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Short-term AI literacy intervention does not reduce over-reliance on incorrect ChatGPT recommendations

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arxiv 2503.10556 v1 pith:KC57YUYW submitted 2025-03-13 cs.CY q-bio.NC

Brett Puppart , Jaan Aru This is my paper · ORCID
classification cs.CYq-bio.NC
keywords chatgptover-relianceincorrectinterventionrecommendationsliteracyreduceeducational
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In this study, we examined whether a short-form AI literacy intervention could reduce the adoption of incorrect recommendations from large language models. High school seniors were randomly assigned to either a control or an intervention group, which received an educational text explaining ChatGPT's working mechanism, limitations, and proper use. Participants solved math puzzles with the help of ChatGPT's recommendations, which were incorrect in half of the cases. Results showed that students adopted incorrect suggestions 52.1% of the time, indicating widespread over-reliance. The educational intervention did not significantly reduce over-reliance. Instead, it led to an increase in ignoring ChatGPT's correct recommendations. We conclude that the usage of ChatGPT is associated with over-reliance and it is not trivial to increase AI literacy to counter over-reliance.

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Cited by 2 Pith papers

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  1. Warning labels shift perceptions of sycophantic AI, but not its influence

    cs.HC 2026-06 unverdicted novelty 6.0 of 10

    Warning labels that describe sycophantic AI shift users' perceptions of the AI but do not reduce the sycophantic advice's influence on self-perceived rightness or repair intent.

  2. Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications for AI Literacy in Programmatic Data Science

    cs.HC 2025-09 conditional novelty 6.0 of 10

    In a graduate data science course, self-reported technical expertise predicted homework grades even with equal access to an LLM assistant, while self-rated AI familiarity and communication skills did not.

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