Adopt more precise terminology such as digital personalized learning (DPL) or personalized adaptive learning, and pursue rigorous longitudinal research with younger learners¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The review's future-research agenda calls for "greater conceptual clarity in defining personalized learning in AI-driven educational contexts, encouraging the use of more precise terminology—such as digital personalized learning (DPL) or personalized adaptive learning." It further calls for closer collaboration between AI developers and educators, integration of technology-based diagnostic assessment addressing cognitive and emotional dimensions, and systematic comparative evaluations of personalized adaptive learning platforms across learner profiles, contexts, and goals.

Design Implications¶

Context¶

Requirements¶

• Closer collaboration between AI developers and educators to ensure emerging tools are pedagogically sound and user-friendly

Constraints¶

• Existing research disproportionately focuses on older learners, leaving gaps for younger, elementary-aged students; much literature relies on cross-sectional designs and self-report data limiting causal inference

Target Learners¶

• Underrepresented learner populations and younger, elementary-aged students

Target Learning Goals¶

• Conceptual clarity and rigorous empirical validation of personalized learning approaches

Claims¶

• The review finds a complex, mixed landscape of ITS effectiveness and a need for greater scientific rigor in experimental design and data analysis [+W]

Related Strategies¶

• Future AI-SRL research should broaden search strategies, include educators and younger learners, address motivation, and apply longitudinal designs
• Research recommendations: broaden program descriptions, account for prior experience and context, define outcomes, and use longitudinal mixed-methods designs
• Adopt unified KPIs and rigorously assess learner diversity, recommender fairness, and long-term engagement when evaluating ITS
• Collaborative cross-cultural research agenda for validating AI-enabled pedagogical accompaniment
• Future team-learning change research should use mixed methods with more than three measurement points in short intervals

Examples¶

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Key Sources¶

• Khalifeh, F., Santiago, R., & Palau, R. (2026). Redefining personalized learning in the artificial intelligence era: an updated systematic review from 2019 to 2025. Smart Learning Environments. https://doi.org/10.1186/s40561-026-00440-6