Undergraduate Students’ Perception of the Role of Generative AI in Biology Education
Authors
Department of Educational Psychology and Counseling, Adeyemi Federal University of Education, Ondo (Nigeria)
Article Information
DOI: 10.51584/IJRIAS.2026.11080048
Subject Category: Education
Volume/Issue: 11/8 | Page No: 643-650
Publication Timeline
Submitted: 2026-03-15
Accepted: 2026-03-20
Published: 2026-09-03
Abstract
This study explored undergraduate students' views on Gen AI, focusing on their perceived advantages in improving understanding of complex biological concepts and their concerns about the tools' potential inaccuracies and biases in academic work. A descriptive research design was used and the study was guided by two research questions. The population of the study was all undergraduate students from all universities in Ondo and Oyo States. The sample size of the study was 1,360 undergraduate students randomly selected from five universities in Ondo and Oyo States. A self-designed questionnaire was employed to gather data for the study. It was adequately validated. Data collected were analyzed using mean score. The results indicate a generally positive perception of these tools in enhancing students’ understanding of complex biological content (M=3.26, SD=1.95), particularly in terms of their ability to help students better visualize difficult biological concepts and complete biology assignments on time. However, the study also highlights concerns about the potential biases and inaccuracies of Gen AI. Undergraduate students showed moderate awareness of these biases (M=2.09, SD=1.78), particularly regarding the limited emotional intelligence of these tools, which may result in inappropriate outputs. It is recommended that higher education institutions establish resource libraries or toolkits offering tutorials, guides, and case studies on effectively using AI tools for learning. Additionally, they should provide dedicated courses or modules on the ethical implications of AI, covering issues related to bias, fairness, and transparency.
Keywords
Generative Artificial intelligence tools (Gen AI), perception, Bias, Artificial intelligence (AI)
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References
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