Table · dataset · 2026
Supplementary file 1_Perceived usefulness and intention to use large language model-generated feedback across three educational levels: a user-centred study in programming.docx
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<p>Generative artificial intelligence is increasingly used to produce formative feedback in programming education; however, whether students find such feedback useful, actionable, and worth using remains underexamined.
Description
This study presents a user-centred evaluation of feedback produced by an automated, rubric-based large language model assessment of programming responses, using a two-layer instrument, namely, response-level feedback on individual answers rated on five dimensions (clarity, specificity, accuracy, actionability, and usefulness) and a consolidated performance report rated on six dimensions (overall satisfaction, relevance, personalisation, cognitive load, intention to use, and motivation).
Using a cross-sectional design, 144 students across secondary, short-cycle higher education (CTeSP—professional higher technical courses), and undergraduate programming rated 893 response-level feedback instances, and 140 of them rated 237 consolidated reports. Ratings were favourable across all dimensions, with student-level means of 4.24–4.43 (response level) and 4.11–4.38 (report level). At the response level, clarity and perceived accuracy were rated highest, and actionability and usefulness lowest.
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Cognitive load was the lowest at the report level. Only the between-context comparison for perceived accuracy reached statistical significance, with lower ratings in CTeSP (ε<sup>2</sup> = 0.076); the small secondary sample limits conclusions about similarity. Perceived usefulness was most strongly associated with actionability and perceived accuracy (R<sup>2</sup> = 0.76), and intention to use with motivation and personalisation (R<sup>2</sup> = 0.53).
These findings characterise the correlates of perceived usefulness and intention to use within each feedback layer, supporting human-centred designs that preserve student judgement and instructor oversight.</p>
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Where it is published
- DOI doi.org/10.3389/feduc.2026.1934069.s001 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
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- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Computing education 77%
Provenance · 1 source records, 17 field assertions
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