The field of educational technology is witnessing significant advancements with the integration of Artificial Intelligence (AI) and multi-agent systems. Researchers are exploring innovative approaches to improve student learning outcomes, teacher professional development, and assessment methods. One notable direction is the use of large language models (LLMs) to drive interactive learning environments, simulate teaching-learning conversations, and provide adaptive feedback. Additionally, multi-agent systems are being designed to enhance the accuracy and consistency of educational evaluations, particularly in code-oriented assessments. These developments have the potential to provide scalable, flexible, and human-like supervision in simulation-based education. Noteworthy papers include:

• A Fuzzy Supervisor Agent Design for Clinical Reasoning Assistance, which presents a novel component for adaptive feedback in clinical scenario simulations.
• AGACCI, a multi-agent system that improves accuracy and interpretability in code-oriented assessments.
• Conversational Education at Scale, a multi-LLM agent workflow for procedural learning and pedagogic quality assessment.