Journal of Cognitive AI for Human Learning

Dr. Thosporn Sangsawang

The Journal of Cognitive AI for Human Learning (JCAIHL) is an international, peer-reviewed scholarly journal dedicated to research at the intersection of Artificial Intelligence, Cognitive Science, and Learning Sciences.

The journal provides a global platform for researchers, scientists, and practitioners to explore the development of intelligent computational approaches that understand, model, explain, and optimize human thinking and learning processes.

JCAIHL focuses on advancing research beyond the conventional application of artificial intelligence in education by emphasizing intelligent systems capable of representing learner knowledge, reasoning about learning processes, adapting to individual learners, providing interpretable insights, and supporting personalized human learning.

The journal encourages interdisciplinary research integrating artificial intelligence, computer science, cognitive science, psychology, educational technology, learning analytics, and related fields to develop next-generation intelligent learning environments.

Through its publications, JCAIHL aims to contribute to the scientific understanding and technological advancement of AI systems that can model human cognition, enhance learning experiences, and support adaptive decision-making in educational environments.

Topics covered include:

The journal covers a comprehensive range of topics aimed at integrating Cognitive Science, AI, and the Learning Sciences. Core areas include learner modeling, intelligent tutoring, and adaptive learning systems. It explores advanced analytics—such as educational data mining and digital twins—alongside cutting-edge technologies like Large Language Models, Generative AI, and Neuro-symbolic AI. Furthermore, the journal emphasizes human-AI interaction, affective computing, and cognitive process modeling. These technological advancements are firmly grounded in ethical principles, prioritizing explainable, secure, inclusive, and human-centered AI for education.

Subject Area and Category:

The Journal of Cognitive AI for Human Learning (JCAIHL) focuses on the design, development, evaluation, and application of cognitive artificial intelligence systems for human learning. The journal covers a broad range of research areas, including cognitive AI and computational models of learning, AI-based personalized and adaptive learning environments, learner modeling and knowledge tracing, explainable and trustworthy artificial intelligence, and intelligent tutoring systems. Furthermore, its scope encompasses multimodal learning analytics, educational data mining, human-AI interaction in educational contexts, generative AI and foundation models for learning, neuro-symbolic AI and cognitive computing, as well as AI-supported assessment and decision-making systems. JCAIHL welcomes interdisciplinary studies connecting artificial intelligence with psychology, neuroscience, cognitive science, education, and the learning sciences. Research published in JCAIHL must demonstrate clear theoretical contributions, methodological advancements, or practical implications, while meticulously considering the ethical, social, and technological impacts of AI-driven learning systems. In alignment with global research priorities, JCAIHL strongly encourages studies that contribute to the development of sustainable, inclusive, and human-centered intelligent learning environments.

Starting publishing date: 2026

Frequency: Quarterly (February, May, August, and November)

Indexed on: