A STUDY TEACHER-STUDENT-AI TRRIAD & REDEFINING COLLABORATIVE MENTORSHIP THROUGH AI-DRIVEN ADAPTIVE LEARNING IN UNDERGRADUATE EDUCATION
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866902002737348608 |
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| author | Mr. Sanjay C Raval |
| author_facet | Mr. Sanjay C Raval |
| contents | <p>The integration of Artificial Intelligence (AI) in education is transforming traditional pedagogical approaches, <br>necessitating a reevaluation of collaborative mentorship models in undergraduate education. The objective of <br>present research is the Teacher–Student–AI triad, focusing on how AI-driven adaptive learning systems can <br>redefine collaborative mentorship. AI technologies offer personalized learning experiences, real-time feedback, <br>and data-driven insights, enabling tailored support for students. However, the role of teachers and the nature of <br>student engagement must be recalibrated to maximize the potential of AI. The present research is a descriptive <br>research design for the study purpose 100 students were taken as sample who are under graduate students from <br>college located in the western suburban i.e. Andheri to Borivali. This study investigates the interplay between <br>teachers, students, and AI systems, examining how AI augments mentorship, alters classroom dynamics, and <br>impacts student outcomes. Through a mixed-methods approach involving surveys, questioner, and face to face <br>interaction to undergraduate students of colleges. The research identifies best practices for leveraging AI to <br>enhance collaborative learning environments. The finding of study revealed that under- graduate college <br>students highlight the potential of AI to facilitate more effective, data-informed mentorship while emphasizing <br>the irreplaceable role of human educators in guiding and inspiring students. The study concludes with <br>recommendations for educators, policymakers, and Ed Tech developers on integrating AI tools to foster <br>adaptive, student-centered learning ecosystems. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19850846 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A STUDY TEACHER-STUDENT-AI TRRIAD & REDEFINING COLLABORATIVE MENTORSHIP THROUGH AI-DRIVEN ADAPTIVE LEARNING IN UNDERGRADUATE EDUCATION Mr. Sanjay C Raval <p>The integration of Artificial Intelligence (AI) in education is transforming traditional pedagogical approaches, <br>necessitating a reevaluation of collaborative mentorship models in undergraduate education. The objective of <br>present research is the Teacher–Student–AI triad, focusing on how AI-driven adaptive learning systems can <br>redefine collaborative mentorship. AI technologies offer personalized learning experiences, real-time feedback, <br>and data-driven insights, enabling tailored support for students. However, the role of teachers and the nature of <br>student engagement must be recalibrated to maximize the potential of AI. The present research is a descriptive <br>research design for the study purpose 100 students were taken as sample who are under graduate students from <br>college located in the western suburban i.e. Andheri to Borivali. This study investigates the interplay between <br>teachers, students, and AI systems, examining how AI augments mentorship, alters classroom dynamics, and <br>impacts student outcomes. Through a mixed-methods approach involving surveys, questioner, and face to face <br>interaction to undergraduate students of colleges. The research identifies best practices for leveraging AI to <br>enhance collaborative learning environments. The finding of study revealed that under- graduate college <br>students highlight the potential of AI to facilitate more effective, data-informed mentorship while emphasizing <br>the irreplaceable role of human educators in guiding and inspiring students. The study concludes with <br>recommendations for educators, policymakers, and Ed Tech developers on integrating AI tools to foster <br>adaptive, student-centered learning ecosystems. </p> |
| title | A STUDY TEACHER-STUDENT-AI TRRIAD & REDEFINING COLLABORATIVE MENTORSHIP THROUGH AI-DRIVEN ADAPTIVE LEARNING IN UNDERGRADUATE EDUCATION |
| url | https://doi.org/10.5281/zenodo.19850846 |