Generative Artificial Intelligence as Assistive Technology in Life Science Education: An Analytical Examination of Accessibility, Cognitive Support, and Inclusive Pedagogy
DOI:
https://doi.org/10.55544/sjmars.5.2.2Keywords:
Generative Artificial Intelligence, Assistive Technology, Life Science Education, Accessibility, Cognitive Support, Inclusive Pedagogy, Universal Design for Learning, Higher EducationAbstract
Generative Artificial Intelligence (GAI) has become an important development in the field of higher education giving new opportunities to accessibility, cognitive support, and inclusive learning. Students with varied language, socio-economic, and cognitive backgrounds often face chronic learning challenges in learning life science -a field that has complex concepts, abstract biological operations and high cognitive load. The paper analytically reviews the involvement of the generative AI as an assistive technology in life science education and how it could be used to improve accessibility, aid cognitive processes, and support the inclusive design of instruction. The study is based on the qualitative, non-interventionist, and theory-based approach, relying on peer-reviewed articles (2018 to 2025), international policy resources, and accessibility and AI ethics models in higher education. Patterns associated with accessibility features, cognitive scaffolding mechanisms, pedagogical inclusiveness and ethical constraints are studied with the help of thematic content analysis. Based on the analysis, generative AI can be used as a dynamic system of assistance with multilingual clarifications, generative representations, a customised pacing strategy, and step-by-step conceptual scaffolding in accordance with Universal Design of Learning and Cognitive Load Theory. Even so, the results also point to more severe issues, such as an algorithmic bias, disparity in access, and the threat to the learner's epistemic agency. The paper concludes with the assertions that ethical governance, pedagogical alignment and accessibility-oriented design of generative AI are subject to the educational value and that further mixed-method and empirical classroom research are needed to lend conceptual arguments, credence and support evidence-based practice.
References
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 610–623.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
CAST. (2018). Universal Design for Learning guidelines version 2.2. Center for Applied Special Technology.
Creswell, J. W. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.
Felder, R. M., & Brent, R. (2016). Teaching and learning STEM: A practical guide. Jossey-Bass Higher and Adult Education Series.
Floridi, L. (2023). Ethics, governance, and policies in artificial intelligence. Springer.
Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
Giannakos, M. N., Sharma, K., Papavlasopoulou, S., & Pappas, I. O. (2024). Generative AI in education: Opportunities, challenges, and future directions. Computers & Education: Artificial Intelligence, 5, 100145.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W. (2024). Ethical and pedagogical considerations for generative AI in higher education. British Journal of Educational Technology, 55(1), 15–29.
Jeon, J., & Lee, S. (2025). Generative AI for multilingual and accessible learning environments. Educational Technology Research and Development, 73(1), 89–107.
Kooli, C. (2025). Artificial intelligence and inclusive education: Transforming assistive technologies. International Journal of Educational Technology in Higher Education, 22(1), 1–19.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
Mayer, R. E. (2020). Multimedia learning (3rd ed.). Cambridge University Press.
Mayer, R. E. (2023). Evidence-based principles for generative AI in education. Educational Psychology Review, 35(2), 421–445.
OECD. (2024). Artificial intelligence in education: Challenges and opportunities. OECD Publishing.
Park, Y., & Kim, H. (2025). A systematic review of generative AI applications in higher education. Computers & Education, 195, 104690.
Selwyn, N. (2023). Should robots replace teachers? AI and the future of education. Polity Press.
UNESCO. (2023). Guidance on generative AI in education and research. United Nations Educational, Scientific and Cultural Organization.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Stallion Journal for Multidisciplinary Associated Research Studies
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
10.55544/sjmars
editor@sjmars.com | 
+91-8874396939
