Using natural language processing to support peer‐feedback in the age of artificial intelligence: a cross‐disciplinary framework and a research agenda

  • 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 toAdvancements 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.show moreshow less

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Metadaten
Author:Elisabeth BauerORCiDGND, Martin GreiselORCiDGND, Ilia Kuznetsov, Markus Berndt, Ingo KollarORCiDGND, Markus DreselORCiDGND, Martin R. Fischer, Frank Fischer
URN:urn:nbn:de:bvb:384-opus4-1048735
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/104873
ISSN:0007-1013OPAC
ISSN:1467-8535OPAC
Parent Title (English):British Journal of Educational Technology
Publisher:Wiley
Place of publication:Weinheim
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/06/16
Tag:Education
Volume:54
Issue:5
First Page:1222
Last Page:1245
DOI:https://doi.org/10.1111/bjet.13336
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Universität Serviceeinrichtungen
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung
Philosophisch-Sozialwissenschaftliche Fakultät / Psychologie
Philosophisch-Sozialwissenschaftliche Fakultät / Psychologie / Lehrstuhl für Psychologie
Philosophisch-Sozialwissenschaftliche Fakultät / Psychologie / Lehrstuhl für Psychologie mit besonderer Berücksichtigung der Pädagogischen Psychologie
Philosophisch-Sozialwissenschaftliche Fakultät / Empirische Bildungsforschung / Lehrstuhl für Learning Analytics and Educational Data Mining
Universität Serviceeinrichtungen / Zentrum für digitales Lehren und Lernen (DigiLLab)
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Licence (German):CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand)