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- Vol 5 RE: Chapter 15
generative artificial intelligenceautomated feedbackhigher educationfeedback literacylarge language modelspersonalised feedbackinstructor oversightalgorithmic biasJournal article Evidence summaryAt a glance
Lee and Moore systematically review ten peer-reviewed empirical studies published from 2019 through 2023 that used generative AI for automated feedback in higher education. Systems operated across several instructional contexts and purposes, producing written guidance, conversational responses, cognitive support, emotional encouragement, and feedback linked to assessment or learning activity.
Key ideas
Reported possibilities include faster and more personalised responses, greater accessibility, lower anxiety when seeking help, and reduced instructor effort on routine feedback, potentially freeing time for complex teaching. The small and heterogeneous evidence base limits strong conclusions, however. Tools, disciplines, outcomes, and study designs varied, and rapid technological change means many systems predate current large language models. Generated feedback can be inaccurate, generic, biased, difficult to explain, or misaligned with learning goals. Students may overtrust it, and automation can weaken dialogue or instructor awareness.
Why it matters
The authors therefore frame GenAI as augmentation rather than replacement. Effective implementation requires instructors to design criteria and prompts, monitor quality, teach feedback literacy, preserve human interaction, and study how learners interpret, verify, and act on advice over time.
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- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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