arXiv subjects

Thorben Jansen

Publications and source records attributed to Thorben Jansen.

3 recordsLinked to original sources

arXiv2026

The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?

With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential to reshape learning and teaching experiences. This meeting report synthesizes the interdisciplinary perspectives of 50 scholars from educational psychology, computer science, science education, and the learning sciences on the use of generative AI for feedback and its promises and risks in educational practice. We highlight points of convergence in the scholarship, identify areas of debate and unresolved challenges, and outline open questions and future directions for research and educational practice that emerged from structured small-group activities designed to bridge disciplinary barriers.

cs.CY↗

arXiv2026

Protecting and Promoting Human Agency in Education in the Age of Artificial Intelligence

Human agency is crucial in education and increasingly challenged by the use of generative AI. This meeting report synthesizes interdisciplinary insights and conceptualizes four aspects that delineate human agency: human oversight, AI-human complementarity, AI competencies, and relational emergence. We explore practical dilemmas for protecting and promoting agency, focusing on normative constraints, transparency, and cognitive offloading, and highlight key tensions and implications to inform ethical and effective AI integration in education.

cs.HC↗

arXiv2025

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.

cs.HC↗