A STUDY TEACHER-STUDENT-AI TRRIAD & REDEFINING COLLABORATIVE MENTORSHIP THROUGH AI-DRIVEN ADAPTIVE LEARNING IN UNDERGRADUATE EDUCATION

The integration of Artificial Intelligence (AI) in education is transforming traditional pedagogical approaches, 
necessitating a reevaluation of collaborative mentorship models in undergraduate education. The objective of 
present research is the Teacher–Student–AI triad, focusing on how AI-driven adaptive learning systems can 
redefine collaborative mentorship. AI technologies offer personalized learning experiences, real-time feedback, 
and data-driven insights, enabling tailored support for students. However, the role of teachers and the nature of 
student engagement must be recalibrated to maximize the potential of AI. The present research is a descriptive 
research design for the study purpose 100 students were taken as sample who are under graduate students from 
college located in the western suburban i.e. Andheri to Borivali. This study investigates the interplay between 
teachers, students, and AI systems, examining how AI augments mentorship, alters classroom dynamics, and 
impacts student outcomes. Through a mixed-methods approach involving surveys, questioner, and face to face 
interaction to undergraduate students of colleges. The research identifies best practices for leveraging AI to 
enhance collaborative learning environments. The finding of study revealed that under- graduate college 
students highlight the potential of AI to facilitate more effective, data-informed mentorship while emphasizing 
the irreplaceable role of human educators in guiding and inspiring students. The study concludes with 
recommendations for educators, policymakers, and Ed Tech developers on integrating AI tools to foster 
adaptive, student-centered learning ecosystems.