Using Natural Language Processing to Support Peer-Feedback in the Age of Artificial Intelligence: A Cross-Disciplinary Framework and a Research Agenda

Autor/inn/en	Bauer, Elisabeth; Greisel, Martin; Kuznetsov, Ilia; Berndt, Markus; Kollar, Ingo; Dresel, Markus; Fischer, Martin R.; Fischer, Frank
Titel	Using Natural Language Processing to Support Peer-Feedback in the Age of Artificial Intelligence: A Cross-Disciplinary Framework and a Research Agenda
Quelle	In: British Journal of Educational Technology, 54 (2023) 5, S.1222-1245 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bauer, Elisabeth) ORCID (Greisel, Martin) ORCID (Kuznetsov, Ilia) ORCID (Berndt, Markus) ORCID (Kollar, Ingo) ORCID (Dresel, Markus) ORCID (Fischer, Martin R.) ORCID (Fischer, Frank)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13336
Schlagwörter	Peer Relationship; Feedback (Response); Artificial Intelligence; Natural Language Processing; Educational Research; Interdisciplinary Approach; Electronic Learning; Research Needs + Suchen Sie Ihr Suchwort? Peer-Beziehungen; Künstliche Intelligenz; Natürliche Sprache; Bildungsforschung; Pädagogische Forschung; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Forschungsbedarf
Abstract	Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01