AI, Expert or Peer? Provider Biases and Feedback Uptake Among Pre-Service Teachers

[2507.16013] AI, Expert or Peer? Provider Biases and Feedback Uptake Among Pre-Service Teachers Skip to main content Search Submit Donate Log in Computer Science > Human-Computer Interaction arXiv:2507.16013 (cs) [Submitted on 21 Jul 2025 ( v1 ), last revised 24 Aug 2026 (this version, v2)] Title: AI, Expert or Peer? Provider Biases and Feedback Uptake Among Pre-Service Teachers Authors: Lucas Jasper Jacobsen , Ute Mertens , Thorben Jansen , Kira Elena Weber View PDF Abstract: The EU AI Act places teachers in charge of using high-risk AI safely in their classes, which requires them to assess AI-generated outputs. Feedback is one of the most consequential of these outputs, yet little is known about pre-service teachers perceptions of AI-generated feedback. In a randomised experiment, 273 pre-service teachers each received one of 30 written feedback messages on a mathematics learning goal, produced under identical instructions by an expert, a peer, or a large language model (LLM). Without knowing the source, the participants judged who had written the message, rated six feedback perception subscales, and revised the learning goal. Source judgements were inaccurate (peer 46%, expert 40%, LLM 36%) and followed message length, not coded feedback quality. LLM feedback received more positive evaluations when ascribed to a human source. Ratings did not differ between feedback ascribed to experts and to peers. Relative uptake was highest for LLM feedback (52%). Coded feedback quality was the only variable significantly associated with uptake. Therefore, beliefs about who wrote a message shaped perceptions but not uptake. A feature-level analysis revealed that Valence was associated with perceptions only and an instructional composite with both perceptions and uptake. The implications for AI-related evaluative skills in teacher education are discussed. Comments: 37 pages, 2 figures, 6 tables Subjects: Human-Computer Interaction (cs.HC) Cite as: arXiv:2507.16013 [cs.HC] (or arXiv:2507.16013v2 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2507.16013 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lucas Jacobsen [ view email ] [v1] Mon, 21 Jul 2025 19:24:40 UTC (708 KB) [v2] Mon, 24 Aug 2026 22:14:39 UTC (833 KB) Full-text links: Access Paper: View PDF view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2025-07 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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