Integrating ARCS-V and MST motivation models into AI-supported distance education design: A synergistic approach

Authors : Harun Serpil, Cemil Şahin

Pages : 38-61

Doi:10.51948/auad.1542975

View : 39 | Download : 45

Publication Date : 2025-01-28

Article Type : Review Paper

Abstract :This article proposes a new framework that integrates the ARCS-V (Attention, Relevance, Confidence, Satisfaction, and Volition) model and Motivational Systems Theory (MST) into AI-supported distance learning environments. The proposed framework shows how the integration of these models can support AI-supported student motivation in a more holistic way. By combining AI tools with motivation assessment, adaptive interventions and synergistic support mechanisms, customized distance learning environments can be developed according to student needs. Combining the strengths of the ARCS-V model, which focuses on providing engaging and satisfying learning experiences, with MST, which emphasizes the importance of personal goals, emotions, and environmental factors, this new approach suggests a more holistic and effective way to sustain student motivation. The paper examines how the ARCS-V and MST models can be combined with the assessment, intervention and support dimensions of Artificial Intelligence in distance education settings. By integrating these two motivational models in ODL with the support of AI, not only effective presentation of content but also increased student engagement can be achieved.
Keywords : Motivation, Open and Distance Learning, ARCS-V Motivation Model, MST Motivation Model, Instructional design, Adaptive learning, Artificial Intelligence