Artificial intelligence in distance education: a systematic review of emerging pedagogical, cognitive and institutional dynamics

Purpose The study aims to systematically review and synthesize findings from peer-reviewed empirical and conceptual studies published between 2020 and 2025 to examine how AI is presently conceptualized, designed and enacted within distance education learning environments. Design/methodology/approach This review employed a systematic synthesis approach to examine how AI is conceptualized, implemented and studied within distance education contexts. In total, 56 peer reviewed articles were included in the systematic review. The process proceeded in iterative phases. Each article was read in full, with analytic notes created to capture research purposes, AI functions, learning contexts, methodological approaches, findings and implications. Particular attention was given to how the studies conceptualized the role of AI, whether as a tutor, tool, assessor, collaborator, facilitator of self-regulation or institutional mechanism, and how these conceptualizations aligned with distance education delivery. Four interconnected themes that structure discourse on AI in distance education emerged from the review. Findings The review identifies four interrelated themes that capture the evolving role of AI in distance education: (1) AI-driven personalization, learner modeling and adaptative support; (2) AI-mediated assessment, feedback and learning facilitation; (3) human–AI interaction, learning processes and pedagogical transformation and (4) AI governance, fairness, integrity and equity. Research limitations/implications A primary limitation concerns the limited number of studies included in this synthesis. Because the 56 articles included in this review were preselected and provided in full text, the analysis does not claim to represent the entirety of global research on AI in distance education during the period under study. While this approach enabled deep examination in education, it may omit relevant studies published in adjacent fields or disciplinary contexts. Future research would benefit from systematic searches across broader databases and inclusion criteria that capture emerging work in related domains such as computational education research, human–computer interaction and learning sciences. A second limitation is the lack of longitudinal evidence. Most studies reviewed relied on short-term interventions or cross-sectional data, making it difficult to assess the sustained effects of AI on learning, motivation or institutional practices. Longitudinal research is needed to understand how learner's relationships with AI evolve over time, how instructors adapt their pedagogical strategies in response to AI integration, and how institutional policies shift as AI becomes more entrenched in distance education environments. Practical implications In addition to the four themes used to guide discourse involving AI and distance education, what emerges from this systematic review is an understanding that the impacts of AI in distance education extend far beyond technological features. AI reorganizes relationships among learners, teachers and institutions, requiring new pedagogical competencies, new forms of collaboration and new frameworks for accountability. For distance education, long committed to principles of access, flexibility and learner empowerment, the challenge is to integrate AI in ways that uphold these values while leveraging the technology's potential to support high-quality learning. Social implications Educators and institutions must adopt intentional, theory-informed and ethically grounded approaches to AI design and implementation. They must cultivate AI literacies among learners and instructors, develop governance structures that focus on fairness and transparency and invest in research that examines AI's long-term effects on learning and teaching. Through such efforts, the field can move toward forms of AI integration that enhance rather than compromise the core mission of distance education. Originality/value The rapid expansion of AI in distance education presents both transformational opportunities and profound responsibilities. By synthesizing evidence across diverse contexts and methodological traditions, this review provides a foundation for navigating these complexities and for shaping the future of AI-enabled distance education in ways that are pedagogically sound, ethically responsible and attuned to the needs and agency of learners.