Trust, Verify-AI or Depend? Critical AI Literacy, Information Verification and Responsible AI Use among Management Students

Abstract

The increasing integration of generative artificial intelligence (GenAI) into higher education has created new opportunities for personalised learning, academic efficiency, and knowledge accessibility, while raising concerns about information reliability, cognitive dependence, and students’ capacity for independent evaluation. Although AI literacy is increasingly recognised as an essential educational competency, less attention has been devoted to the relationship between students’ self-reported verification practices and their expectations regarding institutional support for responsible AI use. This study investigates how management students engage critically with AI-generated information and identifies pedagogical priorities for strengthening responsible, reflective, and autonomous AI-supported learning. Drawing on a cross-sectional questionnaire completed by 93 management students, the research examines AI-related verification behaviours, perceptions of cognitive risks, approaches to academic problem-solving, and preferences for AI literacy education. Descriptive findings indicate that 44.7% of respondents report always or often verifying AI-generated information, whereas 69.9% identify the critical evaluation of AI outputs as a priority for university education. The 25.2-percentage-point difference highlights a contrast between reported verification behaviour and expressed educational priorities, although these indicators measure distinct constructs and should not be interpreted as a direct estimate of an individual competency deficit. Building on contemporary scholarship concerning AI literacy, responsible trust, epistemic responsibility, and human–AI collaboration, the article proposes VERIFY–AI, a pedagogical framework designed to support source evaluation, error detection, evidence-based reasoning, and accountable use of AI-generated content. The study contributes context-specific empirical evidence to discussions of responsible AI integration in management education and offers practical directions for curriculum development, assessment design, and critical AI literacy instruction. Given the modest sample size, reliance on self-reported data, and cross-sectional design, the findings are interpreted as exploratory rather than causal or broadly generalisable.