Overview
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Aims and Scope
The Adaptive Learning (AL) journal is an international, peer-reviewed scholarly publication that explores the design, development, and application of adaptive and intelligent learning systems across education, technology, and cognitive sciences.
In an age of digital transformation, Adaptive Learning serves as a global platform for educators, researchers, and technologists to share innovative approaches that harness artificial intelligence, data analytics, and personalized learning frameworks to enhance human learning experiences.
The journal aims to bridge educational theory and intelligent technology, fostering interdisciplinary collaboration between education, computer science, cognitive psychology, and data science. Through its publications, AL seeks to advance both theory and practice in adaptive education, supporting personalized learning pathways that respond dynamically to learners’ needs, behaviors, and contexts.
| Note: The journal does not consider submissions with SLR type research. |
The Adaptive Learning (AL) journal aims to advance the field of personalized and intelligent education by promoting innovative research and technological development that make learning more flexible, data-driven, and human-centered. The journal provides an international forum for original research, theoretical insights, and applied studies that enhance the adaptability and effectiveness of learning systems across disciplines and learning environments.
Topics of interest include (but are not limited to):
- Adaptive educational technologies and intelligent tutoring systems.
- AI-driven personalization in e-learning and digital education platforms.
- Learning analytics and predictive modeling for adaptive instruction.
- Cognitive and behavioral adaptation in digital learning environments.
- Adaptive assessment and feedback mechanisms.
- Gamification and motivation models in personalized learning.
- Equity, ethics, and inclusivity in adaptive learning systems.
- Cross-disciplinary integration of pedagogy, neuroscience, and computational methods.
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