Authors : Souvik Chakraborty; Dr. Harikrishnan M.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/yuedxrau

Scribd : https://tinyurl.com/yf45s87z

DOI : https://doi.org/10.38124/ijisrt/26mar1073

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : With the emergence of Artificial Intelligence (AI), learning can be personalized, intelligent tutoring is now possible, predictive analytics can be generated, and automated assessment can be performed. AI technologies in the teaching of life-sciences, including, but not limited to, machine learning, virtual laboratories, adaptive learning environments, and bioinformatics tools, can play a major role in supporting education in teaching, scientific research training, and scientific learning based on data. Even with such benefits, AI has not been successfully integrated in the life-sciences curricula across most institutions of higher learning. Various barriers such as technology infrastructure, inadequate training of faculty, institutional support, ethical issues of privacy of data, and the resistance to technological change, among others, have remained a hindrance to proper implementation. The proposed paper will focus on the existing impediments and facilitating elements that affect AI implementation in life-sciences education. The mixed-methods research design was taken to gain an in-depth perspective on the issue. The quantitative data were gathered using a structured questionnaire of life-science students and faculty members and the qualitative data were collected using semi-structured interviews with educators and academic administrators. The results have shown that the key obstacles are the lack of AI literacy among teachers, institutional insufficiency, academic integrity issues, and the absence of explicit educational policies assisting the implementation of AI. On the other hand, it has named institutional investment in digital infrastructure, faculty professional development programs, interdisciplinary collaboration, and supportive educational policies as key enablers to learners using AI to drive learning. The paper makes a case of the significance of strategic planning and institutional readiness in effective adoption of AI in the field of life-sciences education. The findings offer practical implications to teachers, administrators and policymakers who want to incorporate AI technologies in the teaching and learning process in order to improve learning outcomes and equip students with the ability to do scientific research with the help of AI.

Keywords : Artificial Intelligence, Life Sciences Education, Educational Technology Adoption, AI Integration, Mixed-Methods Research.

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