Abstract:

Background:
The rapid integration of artificial intelligence (AI) tools into higher education has launched a fundamentally new era of teaching and learning, creating unprecedented challenges for pedagogical practice, assessment, and academic integrity. This study examines how students and educators across disciplines navigate AI-informed learning environments, with focused comparative attention to medical students and medical educators, and to the implicit messages conveyed through the hidden curriculum of digital education.

Methodology:
A mixed-methods survey study was conducted with 265 students and 83 faculty members at Akaki Tsereteli State University. The student sample included 58 (21%) medical students across six academic years, while the faculty sample included 27(32%) medical educators. Structured questionnaires explored five dimensions: AI tool utilization patterns; learning behaviors and critical thinking; ethical awareness and academic integrity; hidden curriculum manifestations; and professional identity formation. Quantitative data were analyzed descriptively and comparatively between medical and non-medical participants, while qualitative responses underwent thematic content analysis.

Key Findings:
Universal AI adoption was reported among students (100%), with ChatGPT, Gemini, and Perplexity identified as the most frequently used tools. Across disciplines, students reported enhanced learning efficiency and comprehension of complex material (82%), alongside concerns about overdependence on AI (71%). Comparative analysis revealed that medical students expressed significantly greater concern regarding the impact of AI on independent clinical reasoning, diagnostic responsibility, and future professional competence.
Across all disciplines, ethical uncertainty was evident, with only 38% of students reporting a clear understanding of institutional AI policies; however, this uncertainty was more pronounced among medical students, who associated AI misuse with potential risks to professional accountability rather than solely academic misconduct. The hidden curriculum emerged as a powerful influence in both groups, but medical students were particularly sensitive to faculty attitudes, assessment practices, and institutional silence, interpreting these as implicit guidance on acceptable AI use.
Faculty responses highlighted shared concerns about assessment validity and superficial knowledge acquisition. Medical educators, in contrast to non-medical faculty, emphasized challenges related to maintaining trust, epistemic authority, and professional role modeling when teaching AI-informed students.

Conclusion:
The findings suggest that while AI-driven transformations in learning affect students across disciplines, their implications are more ethically and professionally consequential, especially in medical education, as they pose a substantial risk to the development of essential clinical reasoning skills in future physicians. Universities must adopt pedagogically informed AI integration strategies that address disciplinary differences, moving beyond prohibition toward explicit guidance. In medical education in particular, AI literacy initiatives should integrate ethical reasoning, clinical responsibility, and professional identity formation, while fostering critical evaluation and independent clinical reasoning among future physicians.

Keywords:

AI-informed students, Digital learning environments, Hidden curriculum, Academic integrity, Teaching and learning.