Enabling and Inhibitory Pathways of University Students' Willingness to Disclose AI Use: A Cognition-Affect-Conation Perspective

2026-04-23 · Source: Artificial Intelligence · Field: Education & Learning — Educational Technology (EdTech), Academic Research & Higher Education, Educational Psychology & Learning Sciences · Depth: Advanced, quick

Summary

A study involving 546 university students and 22 interviews investigated the psychological factors influencing students' willingness to disclose AI use in academic work. Employing a Cognition-Affect-Conation (CAC) framework, the research found that psychological safety significantly boosts disclosure intention, being positively influenced by perceived fairness, teacher support, and organizational support. Conversely, evaluation apprehension diminishes disclosure intention and psychological safety, exacerbated by perceived stigma, uncertainty, and privacy concerns. Qualitative data further revealed that clear institutional policies and supportive teaching practices foster openness, while policy ambiguity and fear of negative repercussions lead to cautious or strategic non-disclosure. The study emphasizes the critical role of institutional environments and clear guidance in promoting responsible AI transparency in higher education.

Key takeaway

For university administrators and educators developing AI policies, your focus should be on cultivating psychological safety and providing unambiguous guidelines. Prioritize clear institutional clarity and supportive instructional practices to mitigate evaluation apprehension and privacy concerns, thereby encouraging students to transparently disclose their AI tool usage. This approach will foster a more responsible and ethical academic environment regarding AI integration.

Key insights

Psychological safety and clear institutional guidance are key to students' willingness to disclose AI use.

Principles

• Psychological safety increases AI disclosure.
• Evaluation apprehension reduces AI disclosure.
• Policy clarity encourages openness.

Method

A sequential explanatory mixed-methods design was used, combining survey data from 546 students analyzed with structural equation modeling and semi-structured interviews with 22 students for interpretation.

In practice

• Implement clear AI use policies.
• Foster supportive teacher interactions.
• Address student privacy concerns.

Topics

• AI Use Disclosure
• Cognition-Affect-Conation Framework
• Psychological Safety
• Evaluation Apprehension
• Higher Education Transparency

Best for: Research Scientist, AI Ethicist, Policy Maker

Related on AIssential

• Over half of Americans fear losing both their jobs and their independent thinking to AI, survey finds — Public perception of AI is split between high hopes for health advancements and significant fears of job displacement and cognitive reliance.
• A uni professor admitted using AI to write an opinion piece. Here’s what it revealed about trust in the technology — Undisclosed AI use erodes public trust and undermines industry credibility.
• AI Literacy at Scale: K-to-Career Access That Delivers Real Student Outcomes — AI integration in education requires balancing student engagement with responsible use, curriculum adaptation, and equitable access.
• The Delegation of Attention — X's open algorithm reveals a "delegation of attention" where systems learn relevance from user reactions, not reflective preferences.
• Your Company Is Already Using AI to Evaluate You — And You Don’t Know It — Companies widely deploy AI-driven "people analytics" to monitor employees, creating an invisible evaluation system with significant career implications.
• Quoting Joseph Weizenbaum — Simple computer programs can induce powerful delusional thinking in normal individuals.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.