Journal article
The Dimensions of Abundance in AI-Generated Feedback
Education sciences, Vol.16(3), pp.1-33
18/03/2026
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Abstract
Feedback is an integral part of the learning process. However, delivering feedback effectively remains challenging, particularly within massified higher education systems that are characterised by large cohorts and increasingly diverse student populations. The emergence of generative artificial intelligence (GenAI) enables new ways of embedding feedback into educational offerings, some of which may be highly beneficial. In this paper, we introduce Abundant Feedback as a conceptual lens for examining the new capabilities that may be enabled by GenAI. We present a four-dimensional framework identifying the dimensions of GenAI feedback as abundance of Volume, of Availability, of Relevance and of Character. Through a systematic literature search, we describe how these dimensions manifest in recent empirical studies, and identify two educational domains, Computer Programming and Foreign Languages, as early adopters of AI-generated feedback. Beyond merely digitising existing scarce feedback processes, we discuss the emergence of new learner-driven feedback practices that are enabled by abundance, that both stimulate and demand student feedback literacy. Our multi-dimension abundance framework provides a lens, as well as the vocabulary and conceptual tools, to guide the implementation of GenAI feedback in ways that help realise the potential of artificial intelligence to enhance student learning.
Details
- Title
- The Dimensions of Abundance in AI-Generated Feedback
- Creators
- Euan Lindsay - Aalborg UniversityAndrew Rodda - Monash UniversityAnna Lidfors Lindqvist - University of Technology SydneyZach Quince - Southern Cross UniversityMay Lim - University of New South WalesDan Jiang - Aalborg University
- Publication Details
- Education sciences, Vol.16(3), pp.1-33
- Publisher
- MDPI
- Number of pages
- 33
- Grant note
- This work was funded in part by the Villum Foundation, grant number VIL57392.
- Identifiers
- 991013372748702368
- Copyright
- © 2026 by the authors.
- Academic Unit
- Centre for Teaching and Learning; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article