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