Generative artificial intelligence (GenAI) is driving education toward a human–machine collaborative paradigm, reshaping teaching and learning interaction. In distance education, the separation of instructor and learner across time and space produces transactional distance—a psychological and communicative gap that constrains learning effectiveness—which neither classical theory nor conventional technology fully addresses in the intelligent era. Grounded in Moore's theory of transactional distance and drawing on Holmberg's guided didactic conversation, connectivism, and Vygotskian scaffolding, this study advances a conceptual framework explaining how GenAI regulates transactional distance through two mutually reinforcing pathways: strengthening dialogue quality and flexibilizing course structure. We construct a "technology–dimension–distance" dynamic model in which GenAI is the intervening variable, dialogue quality and structural flexibility are chain mediators, transactional distance is the outcome, and learner characteristics moderate the mediating paths. We articulate the internal mechanisms of this dual-path regulation, formalize the argument as testable propositions, and derive practical guidelines for dialogue design, structural flexibility, and ethical governance, offering actionable guidance for platform design, curriculum structuring, and resource allocation in intelligent distance education.

Jingru He, Yuqian Zhao, Chengbo Wang. Optimizing Transactional Distance in Distance Education under Generative Artificial Intelligence Intervention—A Dual-Dimensional Framework of Dialogue Quality and Structural Flexibility. Frontiers in Educational Research (2026), Vol. 9, Issue 9: 12-19. https://doi.org/10.25236/FER.2026.090902.

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