Adaptive learning functions as the algorithmic engine that operationalizes personalized learning¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Across 13 studies, adaptive learning was consistently described as the primary mechanism through which personalization is operationalized, dynamically adjusting task difficulty, learning sequences, feedback, and pacing based on learner data. → Khalifeh 2026 q2 i? Most adaptive learning applications were in higher education—particularly computer science, language learning, and mathematics—with positive effects on achievement, engagement, and efficiency reported across quasi-experimental and design-based studies. → Khalifeh 2026

Evidence¶

Khalifeh 2026¶

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

q2 · i? · review · r2

The review's synthesis of the 13 adaptive-learning studies, many adopting a three-component model (learner, content, instructional models) in which algorithms translate performance, interaction, self-report, and motivational data into instructional adaptations. Positive effects are reported as the reviewed studies' findings, attributed by the review.

"Adaptive learning appeared in 13 studies and was consistently described as the primary mechanism through which personalization is operationalized, dynamically adjusting task difficulty, learning sequences, feedback, and pacing based on learner data."

Discussion¶

Related Claims¶

• Intelligent tutoring systems improve achievement over non-individualised instruction — related
• Individualized instruction has largely receded in recent AI-focused literature, functioning as a pedagogical predecessor to AI-based personalization — related
• Simulations of adaptive, multistage, and fixed form tests show the adaptation measures can be used operationally to identify difficulties with adaptation — related
• AI-driven personalized and adaptive learning pathways support learner engagement and satisfaction in online courses — a narrower finding that bears on this claim
• Personalized learning functions as a collaborative human–AI model in which teachers and AI systems play complementary roles — related
• AI serves two complementary roles in learning: cognitive partner and personalized recommendation mechanism — related
• In one quasi-experimental VR shooting study (n = 50), reported in a review, rule-based dynamic difficulty outperformed progressive difficulty — a narrower finding that bears on this claim
• Intelligent tutoring systems with dynamic student modeling increase learning gains over regular instruction — related