Educational Multi-Agents Empowering Interdisciplinary Thematic Learning: Development Logic, Implementation Mechanisms, and Application Strategies

Interdisciplinary thematic learning, as a key approach to breaking through traditional disciplinary boundaries and cultivating core competencies, has become an important direction of global educational reform. However, in practice, it faces structural challenges such as insufficient teachers' capacity to design interdisciplinary themes, limited learning support for students, and the insufficient capture of competencies by traditional assessment systems. Educational multi-agents had undergone an evolution process from instrumentalization to intelligentization, from generalization to scenario-based specialization, and from individualization to collaboration, featuring core characteristics such as distributed cognition and functional specialization, dynamic adaptability and self-organizing evolution, multimodal interaction and situational awareness capability, as well as collaborative decision-making and the emergence of collective intelligence. It also demonstrated multi-dimensional application value in empowering interdisciplinary thematic learning, offering effective tools to address these structural challenges. The realization of educational multi-agents empowering interdisciplinary thematic learning required the adoption of a sequential guidance-driven in design, a parallel role-based coordination empowerment in implementation, and a feedback-based adaptive intervention in evaluation. When applying educational multi-agents to empower interdisciplinary thematic learning, it was necessary to rely on the multi-agents collaboration architecture to maximize system efficiency, create a localized knowledge ecosystems and practical scenarios with multi-intelligent agents, design a human-machine collaboration model centered on learners, establish a closed loop of competency evaluation and intervention supported by multi-agents, and ensure the resource foundation and ethical norms for the operation of multi-agents.