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Abstract
As artificial intelligence (AI) continues to shape education, its role in self-directed learning, particularly in biology, remains underexplored. This study investigates AI adoption among high school students in Ho Chi Minh City using the technology acceptance model (TAM), integrating structural equation modeling, thematic analysis, and latent Dirichlet allocation topic modeling. Findings challenge traditional TAM assumptions, revealing that while perceived usefulness positively influences attitude toward use, it does not significantly predict behavioral intention. Additionally, perceived ease of use is not a strong predictor of AI adoption, highlighting the unique demands of biology education. A key finding is that adoption constraints, including concerns about AI accuracy, privacy, and limited experimental capabilities, significantly hinder AI adoption. Students predominantly use AI for exam preparation and homework support, rather than for exploratory learning or experimental simulations. AI-based quizzes are perceived as the most useful, whereas open-ended AI chatbots are less engaging for biology learning. The study underscores that biology students require AI tools that extend beyond theoretical learning to include laboratory simulations, data analysis, and experimental design support. To enhance AI adoption, this study recommends AI literacy programs for students and educators, the development of AI-driven virtual laboratory tools, and improved accuracy and reliability of AI-generated biological content. Institutional policies should support AI integration by ensuring accessibility, training, and regulatory oversight. By addressing these limitations, AI can transition from a passive knowledge provider to an active facilitator of scientific discovery in biology education.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 21, Issue 10, 2025, Article No: em2716
https://doi.org/10.29333/ejmste/17158
Publication date: 01 Oct 2025
Online publication date: 24 Sep 2025
Article Views: 28
Article Downloads: 10
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