Assessing Undergraduates' Perceptions of Use of AI Generative Tools for Academic Activities in Adeyemi Federal University of Education, Ondo, Ondo State, Nigeria
The increasing integration of Artificial Intelligence (AI) generative tools in higher education has transformed academic activities, presenting both opportunities and challenges. This study assesses undergraduates’ perceptions of use of AI generative tools for academic activities at Adeyemi Federal University of Education, Ondo, Nigeria. Five research questions guided the study, focusing on common AI generative tools, appropriate and inappropriate uses, benefits, and challenges. A survey research design was adopted, and 250 students were randomly selected from five faculties using simple random sampling technique. Data were collected using a researchers’ self-constructed questionnaire with an internal consistency of 0.79 using Cronbach Alpha and analysed using descriptive statistics of frequency count, percentage and mean. Findings revealed that, students widely use AI tools, with Quillbot, Grammarly, Meta AI and ChatGPT being the most common. Students acknowledged appropriate uses such as drafting, editing, and research assistance, while also demonstrating high acceptance of inappropriate uses, including examination malpractice and data falsification. AI was perceived as beneficial in enhancing collaboration, accessibility, and writing proficiency but posed challenges related to overreliance, biases, and ethical concerns. The study recommended among others the implementation of institutional policies, AI literacy programmes, faculty guidance, and stricter measures against AI misuse to ensure responsible AI adoption in academic settings.
Authors
- Adebisi A. Olorunfemi
- Akinyemi Olufunminiyi Akinbobola (ORCID: https://orcid.org/0000-0002-9473-2055)
- Ojuri Sunday Omojuwa
Publication Details
- Journal
- Middle East Research Journal of Humanities and Social Sciences
- Published
- 2025-10-03
- DOI
- https://doi.org/10.36348/merjhss.2025.v05i05.003
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00