Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education

2025-02-09

AI Summary

This study investigates U.S. higher education faculty’s pedagogical practices, cognitive attitudes, and co-occurring trust–distrust dynamics toward generative artificial intelligence (GenAI). Method: A mixed-methods study—comprising a survey and semi-structured interviews—was conducted in March 2024 with 178 faculty members at a single university. Contribution/Results: Findings empirically demonstrate, for the first time, that trust and distrust in GenAI are orthogonal rather than bipolar dimensions—challenging the conventional binary framework. Although faculty exhibit high familiarity with GenAI, actual pedagogical integration remains low; trust and distrust coexist significantly without mutual attenuation; and familiarity levels differ markedly across attitudinal subgroups. Based on these insights, the study proposes a “trust calibration” theoretical framework and identifies six evidence-informed, practice-oriented strategies to support rational GenAI adoption. These contributions provide an empirical foundation and actionable intervention pathways for GenAI governance and faculty professional development in higher education.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Generative AI (GenAI) has brought opportunities and challenges for higher education as it integrates into teaching and learning environments. As instructors navigate this new landscape, understanding their engagement with and attitudes toward GenAI is crucial. We surveyed 178 instructors from a single U.S. university to examine their current practices, perceptions, trust, and distrust of GenAI in higher education in March 2024. While most surveyed instructors reported moderate to high familiarity with GenAI-related concepts, their actual use of GenAI tools for direct instructional tasks remained limited. Our quantitative results show that trust and distrust in GenAI are related yet distinct; high trust does not necessarily imply low distrust, and vice versa. We also found significant differences in surveyed instructors' familiarity with GenAI across different trust and distrust groups. Our qualitative results show nuanced manifestations of trust and distrust among surveyed instructors and various approaches to support calibrated trust in GenAI. We discuss practical implications focused on (dis)trust calibration among instructors.

Problem

• Investigates instructor engagement with Generative AI
• Explores trust and distrust dynamics in GenAI
• Examines GenAI integration challenges in education

Innovation

• Mixed-methods study
• Surveyed 178 instructors
• Examined trust and distrust