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A STUDY ON STUDENT ADAPTATION TO AI-DRIVEN ACADEMIC TOOLS IN THE ERA OF INTELLIGENT LEARNING WITH SPECIAL REFERENCE TO COIMBATORE DISTRICT

Author Information
Name: G. Kowsalya & S. Hariharan
Country: India
Publication Details
Year: 2026
Volume: Volume No: 13, Issue No: 1, Year: 2026
Page Number: 289-296
DOI: https://doi.org/10.5281/zenodo.19692615
Abstract
ABSTRACT
The Rapid integration of Artificial Intelligence (AI) in higher education has triggered a
significant transformation in academic learning environments. This study examines student adaptation to AI-driven academic tools in the era of intelligent learning, with special reference to higher education institutions in Coimbatore District, Tamil Nadu. The study is grounded in the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovations Theory, all of which collectively explain how perceived usefulness, ease of use, social influence, and individual characteristics shape technology adoption among learners. Using a descriptive research design and stratified random sampling, primary data were collected from 120 students through a structured questionnaire covering five dimensions: awareness and usage of AI tools, perceived benefits on academic performance, challenges and concerns, future outlook, and demographic influences. Statistical analyses including percentage analysis, crosstabulation, Chi-square test, and Independent Samples T-Test were performed using IBM SPSS. Findings reveal that 91.7% of students are aware of AI academic tools, with 56.7% using them daily. ChatGPT dominates usage at 55.8%, while 57.5% report improved academic performance and 68.3% indicate AI saves time. Key challenges include ethical concerns such as plagiarism and over-dependence (21.7%), technical knowledge gaps (20%), limited device and internet access (20%), and reliability issues (20%). The study further finds
no statistically significant difference between genders or age groups in AI confidence or
usage frequency.

Keywords: Artificial Intelligence in Education, Student Adaptation, AI-Driven Academic
Tools, Intelligent Learning, Technology Acceptance Model, Digital Literacy, Academic
Performance, Coimbatore District
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