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Pedagogical framework for hybrid intelligent feedback.

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Title: Pedagogical framework for hybrid intelligent feedback.
Authors: Banihashem, Seyyed Kazem1 (AUTHOR) kazem.banihashem@ou.nl, Noroozi, Omid2 (AUTHOR), Khosravi, Hassan3 (AUTHOR), Schunn, Christian D.4 (AUTHOR), Drachsler, Hendrik1,5 (AUTHOR)
Source: Innovations in Education & Teaching International. Apr2026, Vol. 63 Issue 2, p554-570. 17p.
Subject Terms: *Generative artificial intelligence, *Teaching models, *Artificial intelligence, *Psychological feedback, *Educational technology
Abstract: Generative AI (GenAI) has gained attention as a new feedback source in education because it can generate human-like text. However, its use in feedback lacks a strong pedagogical framework, which is necessary for effective implementation. This paper addresses this gap. It outlines human-centered feedback challenges, and then explores human and artificial cognition differences, highlighting the need for hybrid intelligence. Next, it positions GenAI feedback within feedback theory and proposes a definition for GenAI feedback. The paper conceptualizes the role of GenAI feedback as either an independent source or as part of a collaborative process with humans referred to as "Hybrid Intelligent Feedback". Building on this conceptualization, it discusses the approaches and principles of hybrid intelligent feedback and then proposes a pedagogical framework that outlines the implementation steps for hybrid intelligent feedback. The paper concludes by describing the pedagogical framework and outlining recommendations for future research on hybrid intelligent feedback. [ABSTRACT FROM AUTHOR]
Copyright of Innovations in Education & Teaching International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Innovations+in+Education+%26+Teaching+International%22">Innovations in Education & Teaching International</searchLink>. Apr2026, Vol. 63 Issue 2, p554-570. 17p.
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  Data: Generative AI (GenAI) has gained attention as a new feedback source in education because it can generate human-like text. However, its use in feedback lacks a strong pedagogical framework, which is necessary for effective implementation. This paper addresses this gap. It outlines human-centered feedback challenges, and then explores human and artificial cognition differences, highlighting the need for hybrid intelligence. Next, it positions GenAI feedback within feedback theory and proposes a definition for GenAI feedback. The paper conceptualizes the role of GenAI feedback as either an independent source or as part of a collaborative process with humans referred to as "Hybrid Intelligent Feedback". Building on this conceptualization, it discusses the approaches and principles of hybrid intelligent feedback and then proposes a pedagogical framework that outlines the implementation steps for hybrid intelligent feedback. The paper concludes by describing the pedagogical framework and outlining recommendations for future research on hybrid intelligent feedback. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Innovations in Education & Teaching International is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/14703297.2025.2499174
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial intelligence
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              Text: Apr2026
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