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Cited by 9Educational Strategies and EpistemologiesMisinformation and Its ImpactsLibrary Science and Information Literacy

The Trap of AI Literacy: The Paradoxical Relationships Between College Students’ Use of LLMs, AI Literacy, and Fact-checking Behavior

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Rheu, Minjin (MJ), Cho, Janghee·2025·Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3706599.3719681
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Abstract

This study examines factors influencing users' critical engagement with large language models (LLMs), focusing on fact-checking behavior.While LLMs transform how individuals acquire knowledge, their rapid adoption raises concerns about uncritical acceptance due to limitations like hallucinations.A survey of college students and young professionals revealed nuanced effects of LLM literacy.Understanding LLM processes, such as input, processing, and output, encourages fact-checking.However, self-efficacy and knowledge of LLM features paradoxically reduce verification by fostering reliance on machine heuristics and elevating the perceived credibility of outputs.These findings highlight the complex role of AI literacy in promoting critical engagement and the importance of education to deepen users' technological understanding.

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  • Artificial intelligence literacy is multidimensional and may influence evaluation, checking strategy, and reliance decisions.

    “Artificial intelligence literacy is multidimensional and may influence evaluation, checking strategy, and reliance decisions (Rheu and Cho, 2025; Zainuddin et al., 2026).”

    From Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education (2026) · cites this paper as Rheu and Cho, 2025

  • Rheu and Cho (2025) reported that understanding LLM processes was associated with more self-reported fact-checking, whereas some forms of self-efficacy and feature knowledge were associated with stronger machine heuristics and less checking.

    “Rheu and Cho (2025) reported a nonuniform, dimension-specific pattern: understanding LLM processes was associated with more self-reported fact-checking, whereas some forms of self-efficacy and feature knowledge were associated with stronger machine heuristics and less checking.”

    From Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education (2026) · cites this paper as Rheu and Cho, 2025

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Cited by

Sorted by citations

Authors
Rheu, M. (., & Cho, J.
Affiliations
  • Loyola University Chicago (US)
  • National University of Singapore (SG)
ORCID
Venue
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2025-04-25
Pages
1-7
DOI
10.1145/3706599.3719681
OpenAlex
W4409887525
Cited by
9
References
14
Citations by year
2026: 8 · 2025: 1
FWCI
35.82
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Library and Information Sciences › Library Science and Information Literacy
Keywords
AI literacy, large language models, university students, young professionals, LLM hallucination, critical engagement with AI

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