Information Literacy and Acceptance of Generative AI among University Students and Research Scholars in India: An Extended TAM Study

Background and Objective: The proliferation of Generative Artificial Intelligence (GenAI) tools including ChatGPT, Gemini, and Microsoft Copilot has fundamentally altered academic research workflows in Indian universities, raising critical questions about user acceptance, information literacy, and academic integrity. This study investigates how four predictors information literacy (IL), modelled as a single composite construct measured through four sub-dimensions (Source Evaluation Literacy, Search Proficiency, Ethical Use of Information, and AI Output Evaluation), together with Perceived Usefulness (PU), Perceived Credibility Risk (CR), and Academic Integrity Concern (AIC) shape scholars’ attitudes toward accepting GenAI tools, within an extended Technology Acceptance Model (TAM) framework. Materials and Methods: A cross-sectional survey was administered to N = 137 valid respondents (after rigorous data cleaning from raw N = 140) comprising undergraduate students, postgraduate students, and PhD research scholars from central (73.2%) and state universities (26.1%) across India, spanning Humanities/Social Sciences (65.9%), Sciences/Engineering (19.6%), and Library and Information Science (13.8%) disciplines. Multiple OLS regression analyses were conducted following systematic data-quality auditing. Results: The model explained 66.3% of variance in GenAI acceptance attitude (R² = 0.663, Adjusted R² = 0.652, F (4,132) = 64.79, p<0.001). All four predictors were statistically significant: Information Literacy (β = 0.208, p = 0.005), Perceived Usefulness (β = 0.652, p<0.001), Perceived Credibility Risk (β = -0.203, p = 0.016), and Academic Integrity Concern (β = 0.167, p = 0.026). Gender, academic level, and university type did not significantly differentiate GenAI acceptance attitudes. Conclusions: Strengthening IL, particularly search proficiency and AI output evaluation competencies, combined with transparent communication of GenAI research utility, meaningfully promotes responsible GenAI adoption. Practical recommendations are offered for academic libraries, institutional policymakers, and GenAI tool developers.