Guarded adoption of generative AI in higher education: high-achieving students, successful-student identity

Synthesis: An institution-wide mixed-methods survey at one Australian university (484 responses, 469 with valid GPA-band data) examined whether successful-student identity and self-reported achievement were associated with different patterns of generative AI engagement. Higher-achieving students reported lower active AI engagement, lower positive affect, lower perceived learning impact and lower AI-related disengagement, alongside slightly higher negative affect. Item-level analyses showed less endorsement of AI-supported autonomy and effective learning, and greater agreement that reliance on AI hinders Critical Thinking and independent problem solving. The open-ended responses described selective use for clarification, summarization and workflow support, with outputs checked and kept subordinate to the student's own judgment. The authors name this pattern guarded adoption: selective, bounded and verification-intensive use in defense of epistemic control.

Key Findings

1. High-achieving students reported lower active AI engagement, lower positive affect toward AI and lower perceived positive learning impact, with a small positive association with negative affect.
2. The strongest Spearman correlations with self-reported GPA band were perceived learning impact (rho = -0.395), positive affect (rho = -0.359) and active AI engagement (rho = -0.357) — statistically significant but only small to moderate in magnitude.
3. High-achieving students also reported lower AI-related disengagement, which rules out a simple technology-avoidance explanation: they were neither enthusiastic heavy users nor disengaged from the technology.
4. At item level, higher achievers were less likely to say AI supported their autonomy, helped them learn more effectively, or formed part of active academic engagement, and more likely to agree that reliance on AI hinders critical thinking and independent problem solving.
5. Open-ended responses showed the workaround rather than the refusal: AI used selectively for clarification, summarization and workflow support, with outputs verified and treated as subordinate to the student's own reasoning.

Guarded adoption as a defended identity

The paper's interpretive contribution is to read the pattern as identity work rather than as a technology-perception variable. For students whose sense of themselves as successful learners rests on producing work through their own effort and judgment, unbounded AI use threatens the identity the achievement depends on. That reading explains the otherwise puzzling combination of low engagement, low disengagement and mild negative affect: the students are not disengaged, they are holding a boundary. The construct also links directly to epistemic agency, since the reported behavior is verification — outputs are checked and then subordinated rather than accepted or rejected wholesale.

The institutional implication is uncomfortable for both sides of the policy debate. Campaigns promoting AI use target exactly the group already exercising discriminating judgment, while AI Detection or prohibition regimes push against students whose self-reported behavior is closest to the pedagogic ideal. The authors note the interpretive limits of their design: a single institution, self-reported GPA bands rather than transcript data, modest internal consistency on some descriptive indices, and correlations that explain only part of the variance in engagement.

Limitations to keep in view

• Achievement is self-reported as a GPA band, which introduces both recall and social-desirability error.
• The survey measures orientation and reported behavior, not observed AI use, so the guarded pattern rests on self-description.
• Correlations between small and moderate mean most variation in AI engagement is not explained by achievement.
• One institution and one national context, so the identity mechanism is a hypothesis for wider testing rather than an established cross-context effect.

Connected Concepts

• Generative AI — the technology whose adoption is patterned
• Learner Identity — successful-student identity as the defended construct
• Learner Agency — epistemic agency and verification as the boundary practice
• Critical Thinking — the concern high achievers report about AI reliance
• Cognitive Offloading — reliance as the behavior being bounded
• Higher Education — the survey's institutional setting

Connected Articles

• "It is a temptation to get it to do the work…" Student Experiences of Navigating the Generative AI Landscape in UK Higher Education: A Cross-Institutional Survey with International Comparison — Student experiences of GenAI in UK higher education
• Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies — Dependency on AI, literacy and self-efficacy
• Reclaiming Epistemic Agency: A Critical Framework for Human-Generative AI Co-Agency in Education — Reclaiming epistemic agency
• Academic Integrity in the Age of AI: University Students' Study Practices and Ethical Judgments — Academic integrity and AI study practices
• An Exploratory Machine Learning Approach to Understanding Determinants of Future ChatGPT Use in Higher Education — Determinants of ChatGPT use in higher education

Citation

Zagami, J. (2026). Guarded adoption of generative AI in higher education: high-achieving students, successful-student identity, and epistemic agency in a single-university mixed-methods survey. International Journal of Educational Technology in Higher Education, 23(1), 47.