Artificial Intelligence in Learning

Henderi (Universitas Raharja, Indonesia)

The Artificial Intelligence in Learning (AIL) (E-ISSN: 3089-3690 | DOI Prefix: 10.63913/ail) is a peer-reviewed international open-access academic journal dedicated to advancing the understanding and application of artificial intelligence in education and learning environments. It publishes high-quality research articles, reviews, and technical reports that explore the development, implementation, and impact of AI-driven technologies in educational contexts. The journal serves as a platform for scholars, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address challenges in AI-enhanced learning.

Topics covered include:

Machine Learning for Personalized Learning; Intelligent Tutoring Systems; Educational Data Mining and Learning Analytics; Adaptive Learning Environments; AI-Driven Assessment and Evaluation; Digital Pedagogy and AI-Supported Instruction.

AIL aims to foster interdisciplinary dialogue and collaboration among computer scientists, educators, cognitive researchers, and instructional designers, contributing to the advancement of intelligent and data-driven education systems. Papers published in AIL are grounded in rigorous research methodologies and are expected to clearly articulate their theoretical and practical contributions. Authors are encouraged to highlight the novelty of their work and consider the broader educational and societal implications, including ethical AI practices and responsible innovation. In alignment with global research priorities, AIL welcomes studies that support sustainability and contribute to the advancement of education in line with the United Nations 2030 Sustainable Development Goals (SDGs).

Subject Area and Category:

The Artificial Intelligence in Learning focuses on the design, development, and evaluation of AI technologies in education. The journal covers research on Machine Learning for Personalized Learning; Intelligent Tutoring Systems; Educational Data Mining and Learning Analytics; Adaptive Learning Environments; AI-Driven Assessment and Evaluation; and Digital Pedagogy Innovation. It also addresses ethical, social, and policy-related aspects of AI in education, as well as practical implementations and case studies demonstrating real-world applications across formal and informal learning settings, including schools, higher education, corporate training, and lifelong learning ecosystems

Starting publishing date: 2025

Frequency: Quarterly (March, June, September, and December)

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