Focus and Scope

Artificial intelligence is transforming how learning is designed, delivered, assessed, and improved. The increasing use of intelligent systems, educational data, adaptive platforms, and automated decision-support tools has created new opportunities to personalize learning, support teachers, improve institutional decision-making, and strengthen educational outcomes. At the same time, the use of AI in education raises important questions related to fairness, transparency, ethics, data privacy, learning quality, and responsible implementation.

Artificial Intelligence in Learning provides a scholarly forum for publishing research on the theoretical, methodological, and applied aspects of artificial intelligence in education and learning environments. The journal welcomes studies that examine AI-based learning technologies, educational analytics, intelligent tutoring systems, digital learning platforms, institutional analytics, and responsible AI practices in education.

The journal encourages research that contributes to the development of evidence-based, ethical, inclusive, and effective AI applications for learning. Submissions may include empirical studies, experimental research, computational models, system development, literature reviews, policy analysis, and interdisciplinary studies connecting artificial intelligence, education, data science, psychology, and learning sciences.

The journal is a forum for the exchange of research findings, analysis, information, and knowledge in areas that include, but are not limited to:

  • Artificial Intelligence in Education – Research on the design, development, evaluation, and implementation of AI technologies for teaching, learning, and educational decision-making.
  • Learning Analytics and Educational Data Mining – Studies using data mining, machine learning, statistical analysis, and visualization to understand learning behavior, student performance, engagement, retention, and academic achievement.
  • Adaptive and Personalized Learning Systems – Research on intelligent tutoring systems, adaptive learning platforms, recommender systems, and personalized feedback mechanisms that support learner-centered education.
  • Prediction, Classification, and Early-Warning Models in Education – Studies applying predictive analytics to identify student risk, academic success, learning difficulties, course completion, scholarship outcomes, and other educational indicators.
  • Digital Learning Platforms and E-Learning Innovation – Research on online learning, blended learning, MOOCs, learning management systems, user engagement, digital learning resources, and technology-supported instruction.
  • Generative AI, AI Literacy, and Educational Practice – Studies examining the use of generative AI, prompt-based learning, AI-assisted teaching, academic integrity, teacher readiness, and learner interaction with intelligent tools.
  • Responsible, Ethical, and Explainable AI in Learning – Research addressing fairness, transparency, bias, privacy, accountability, governance, and the responsible adoption of AI in educational contexts.
  • Skills, Workforce, and Lifelong Learning Analytics – Studies on skill development, employability, professional learning, AI-related competencies, career pathways, and data-driven workforce education.