Presentation Schedule
A Quantitative Pilot Study of the Application of Generative Artificial Intelligence in Life Science Literature Research (108889)
Friday, 10 July 2026 15:30
Session: Poster Session 2
Room: Brunei Gallery (Ground Floor)
Presentation Type:Poster Presentation
The rapid advancement of generative artificial intelligence (genAI) has opened new opportunities for supporting literature research in life science education. This quantitative pilot study evaluated the use of a genAI‑based tutor to assist undergraduate students in four key aspects of literature research: brainstorming, information searching, fact‑checking, and proof-reading. GenAI offers notable advantages, including its capacity to process large volumes of scientific text and to provide intuitive, conversational support without requiring specific technical terms for searching. Nevertheless, its database‑dependent nature highlights limitations in accuracy/currency of information, and potential risks of propagating errors, underscoring the need for user alertness. Thirty‑five life science undergraduates participated in a structured training session introducing the AI‑tutor, after which they evaluated its performance using a 10‑point Likert scale. Students reported prior experience with genAI (mean baseline knowledge 5.94 ± 0.33) and rated the AI‑tutor positively across all applications. Brainstorming and information searching both achieved mean scores of 6.57, reflecting the usefulness of natural‑language prompts in generating research ideas and navigating the literature. Fact‑checking received a moderate score of 6.06, consistent with known limitations of LLM‑generated factual accuracy. Proofreading was the strongest domain (mean 7.00), confirming the value of genAI for enhancing clarity, grammar, and referencing consistency. Taken together, this study demonstrates the potential of genAI to complement traditional approaches to literature research in life science education. Its intuitive, student‑centred design could offer broader relevance across disciplines, provided that genAI tools are applied critically and supported by systematic training.
Authors:
Fai-hang Lo, The Chinese University of Hong Kong, Hong Kong
About the Presenter(s)
Dr FH Lo is currently a Lecturer in the School of Life Sciences at the Chinese University of Hong Kong.
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