Who Gives Feedback Matters: Student Biases Towards Human and AI‐Generated Formative Feedback.
Background: Feedback is essential for learning, helping individuals understand and improve their performance. However, providing timely, personalised feedback in higher education is challenging. Generative AI offers a scalable solution, yet little is known about students' biases towards AI‐generated...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 1; pp. 1 - 23 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191181607&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191181607 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2026 vid: 42 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191181607 191181607 191181607 10.1111/jcal.70153 191181607 ppf: 1 ppct: 22 formats: tig: atl: Who Gives Feedback Matters: Student Biases Towards Human and AI‐Generated Formative Feedback. aug: au: Nazaretsky, Tanya Mejia‐Domenzain, Paola Swamy, Vinitra Frej, Jibril Käser, Tanja affil: EPFL, Lausanne, Switzerland sug: subj: Students, College Psychosocial Factors Artificial Intelligence, Generative Algorithms Automation User-Computer Interface Student Attitudes Evaluation Feedback Attitude to Computers Human Male Female Adolescence Adult Multimethod Studies Descriptive Statistics Wilcoxon Rank Sum Test Chi Square Test Funding Source Educational Technology Computer Literacy Learning Computer-Assisted Instruction Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Background: Feedback is essential for learning, helping individuals understand and improve their performance. However, providing timely, personalised feedback in higher education is challenging. Generative AI offers a scalable solution, yet little is known about students' biases towards AI‐generated feedback. Objectives: This study aims to investigate how the identity of the feedback provider (human vs. AI) affects students' perceptions of feedback quality and credibility. Methods: The study involved 472 students across diverse academic programmes and levels in authentic educational environments and employed a within‐subject experimental design with a priming effect. A mixed‐methods approach combined quantitative analysis of feedback evaluations with qualitative insights into students' perceptions to deepen understanding of the observed biases. Results and Conclusions: Students perceived AI as a significantly less credible feedback provider and tended to associate lower feedback quality with AI. Disclosing the feedback provider's identity led to decreased evaluations of AI‐generated feedback and an increased preference for human‐crafted feedback. These patterns were consistent across academic levels, genders, and fields of study. These insights highlight the need for targeted interventions, such as improving AI literacy and building human‐in‐the‐loop systems, to mitigate biases and enhance the effectiveness of AI in educational feedback systems. Lay Description: What is already known about this topic ○Advances in generative AI have opened up significant opportunities to provide scalable and high‐quality formative feedback.○Users' evaluations of AI‐generated content are significantly influenced by biases towards AI as a content provider.○These biases manifest in two opposing forms: algorithm aversion and algorithm appreciation.What this paper adds ○We conducted an empirical study with 472 students in authentic learning environments to evaluate their perceptions of human and AI‐generated feedback.○Students consistently attributed higher‐quality feedback to human providers, even when the feedback was AI‐generated.○Feedback explicitly identified as human‐crafted was perceived as higher quality.Implications for practice and/or policy ○It is essential to address student perceptual biases to maximise the effectiveness and acceptance of AI‐generated feedback in education.○Enhancing students' AI literacy can help reduce negative biases and foster a more balanced evaluation of AI as a feedback provider.○Incorporating humans into the feedback loop by combining AI‐generated feedback with human oversight or validation can enhance trust and ensure the quality of feedback. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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