Aim and Scope

AI in Learning explores the transformative impact of artificial intelligence on education. AI is reshaping educational experiences by personalizing learning, automating administrative tasks, and providing data-driven insights that enhance educational practices. AI-powered tools in and outside classrooms adapt content to individual learning needs, supporting innovations such as adaptive tutoring, differentiated teaching, tailored exercises, and intelligent feedback. Additionally, AI supports educators by managing administrative workloads, optimizing schedules, identifying at-risk students, and fostering equity. As AI continues to integrate into educational contexts, this journal emphasizes the importance of ethical considerations, transparency, and equity in its applications.

We welcome submissions from a wide range of quantitative and qualitative research methodologies, exploring the role of AI across all educational stages and settings, including industry applications. Areas of interest include, but are not limited to:

Key Areas of Focus

  • Curriculum and Assessment Design: AI-driven approaches to creating and evaluating educational content.
  • Humanizing Pedagogy: Leveraging AI for culturally relevant, personalized learning that supports diverse student identities.
  • Educational Technology Integration: Innovations in tool-specific educational technology to enhance learning experiences.
  • Critical Perspectives on AI and Equity: Exploring the ethical and equity implications of AI in educational environments.
  • Inclusive Student Support: AI applications for supporting diverse student needs, including underrepresented groups.
  • Innovative Pedagogical Approaches: AI-enhanced teaching methods that foster engagement and learning outcomes.
  • Education Leadership and Policy: Impacts of AI on educational policy and institutional decision-making.
  • Streamlining Educational Business Processes: Optimizing workflows and administrative functions.
  • Diversity, Equity, and Inclusion (DEI/EDI): Including considerations on decolonization in AI for education.
  • Support for Specific Student Groups: Tailored approaches for international, mature, and part-time students.
  • Specialized Areas of Student Support: Addressing psychological, emotional, social needs, and special study requirements.

Further Areas of Interest

  • Transitional Support: AI’s role in aiding cultural and language transitions.
  • New Frameworks and Methodologies: The development and application of new theories and methods.
  • Stage-Specific Learning Needs: Approaches to meet the diverse needs of students across different educational stages.