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Cited by 77Student Assessment and FeedbackArtificial Intelligence in EducationOnline Learning and Analytics

A comparative analysis of the skilled use of automated feedback tools through the lens of teacher feedback literacy

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Buckingham Shum, Simon, Lim, Lisa-Angelique, Boud, David, Bearman, Margaret, Dawson, Phillip·2023·International Journal of Educational Technology in Higher Education
DOI 10.1186/s41239-023-00410-9
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Abstract

Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed feedback literacy. A previously published teacher feedback literacy competency framework has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of automated feedback (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: Which teacher feedback competencies are necessary for the skilled use of open AF tools? and What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies? We conduct an analysis of published evidence concerning teachers’ use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers’ feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of automated feedback literacy, that is, distinctive teacher competencies linked to the skilled use of open AF tools.

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  • Recent work has suggested that the educational use of automated feedback depends on how human and machine roles are distributed within feedback processes, and that AI assistance may weaken student agency when critique is accepted too readily or when difficult judgments are outsourced to the system.

    “Recent work has suggested that the educational use of automated feedback depends on how human and machine roles are distributed within feedback processes, and that AI assistance may weaken student agency when critique is accepted too readily or when difficult judgments are outsourced to the system (Buckingham Shum et al., 2023;Darvishi et al., 2024).”

    From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Buckingham Shum et al., 2023

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • The findings add empirical weight to the argument that the pedagogical value of GenAI depends not only on what the tool can generate, but also on whether surrounding feedback designs amplify or displace students' evaluative responsibility.

    “In this respect, the findings add empirical weight to the argument that the pedagogical value of GenAI depends not only on what the tool can generate, but also on whether surrounding feedback designs amplify or displace students' evaluative responsibility(Buckingham Shum et al., 2023;Darvishi et al., 2024).”

    From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Buckingham Shum et al., 2023

    The Conclusion (page unavailable) says open automated-feedback tools can cultivate learner agency and productive action rather than passive feedback processes. The ‘Changing conceptions of feedback’ section likewise says students must actively make sense of feedback. The paper supports the importance of feedback design and student agency, but its analysis of automated-feedback tools does not establish the claim specifically for GenAI or whether GenAI designs displace students’ evaluative responsibility. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • For higher education digital learning, the central implication is not whether students can access GenAI feedback, but how GenAI feedback is pedagogically organized so that human evaluative work remains at the center.

    “For higher education digital learning, the central implication is therefore not whether students can access GenAI feedback, but how GenAI feedback is pedagogically organized so that human evaluative work remains at the center (Buckingham Shum et al., 2023; Xia et al., 2024).”

    From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Buckingham Shum et al., 2023

    The Introduction and Conclusion emphasize designing automated feedback around active student participation and teachers’ skilled use of technology; the ‘Crafts appropriate inputs to students’ section says such tools should augment teachers rather than automate them out of the loop. This supports the claim’s focus on pedagogical organization and human work, but the paper does not frame that implication specifically around students’ access to GenAI feedback. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The advantages of reflective and hybrid designs appear to reflect a more productive organization of evaluative activity rather than short-term score improvement alone.

    “Their advantages therefore appear to reflect a more productive organization of evaluative activity rather than short-term score improvement alone(Carless & Boud, 2018;Buckingham Shum et al., 2023).”

    From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Buckingham Shum et al., 2023

    The sections “Designs for feedback dialogues and cycles” and “Manages tensions between feedback and grading” describe feedback organized around revision and learning processes rather than grade justification. The paper does not establish the comparative advantages of reflective and hybrid designs or assess short-term score improvement, so those parts of the attributed claim remain unsupported. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Buckingham Shum, S., Lim, L.-A., Boud, D., Bearman, M., & Dawson, P.
Affiliations
  • University of Technology Sydney (AU)
  • Deakin University (AU)
ORCID
Venue
International Journal of Educational Technology in Higher Education
Publisher
Springer Science and Business Media LLC
Published
2023-07-12
Volume
20
Issue
1
ISSN
2365-9440 (ISSN-L)
DOI
10.1186/s41239-023-00410-9
OpenAlex
W4383873837
Cited by
77
References
16
Citations by year
2026: 37 · 2025: 25 · 2024: 12 · 2023: 2
FWCI
33.94
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
Universitat Oberta de Catalunya
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
automated feedback, artificial intelligence in education, educational analytics, university teaching

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