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

Öz

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.

Anahtar Kelimeler

• Motivation
• ARCS-V Motivation Model
• Motivational Systems Theory
• Artificial Intelligence-Assisted Instructional Design

ARCS-V ve MST motivasyon modellerinin yapay zekâ destekli uzaktan eğitim tasarımıyla bütünleştirilmesi: Sinerjik bir yaklaşım

Öz

Bu çalışma, ARCS-V (Dikkat, Alaka, Güven, Memnuniyet ve İstek) modelini ve Motivasyon Sistemleri Teorisini (MST) yapay zekâ destekli uzaktan eğitim ortamlarına entegre eden yeni bir çerçeve önermektedir. Önerilen çerçeve ile bu modellerin entegrasyonunun, YZ destekli öğrenci motivasyonunu daha bütüncül bir şekilde nasıl destekleyebileceği gösterilmektedir. YZ araçları ile motivasyon değerlendirme, uyarlanabilir müdahaleler ve sinerjik destek mekanizmalarını birleştirerek öğrenci ihtiyaçlarına göre özelleştirilmiş uzaktan eğitim ortamları geliştirilebilir. İlgi çekici ve doyurucu öğrenme deneyimleri sağlamaya odaklanan ARCS-V modelinin güçlü yönlerini, kişisel hedeflerin, duyguların ve çevresel faktörlerin önemini vurgulayan MST ile birleştiren bu yeni yaklaşım, öğrenci motivasyonunu sürdürmek için daha bütünsel ve etkili bir yol önermektedir. ARCS-V ve MST modellerinin uzaktan eğitim ortamlarına Yapay Zekanın değerlendirme, müdahale ve destek boyutlarıyla nasıl birleştirilebileceği incelenmektedir. Uzaktan eğitimde bu iki motivasyon modelinin yapay zekâ desteğiyle bütünleştirilmesi ile yalnızca içeriğin etkili sunumu değil aynı zamanda öğrenci katılımının da arttırılması sağlanabilir.

Anahtar Kelimeler

• Motivation
• Open and Distance Learning
• ARCS-V Motivation Model
• MST Motivation Model
• Instructional design
• Adaptive learning
• Artificial Intelligence

Kaynakça

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