EduAI Nexus: Journal of Artificial Intelligence in Education

EduAI Nexus: Journal of Artificial Intelligence in Education is an international, open-access, peer-reviewed journal that provides a global platform for researchers, developers, educators, and policymakers to critically engage with the intersection of artificial intelligence (AI) and educational transformation through interdisciplinary perspectives. The journal bridges computer science, learning sciences, technology ethics, and public policy to explore the multifaceted impact of AI on global educational systems.
Unlike existing journals that mainly focus on system design or instructional technology, EduAI Nexus uniquely highlights the ethical, policy-related, and equity-driven dimensions of AI integration in education, particularly within both developed and underrepresented contexts. The journal prioritizes research that promotes not only technological advancement but also global educational justice and social sustainability, addressing a critical gap in current AI-education discourse.

Aims & Scope

EduAI Nexus welcomes original research papers, systematic reviews, position papers, and policy reports that adopt interdisciplinary and methodologically diverse approaches to the study of artificial intelligence in educational contexts. Topics of interest include, but are not limited to:

• Ethical, inclusive, and equity-centered design of AI-powered learning environments.
• Educational policy analysis and governance frameworks for AI implementation.
• AI-supported pedagogical transformation in developing countries.
• Personalized learning and bridging educational disparities using AI.
• AI-enabled decision-making systems in educational leadership and management.
• Applications of AI in assessment, learning analytics, and emotion recognition.
• Machine learning and educational data mining.
• Philosophical and ethical critiques of AI in classroom settings.
• Human-AI collaboration for enhancing learner engagement and achievement.
• Empirical, qualitative, quantitative, or mixed-method studies on AI's impact in real-world settings.
• Cross-national and cross-cultural comparative research on AI in education.

Methodological and Theoretical Approaches

EduAI Nexus supports a wide range of methodological and theoretical approaches grounded in the core paradigms of educational, technological, and interdisciplinary research: positivist, interpretive, and critical.

The positivist paradigm includes quantitative methods such as experimental design, large-scale data analysis, surveys, and predictive modeling, often used to assess the effectiveness, accuracy, or performance of AI applications.

The interpretive paradigm includes qualitative approaches such as case studies, phenomenology, ethnography, and grounded theory, which seek to understand how AI is experienced, interpreted, and negotiated by stakeholders in diverse educational settings.

The critical paradigm emphasizes inquiry into power, ethics, equity, and social justice. It includes feminist, postcolonial, participatory, and critical data studies approaches that interrogate algorithmic bias, surveillance, access, and the broader systemic implications of AI in education.

EduAI Nexus also welcomes mixed-methods research, computational and simulation-based studies, and conceptual or philosophical inquiry that critically explores the theoretical foundations and assumptions underlying AI in education. All submissions should demonstrate methodological clarity and epistemological alignment with their chosen research paradigm and educational context.

Publication Categories

EduAI Nexus publishes a range of scholarly article types that reflect methodological diversity and interdisciplinary engagement:

• Research articles, which report original empirical studies using quantitative, qualitative, or mixed-methods approaches. Submissions should clearly state their methodological design, theoretical framework, and relevance to AI's educational implications
• Conceptual articles, which aim to advance theoretical understanding through systematic analysis of existing literature. These may adopt one of the following conceptual approaches: theory synthesis, theory adaptation, typology, or model development. Submissions must articulate a clear theoretical purpose, justify conceptual choices, and present a logically structured argument.
• Review articles, which synthesize and evaluate existing research. This includes systematic reviews, meta-analyses, scoping reviews, integrative reviews, and bibliometric analyses. Submissions must outline their search procedures, inclusion criteria, and analytical strategy.
• Position papers, which offer informed perspectives, theoretical reflections, or normative arguments on emerging debates or challenges at the intersection of AI and education. These articles are typically non-empirical but grounded in scholarly reasoning.
• Policy reports, which analyze regulatory frameworks, ethical standards, institutional practices, or national and global policy landscapes related to AI implementation in education.
• Case studies, which provide descriptive and analytical accounts of specific interventions, systems, or institutional experiences involving AI in education. These may involve empirical investigations in real-world contexts, or reflective or illustrative narratives that examine applied practices and their educational, ethical, or policy implications. Case studies may be situated within any recognized research paradigm.