AI-driven personalised learning now shapes the pace, practice, and feedback of millions of learners. Most systems, however, personalise learning for the learner through opaque algorithmic decisions instead of equipping learners to personalise their own study. This review examines that asymmetry. Twenty-five studies published between 2017 and 2026, retrieved from Google Scholar, ERIC, and Scopus, are synthesised thematically around learner agency, self-regulated learning, and system design. The synthesis identifies an illusion of autonomy: surface-level choices sit alongside algorithmic authority over goals, sequencing, and mastery criteria, which narrows the room learners have to practise goal setting, strategic monitoring, and reflective evaluation. Duolingo and two platforms used in Vietnam serve as illustrative cases showing how gamified metrics and default pathways can redirect attention from meaningful goals towards externally driven engagement. The paper answers with a learner-driven personalisation framework resting on three principles: shared control, design that activates self-regulated learning, and algorithmic transparency coupled with AI literacy. A three-item rubric operationalises the framework so that schools can detect illusion-of-autonomy risks before adoption. The analysis extends to equity, since learners with limited devices, connectivity, or AI literacy gain the least from transparency measures that presuppose digital capital. Implications concern system designers, educational leaders, and teachers in Vietnam, who need evaluation criteria reaching beyond short-term performance. The contribution is a concise screening tool that links learner control, the three phases of self-regulated learning, and algorithmic transparency, complementing broader agency rubrics with a focused test for illusion-of-autonomy risks.
Research Article | Open Access | Download Full Text
Volume 3 | Issue 2 | Year 2026 | Article Id: DSM-V3I2P103 DOI: https://doi.org/10.59232/DSM-V3I2P103
From Algorithmic Control to Learner-Driven Personalisation: Rethinking AI in Education
Minh Phan, Duong Thanh Linh, Vuong T. Pham
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 29 Jun 2026 | 30 Jul 2026 | 25 Sep 2026 | 03 Sep 2026 |
Citation
Minh Phan, Duong Thanh Linh, Vuong T. Pham. “From Algorithmic Control to Learner-Driven Personalisation: Rethinking AI in Education.” DS Journal of Multidisciplinary, vol. 3, no. 2, pp. 37-52, 2026.
Abstract
Keywords
Adaptive Learning Platforms, Educational Equity, Learner Agency, Self-Regulated Learning, Shared Control.
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