📄 Research Article
Redefining personalized learning in the artificial intelligence era: an updated systematic review from 2019 to 2025
Synthesis: Khalifeh, Santiago, and Palau (2026) systematically review how AI is redefining personalized learning (PL) terminology from 2019–2025 across six databases. They find that PL remains a broad, conceptually ambiguous umbrella term — with adaptive learning, individualized instruction, and customized learning used interchangeably — and argue that AI-driven real-time feedback, adaptive sequencing, and intelligent tutoring have transformed PL in ways that demand a unified framework and definition. They also identify persistent research gaps: a disproportionate focus on older learners, limited longitudinal/inclusive/methodologically rigorous research, and insufficient attention to privacy, bias, ethics, teacher preparation, and human–AI collaboration.
Key Findings
- PL is conceptually ambiguous. Personalized learning functions as a broad, multi-layered umbrella term without a universally accepted definition; related terms are used interchangeably, creating clarity problems for research and practice.
- AI is transforming PL. Real-time feedback, adaptive sequencing, intelligent tutoring, and dynamic learning pathways have shifted PL from static, one-size-fits-all instruction toward adaptive, data-driven, learner-centered approaches.
- A unified term is needed. The review calls for a common framework to clarify definitions and enhance consistency across educational technology practice — extending Shemshack and Spector's (2020) earlier terminology review.
- Research gaps persist. Disproportionate focus on older learners; a lack of longitudinal, inclusive, and methodologically rigorous research, especially in underrepresented populations and diverse contexts; frameworks lagging behind technological innovation; and under-addressed concerns around privacy, bias, ethics, teacher preparation, and human–AI collaboration.
Implications
This review clarifies the often-conflated concepts at the heart of Personalized Learning and Adaptive Learning, connecting them to Intelligent Tutoring, LLM-based tutors, and Learning Analytics. Its call for definitional rigor and unified terminology speaks to the wiki's critique of weak and ambiguous theory/construct use, and its emphasis on methodological and equity gaps aligns with Equity In AI Education and Research Methods AIED. For designers, it cautions that "personalized" is frequently a vague label rather than a precise, evidence-backed claim.
Connected Concepts
- Personalized Learning
- Adaptive Learning
- Intelligent Tutoring
- LLM
- Generative AI
- Learning Analytics
- Equity In AI Education
- Research Methods AIED
- Limitations In AIED Research
- Item Response Theory
Connected Articles
- AI Powered Personalized Learning Elementary Fractions 2026 — AI-powered personalized learning
- Learnmate2 LLM Adaptive Learning — LLM adaptive learning
- Ecnuclaw K12 Personalized Companion — K-12 personalized companion
- Learning To Prompt Adaptive Tutoring — Prompt-based adaptive tutoring
- Tact Pedagogically Adaptive Esl Tutoring — Pedagogically adaptive ESL tutoring
Citation
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, 13, 19. https://doi.org/10.1186/s40561-026-00440-6