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April 7, 2026Big Data and Cognitive ComputingOpen Access

Exploring the Mechanisms Influencing Graduate Students’ Adoption of Generative AI: Insights from the Technology Acceptance Model

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Authors

QCQing ChenYXYujie XueJLJie Lin

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Overview

Explores cognitive factors influencing graduate students' adoption of generative AI, suggesting new insights for system design.

Key Points

  • The aim is to investigate the cognitive mechanisms that affect graduate students' adoption of generative AI.
  • Thematic analysis of in-depth interviews with 20 graduate students.
  • Exploration of constructs such as perceived usefulness and risk perception.
  • Integration of cognitive calibration between trust and risk evaluation.
  • Seven interrelated constructs were identified influencing adoption.
  • Cognitive calibration plays a significant role in trust and risk evaluation.
  • Interaction subjectivity is crucial for determining engagement type with AI.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69d49fa9b33cc4c35a2280bbhttps://doi.org/10.3390/bdcc10040108
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Understanding students’ perceptions of generative AI: Implications for pedagogy and graduate employability2025 · 5 citations
  2. 2Exploring the acceptance of generative artificial intelligence for language learning among EFL postgraduate students: An extended TAM approach2024 · 51 citations
  3. 3Generative-AI, a Learning Assistant? Factors Influencing Higher-Ed Students' Technology Acceptance2024 · 108 citations
  4. 4Generative AI in Graduate Education: Student Experiences, Critical Thinking, and Academic Practice2026
  5. 5Understanding and Modeling Technology Adoption in a New Era: A Cross-Sectional Study on Higher Education Teachers’ Adoption and Use of Generative Artificial Intelligence2026 · 1 citations