Automated feedback with artificial intelligence and self-regulation of mathematical learning in secondary education: a systematic literature review 2020 to March 2026
DOI:
https://doi.org/10.70577/asce.v5i3.996Keywords:
artificial intelligence; automated feedback; self-regulated learning; mathematics learning; secondary education; systematic review.Abstract
This systematic review examines the role of AI-based automated feedback in supporting self-regulated learning (SRL) in secondary school mathematics. The study followed the PRISMA 2020 protocol and searched seven databases (Scopus, Web of Science, ERIC, SciELO, Redalyc, MDPI, and Frontiers in Education) for the period January 2020 to March 2026. From 347 initial records, 20 studies met the inclusion criteria for final analysis. Intelligent tutoring systems with student knowledge modeling show the most consistent evidence of effectiveness, followed by adaptive platforms and large language model-based conversational assistants. Error monitoring is the SRL dimension most consistently supported by automated feedback; metacognitive planning, by contrast, yields contradictory results depending on how much decision-making control the system transfers to the learner. The most recurrent limitations are superficial feedback, the digital divide, and the risk of technological dependency when instructional design does not include progressive transfer of control. The review concludes that automated feedback produces sustained effects on SRL only when teachers actively interpret system data and integrate it into classroom practice.
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