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The Role of Machine Learning in Education: Personalisation, Pedagogy, Equity, Ethics, and the Future of Teaching and Learning. An Extended Systematic Review and Policy Analysis (2020-2026)
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- TRV-2026-2775raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
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Background: Machine learning (ML) has evolved from experimental technology to embedded educational infrastructure. Despite rapid adoption, systematic evidence synthesis with reproducible methods is lacking. Objectives: To conduct a PRISMA 2020-compliant systematic review and meta-analysis of ML in education (2020-2026), examining effectiveness, teacher transformation, equity, and policy implications. Methods: Pre-registered protocol (OSF). Searched 5 databases (Web of Science, Scopus, ERIC, ACM DL, IEEE Xplore)…
The gain
A systematic review and meta-analysis of 89 studies with 47,231 learners found machine-learning teaching tools raised learning outcomes by an average g=0.58, rising to g=0.81 with teacher training and coaching versus g=0.31 without.
Sources
1 on file- Trends in Computer Science and Information Technologydoi:10.17352/tcsit.000114
Cite this record
v1Truvace Impact Record TRV-2026-2775, v1: “The Role of Machine Learning in Education: Personalisation, Pedagogy, Equity, Ethics, and the Future of Teaching and Learning. An Extended Systematic Review and Policy Analysis (2020-2026).” Truvace, 2026-11-12. /record/TRV-2026-2775 (accessed on the day you copy it). sha256 e57cd0aa141392a7…
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