About the Journal

Focus and Scope

The Review of Artificial Intelligence in Education is a scholarly journal dedicated to advancing knowledge on the applications, implications, and innovations of artificial intelligence (AI) in education. The journal serves as a platform for critical analysis, empirical research, and theoretical exploration of AI-driven technologies and their transformative role in learning environments, institutional management, and educational business models.

We aim to foster interdisciplinary dialogue among scholars, researchers, policymakers, and practitioners from the fields of education, business administration, public administration, and technology. By integrating perspectives from AI, education management, and governance, we seek to address pressing challenges, opportunities, and ethical considerations in the adoption of AI-powered solutions for education.

The journal welcomes contributions that explore AI's role in personalized learning, decision-making in educational institutions, governance models, corporate training, and digital learning strategies. Additionally, we prioritize discussions on policy frameworks, ethical concerns, digital equity, and the socioeconomic impact of AI on education.

Thematic Areas

To align with international academic standards and the requirements of the Spell database, we invite submissions within the following key thematic areas:

1. AI in Educational Management and Institutional Governance

  • AI applications in decision-making, resource allocation, and institutional strategy.
  • AI-driven performance analytics for academic institutions and student success.
  • Governance and policy frameworks for AI integration in education.
  • Ethical, regulatory, and compliance considerations in AI-enhanced education.

2. AI in Corporate Learning, Workforce Development, and Training

  • AI-powered professional development and corporate training platforms.
  • Personalized skill development through AI-driven learning analytics.
  • The role of AI in executive education and leadership training.
  • AI-driven solutions for human capital management and talent acquisition.

3. Business Models and EdTech Entrepreneurship

  • AI-driven innovations in EdTech startups and digital learning enterprises.
  • Economic and financial sustainability of AI-powered education businesses.
  • AI's impact on education markets, policy-making, and investment trends.
  • The intersection of AI, educational entrepreneurship, and business strategy.

4. AI, Data Science, and Decision Support Systems in Education

  • The use of machine learning and predictive analytics in educational decision-making.
  • AI-enhanced assessment systems, automated feedback, and academic advising.
  • Digital transformation and data-driven governance in education.
  • Risk assessment, security, and privacy challenges in AI-driven education.

5. Societal and Economic Implications of AI in Education

  • AI’s impact on educational equity, access, and inclusivity.
  • Socioeconomic and cultural challenges of AI adoption in diverse learning environments.
  • Digital transformation and its effects on public and private education sectors.
  • Policy responses to AI-driven changes in higher education and lifelong learning.

Target Audience

The journal is intended for:

  • Academics and Researchers: Scholars in education, business administration, public administration, AI, and policy studies.
  • Education and Institutional Leaders: University administrators, policy-makers, and institutional decision-makers.
  • Industry Professionals and Practitioners: AI developers, corporate trainers, and EdTech entrepreneurs.
  • Regulators and Policymakers: Government officials, non-governmental organizations, and agencies shaping AI-driven education policies.

Editorial Sections

To ensure comprehensive and high-quality scholarship, the journal includes the following editorial sections:

1. Scientific Review Articles

  • Systematic and critical reviews of emerging trends and theoretical advancements in AI applications in education.
  • Meta-analyses of existing research on AI-enhanced learning, decision-making, and policy implications.

2. Empirical Research and Case Studies

  • Studies documenting the impact of AI-driven learning strategies on educational outcomes.
  • Case analyses of AI implementation in institutional decision-making, corporate training, and educational entrepreneurship.

3. Policy and Governance Analyses

  • Research on the intersection of AI, educational governance, regulatory compliance, and ethics.
  • Policy recommendations for sustainable and responsible AI integration in education.

4. Ethical and Social Reflections

  • Explorations of digital equity, bias, and ethical concerns in AI-enhanced education.
  • Discussions on transparency, fairness, and AI accountability in education.

5. Expert Interviews and Thought Leadership

  • Conversations with leading experts in AI, education technology, and business strategy.
  • Insights from institutional leaders, policymakers, and AI developers.

6. Perspectives and Opinion Pieces

  • Scholarly commentaries on emerging issues and theoretical debates in AI and education.
  • Forward-looking discussions on AI’s potential to reshape global education.