EXPLORING GENERATIVE AI IN LEARNING BIOLOGICAL DIVERSITY: PERSPECTIVES OF PRE-SERVICE SCIENCE TEACHERS
DOI:
https://doi.org/10.35631/IJEPC.1164018Keywords:
AI Literacy, Biological Diversity, Epistemic Vigilance, Generative Artificial Intelligence (Genai), Pre-Service Science Teachers, Science EducationAbstract
This study investigated the patterns of use, pedagogical roles, challenges, and ethical considerations of generative artificial intelligence (GenAI) among pre-service science teachers in learning Biological Diversity. A mixed-method case study design was conducted involving 15 Year 1 pre-service science teachers enrolled in a 14-week course at the Institute of Teacher Education, Penang Campus, Malaysia. Data were collected via an electronic mixed-method questionnaire comprising 5-point Likert scale items and open-ended questions. Quantitative data were analysed using descriptive statistics, while qualitative responses were analysed using thematic analysis. Results showed universal GenAI adoption (), with ChatGPT identified as the primary tool () alongside Google Gemini (). Participants predominantly used GenAI for speciation understanding (), concept explanation (), and taxonomy classification (). Highly positive perceptions were reported regarding GenAI's role in connecting real-world issues, simplifying complex concepts, and aiding self-paced learning. However, major challenges included concerns over scientific accuracy (), potential over-reliance reducing independent thinking (), and over-simplification of technical procedural knowledge. Consequently, of participants reported actively verifying AI outputs against textbooks and scientific literature. The study concludes that while GenAI is a valuable supplementary tool for biological diversity education, its successful implementation requires guided instruction that prioritizes epistemic vigilance, critical thinking, and ethical AI literacy.
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References
Albadarin, Y., Saqr, M., Pope, N., et al. (2024). A systematic literature review of empirical research on ChatGPT in education. Discover Education, 3(60). https://doi.org/10.1007/s44217-024-00138-2.
Almasri, F. (2024). Artificial intelligence in science education: Enhancing conceptual understanding and engagement. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10176-3.
Chan, C. K. Y.., & Hu, W.. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Education and Information Technologies, 29(7), 1–21.
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating? Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8.
Deng, J., Chen, X., & Liu, Y. (2024). Ethical implications of generative artificial intelligence in higher education. Educational Technology Research and Development. https://doi.org/10.1007/s11423-024-10345-8.
Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). “So wh.at if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the learning sciences. Springer.
Kasneci, E., Sessler, K., Küchemann, S., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274.
Krause, M., Pietzner, V., & Fischer, H. (2025). Pre-service teachers’ AI literacy and its implications for classroom practice. Computers & Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2025.100215.
Lee, H., & Zhai, X. (2024). Generative artificial intelligence in science education: Opportunities for teaching and learning. Research in Science Education. https://doi.org/10.1007/s11165-024-10174-5.
Luckin, R., & Holmes, W. (2022). Intelligence unleashed: An argument for AI in education. Pearson Education.
Nyaaba, M., Shi, L., & Mensah, R. (2024). Pre-service teachers’ use of generative AI as a learning assistant in science education. https://arxiv.org/abs/2407.11983.
Porayska-Pomsta, K., Holmes, W., & Nemorin, S. (2024). Ethical considerations in artificial intelligence in education. arXiv. https://arxiv.org/abs/2406.11842.
Rahmawati, K. S. N., Suciptaningsih, O. A., & Anggraini, A. E. (2025). Generative artificial intelligence in education: Opportunities, risks, and ethical challenges. Journal of Innovation and Research in Primary Education. https://doi.org/10.56916/jirpe.v5i1.3106.
Spasopoulos, T., Sotiropoulos, D. J., & Kalogiannakis, M. (2025). Generative AI in pre-service science teacher education: A systematic review. Advances in Mobile Learning Educational Research, 5(2), 1501–1523. https://doi.org/10.25082/AMLER.2025.02.007.
Tlili, A., Shehata, B., Adarkwah, M. A., et al. (2023).What if the devil is my guardian angel:ChatGPT as a case study of using chatbots in education.Smart Learning Environments,10(1), 15.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://www.unesco.org.
Wang, X., Zainuddin, Z., & Leng, C. H. (2025). Generative artificial intelligence in pedagogical practices: A systematic review of empirical studies. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2485499.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27.
