide giving an overview of how ChatGPT works, explaining how it can be used in HE; here the main challenges and ethical implications are discussed and practical steps that HE institutions can take to address these are proposed. In brief, both staff and students need to become proficient in the use and understanding of AI; forms of assessment need to be reviewed to ensure they are still fit for purpose; clear guidance needs to be provided to students about how and when AI tools can be used; AI should be linked to course/programme learning outcomes; improve the queries/prompts posed to AI tools to get from them the most useful and relevant outcome. As Prof Acar (Harvard University and King’s College London) recently proposed, the following five skills are needed to teach students to use AI effectively (the PAIR framework):
1) problem formulation,
2) AI Exploration,
3) interaction and experimentation,
4) critical thinking and
5) willingness to reflect.

Methods:
During the academic year 2023/2024 I have introduced the use of a modified PAIR framework in both my 3rd Year undergraduate Neuroscience modules (Behavioural science and Perspectives on Pain and Nervous system disorders) at King’s College London (KCL). Each module is attended by 120 students. I have introduced the PAIR framework and the proposed assignment with class workshops aimed at getting the students familiarise with the task and explain to them the proposed aim to integrate generative AI in the module focusing on the development of transferable skills for an AI-driven world. The students were then asked to produce a 2,000-word essay (peer-reviewed before submission by two students) in their own words; then they had to ask the AI tool to write an essay with the same title, record prompts used, tools explored, chosen or discarded and then write a 500-word reflection on the comparison between the two essays. Due to the variety of existing AI tools and to ensure accessibility and an inclusive learning environment, the students have been asked to use tools available from KCL subscriptions thus avoiding using those which have a paywall.

Results:
I have marked all the 240 own students’ essays, read the AI-produced ones and the reflections. I found out a huge variability in the proficiency with which students were able to obtain essays from their chosen AI tool; the reflective piece was by far the most interesting one to read, overall showing a high level of students’ enthusiasm and engagement with the task. I am also still in the process of analysing the huge students’ output from a qualitative point of view and will present an initial outcome at ICERI2024. In conclusion I strongly support embedding the use of generative AI in HE assessment as this will provide students with essential transferable skills supporting them in their future careers once graduated.
Keywords:
Generative Artificial Intelligence, Higher Education, Assessment for learning, PAIR framework.