Artificial intelligence in biology education: Perspectives of pre-service biology teachers

Abstract

This study explores the attitudes, perceptions, and expectations of pre-service biology teachers regarding the integration of artificial intelligence (AI) in the educational process. Using a combination of case studies and student-created visualizations, the research examines how AI can support teaching and learning in biology. The findings indicate that students view AI as a pedagogical assistant that enhances the visualization of complex biological processes, facilitates personalized learning, and promotes student engagement. At the same time, participants demonstrated a critical understanding of potential risks, including over-reliance on technology and the need for critical evaluation of AI-generated content. The results highlight the importance of an active teacher role and the development of both digital and pedagogical competencies in future educators. Overall, the study suggests that AI has significant potential to enrich biology education when implemented as a complementary tool within a balanced, pedagogically guided approach.   References: Acarli D. S. 2026. The role of artificial intelligence in biology education: What do future educators think? – In: Abdelaal R. M. S. & Kayyali M. (Eds), AI-powered pedagogy, academic transformation, and faculty empowerment, IGI Global Scientific Publishing, 265–287. https://doi.org/10.4018/979-8-3373-9220-2.ch011 Bakirova A., Omarova R. & Baimaganbetova M. 2025. Developing students’ research skills through the integration of artificial intelligence in biology lessons. - Eurasian Science Review 3 (7): 104-111. https://doi.org/10.63034/esr-565 Filiz O., Kaya M. H. & Adiguzel T. 2025. Teachers and AI: Understanding the factors influencing AI integration in K-12 education. – Education and Information Technologies 30: 17931-17967. https://doi.org/10.1007/s10639025134632 Granström M. & Oppi P. 2025. Assessing teachers’ readiness and perceived usefulness of AI in education: An Estonian perspective. - Frontiers in Education 10: 1622240. https://doi.org/10.3389/feduc.2025.1622240 Guan L., Y. Zhang & Gu M. M. 2024. Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. – Computers and Education: Artificial Intelligence 8: 100341. https://doi.org/10.1016/j.caeai.2024.100341 Halhaji P. N. & Murniati N. 2025. Biology education students’ readiness for AI based learning through TPACK. – Jurnal IPA Terpadu 9 (3): 100–115. https://doi.org/10.35580/jit.v9i3.10030 Huang L., Zhang Y. & Li M. 2024. Applications of AI in biological classification and data analysis. – Advances in Computational Biology Education 11 (3): 140–160. Kotsis K. T. 2025. Misconceptions about artificial intelligence from preservice teachers: A literature review. – EIKI Journal of Effective Teaching Methods (JETM) 3 (2): 199-218. Kurniawan A., Hariyadi S., Prabowo A. S. & Savira N. I. I. 2024. Perceptions of pre service and in service biology teachers on artificial intelligence in biology learning. – International Journal of Biology Education Towards Sustainable Development 4 (1): 1–15. https://doi.org/10.53889/ijbetsd.v4i1.432 Lee G., Yun M., Zhai X. & Crippen K. 2025. Artificial intelligence in science education research: Current states and challenges. - Journal of Science Education and Technology 34. https://doi.org/10.1007/s10956-025-10239-8 Mnguni L., Nuangchalerm P., El Islami R. A. Z., Sibanda D., Ramulumo M. & Sari I. J. 2024. AI integration in biology education: Comparative insights into perceived benefits and TPACK among South African and Indonesian pre-service teachers. - Asia-Pacific Science Education 10 (2): 381-410. https://doi.org/10.1163/23641177-bja10086 Tan K.-H. & Lim B. P. 2018. The artificial intelligence renaissance: Deep learning and the road to human-level machine intelligence. - APSIPA Transactions on Signal and Information Processing 7 (1): e6. https://doi.org/10.1017/ATSIP.2018.6 Taylor L., Gupta V. & Jung K. 2024. Leveraging visualization and machine learning techniques in education: A case study of K-12 state assessment data. - Multimodal Technologies and Interaction 8 (4): 28. https://doi.org/10.3390/mti8040028 Telaumbanua T. 2025. The role of artificial intelligence in improving the quality of biology learning. - Cognizance Journal of Multidisciplinary Studies 5 (1): 78–84. https://doi.org/10.47760/cognizance.2025.v05i01.007 Vinci V., Agrati L. S., Berardi P. & Beri A. 2026. Artificial intelligence as a catalyst for transformative assessment: designing teacher literacy at the crossroads of ethics, pedagogy, and human relationships. - Frontiers in Education 11: 1760626. https://doi.org/10.3389/feduc.2026.1760626 Wahyuni A., Junaidi A., Mansur S., Sriadhi S. & Halimatun F. 2025. The role of AI based chatbots in supporting biology education students’ learning. - Journal of Biology Education Research 6 (2): 140–148. https://doi.org/10.55215/jber.v6i2.82