GENERATIVE ARTIFICIAL INTELLIGENCE AND UNDERGRADUATE RESEARCH: EXPERIENCES FROM RESEARCH PROJECT SUPERVISION IN A NIGERIAN COLLEGE OF EDUCATION

Abstract

Generative artificial intelligence (GenAI) is progressively influencing the methods by which students in higher education search for information, formulate concepts, and compose scholarly articles. Nevertheless, there is limited data regarding the influence of GenAI on undergraduate research methodologies at Nigerian Colleges of Education. This research investigates the application of GenAI in the formulation of undergraduate research projects, drawing from the researcher's experience overseeing 28 students across three academic terms at a Nigerian College of Education. A qualitative practitioner-inquiry methodology was employed in the research. Data were gathered from students' study concepts and their subsequent project drafts, supervisory observations and remarks, casual conversations about AI utilisation, relevant WhatsApp communications concerning project tasks, and reflective journals kept during supervision. The content was thematically analysed to identify patterns in students' AI-assisted study techniques. Four topics surfaced: applications of GenAI within the study methodology; academic and linguistic advantages; epistemological, ethical, and dependency-related risks; and consequences for research oversight and evaluation. GenAI proved beneficial for ideation, topic enhancement, elucidating concepts, condensing information, and enhancing linguistic expression. Students additionally encountered fabricated citations, uncritical acceptance of produced material, diminished interaction with primary sources, challenges in accessing digitally obscure local scholarship, and reliance on AI for tasks intended to foster autonomous research skills. The findings indicate that effective integration of GenAI necessitates AI literacy encompassing critical evaluation, verification of facts, contextual comprehension, ethical judgement, and intellectual accountability. The study advocates for process-focused oversight, AI-informed evaluation, and contextually relevant institutional policies for the responsible use of GenAI in undergraduate research.