The Role of Learning Motivation Factors in Deepseek Generative AI Adoption among Higher Education Students in India

Abstract

This research explores adoption of the Deepseek, an artificial intelligence (AI) platform among higher education students in India by integrating the Technology Acceptance Model (TAM) with learning motivation factors. Given the rapid rise of AI-based platforms in educational sector, understanding their adoption is not only timely but also essential for ensuring equitable and effective learning outcomes. Addressing a critical research gap in understanding of rapidly evolving EdTech sector, the research blends constructs such as learning interest, achievement goals, self-efficacy, and subjective norms in expanding the typical TAM model. This integrative approach allows for a more holistic framework that captures both technological perceptions and learner-driven motivational factors, making the model especially relevant in emerging economies where educational technology adoption varies widely. Data were gathered using an online survey via Google Forms, providing 346 valid responses from students. The sample consisted of students from diverse academic disciplines, ensures representativeness across different fields of study and thereby enhancing the generalizability of the results. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS-3 software. The findings support the extended TAM model which indicated that learning interest and achievement goals have significant impact on  perceived ease of use. Self-efficacy and subjective norms have significant impact on perceived usefulness and behavioral intention has significant impact on actual usage, demonstrating its pivotal role in technology adoption. These relationships suggest that motivation-related constructs are not peripheral but central in shaping how students interact with AI-powered platforms. This study advances the literature on educational technology by establishing a new TAM model as applied to AI-powered learning tools in emerging economies. The practical implications are that developers of Deepseek need to make the platform more user-centered in order to increase adoption. Future research avenues involve analyzing other contextual factors and longitudinal patterns of adoption over time. These findings provide useful insights for stakeholders who want to maximize AI learning tool integration in universities.