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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.00050 |
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| _version_ | 1866912405292843008 |
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| author | Dang, Canh Thien Nguyen, An |
| author_facet | Dang, Canh Thien Nguyen, An |
| contents | As artificial intelligence (AI) becomes integral to the society, the ability to critically evaluate AI-generated content is increasingly vital. On the context of management education, we examine how academic skills, cognitive traits, and AI scepticism influence students' ability to detect factually incorrect AI-generated responses (hallucinations) in a high-stakes assessment at a UK business school (n=211, Year 2 economics and management students). We find that only 20% successfully identified the hallucination, with strong academic performance, interpretive skills thinking, writing proficiency, and AI scepticism emerging as key predictors. In contrast, rote knowledge application proved less effective, and gender differences in detection ability were observed. Beyond identifying predictors of AI hallucination detection, we tie the theories of epistemic cognition, cognitive bias, and transfer of learning with new empirical evidence by demonstrating how AI literacy could enhance long-term analytical performance in high-stakes settings. We advocate for an innovative and practical framework for AI-integrated assessments, showing that structured feedback mitigates initial disparities in detection ability. These findings provide actionable insights for educators designing AI-aware curricula that foster critical reasoning, epistemic vigilance, and responsible AI engagement in management education. Our study contributes to the broader discussion on the evolution of knowledge evaluation in AI-enhanced learning environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment Dang, Canh Thien Nguyen, An Computers and Society As artificial intelligence (AI) becomes integral to the society, the ability to critically evaluate AI-generated content is increasingly vital. On the context of management education, we examine how academic skills, cognitive traits, and AI scepticism influence students' ability to detect factually incorrect AI-generated responses (hallucinations) in a high-stakes assessment at a UK business school (n=211, Year 2 economics and management students). We find that only 20% successfully identified the hallucination, with strong academic performance, interpretive skills thinking, writing proficiency, and AI scepticism emerging as key predictors. In contrast, rote knowledge application proved less effective, and gender differences in detection ability were observed. Beyond identifying predictors of AI hallucination detection, we tie the theories of epistemic cognition, cognitive bias, and transfer of learning with new empirical evidence by demonstrating how AI literacy could enhance long-term analytical performance in high-stakes settings. We advocate for an innovative and practical framework for AI-integrated assessments, showing that structured feedback mitigates initial disparities in detection ability. These findings provide actionable insights for educators designing AI-aware curricula that foster critical reasoning, epistemic vigilance, and responsible AI engagement in management education. Our study contributes to the broader discussion on the evolution of knowledge evaluation in AI-enhanced learning environments. |
| title | Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2506.00050 |