Social Influence and Disciplinary Variation in Graduate Students’ Acceptance of Generative Artificial Intelligence

• In Event: Adoption and Resistance: Understanding the Human Factors of AI Integration

Abstract

This study examines how graduate students adopt generative AI tools in academic settings, drawing on survey data from Chinese research universities. Using an extended Technology Acceptance Model, we explore how perceived usefulness, ease of use, and external influence shape behavioral intention, with attention to gender, education level, and disciplinary differences. Findings reveal that social and institutional contexts—not usability—are the primary drivers of adoption. Quantitative disciplines show stronger uptake than fields like medicine, reflecting distinct epistemological orientations. Female students value AI’s efficiency benefits more than males, potentially linked to academic workload disparities. These results highlight the sociocultural embeddedness of AI adoption and call for discipline-sensitive, equity-aware approaches to integrating emerging technologies in higher education.

Author

• Jie Lin, Shanghai Jiao Tong University