Abstract :

Artificial Intelligence Is Starting To Reshape The Educational Practice And Become More Responsive, Data-driven, And Learner-focused Across A Significantly Wider Scope Of Instructions. Traditional Uniform Teaching Methods May Be Ineffective In Meeting The Needs Of The Various Students In Life Science Classrooms Where Students Can Be Of Differing Levels Of Prior Knowledge, They Can Have Different Learning Styles, And Conceptual Clarity Levels. In This Paper, We Will Be Discussing The Design Of An AI-based Adaptive Instructional Model, One That Relies On Learning Analytics To Enhance Individualization, Student Interaction, And Conceptual Comprehension. The Paper Seeks To Explore The Way Adaptive Systems That Use AI Can Assist Teachers In Modifying Content, Delivery Pace, Assessment, And Feedback Based On Student Performance And Interaction. It Is Suggested That An Analytical Framework Based On Learning Analytics Will Be Used To Capture The Student Behavior And Identify The Gaps In The Learning And Direct Real-time Adjustment In The Instructional Plans Of Life Science Learning. It Is Believed That The Model Will Help To Enhance Learner Engagement, Maintain Their Academic Interest, And Increase The Knowledge Base About The Fundamental Concepts In Biology By Providing Customized Learning Opportunities And Support In A Reasonable Time. It Is Also Aimed At Helping Educators To Make Evidence-based Pedagogical Choices Based On Data Dashboards And Predictive Insights. The Importance Of The Research Has Been Suggested To Address The Gap Between Educational Technology And Classroom Pedagogy Which Provides The Subject Specific, Scaling And Practical Model Of Enhancing Teaching Effectiveness And Student Learning Outcome In Life Science Classrooms.

Published:

10-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

204-217

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite