From Monologue to Symphony: A Collaborative Framework for Fusion of ARCS-V Motivation Model and Multi-Agent Systems

Abstract

In light of recent changes to engineering education, one of the primary issues facing higher vocational universities is how to effectively encourage and maintain the learning motivation of students. Lack of interest, low self-esteem, and weak willpower are common factors contributing to a lack of systematic motivation among students, especially in the technology-intensive and practical courses. Most existing instructional systems are limited in their ability to foster collaborative social learning and higher-order cognitive skills because of their “monologue” interaction paradigm and dependence on single-agent assistance. To address this challenge, this paper explores how to construct a collaborative teaching framework that combines the ARCS-V motivation model with an advanced multi-agent system. The framework seeks to establish an ecosystem that can methodically stimulate and sustain students’ attention, relevance, self-confidence, satisfaction, and willpower by establishing multi-role agents like experts, mentors, and peers. Quasi-experimental research method was adopted to compare the effect of multi-agent learning environment with traditional teaching mode. The results show that multi-agent environment can significantly improve students’ learning motivation, and their academic performance in high-order cognitive dimensions such as reasoning and application is better, which effectively promotes deep learning cognitive level. Using state-of-the-art AI technology, this collaborative framework provides a comprehensive theoretical scheme for methodically resolving learning motivation issues and developing the advanced skills needed by upcoming engineering talents.

Affiliated Institutions

• Shenzhen Polytechnic CN

Related Publications