The field of AI-enhanced education and human interaction is rapidly evolving, with a focus on developing innovative solutions that improve learning experiences, teaching effectiveness, and human connection. Recent research has explored the potential of augmented intelligence, large language models, and conversational AI to support personalized learning, intelligent tutoring systems, and emotionally rich exchanges. Notably, the development of teacher-centered design approaches, scalable frameworks for evaluating teaching effectiveness, and tools for collecting scaffolding dialogues between experts and novices are advancing the field. Furthermore, the creation of benchmarks for guided instruction capabilities in STEM education and the evaluation of LLM-guided reflection on learning outcomes are driving progress.

Particularly noteworthy papers include: The paper on MathAIde app, which presents a mixed user-centered approach to enable augmented intelligence in intelligent tutoring systems. The paper on SimInstruct, which introduces a responsible tool for collecting scaffolding dialogues between experts and LLM-simulated novices. The paper on SID, which benchmarks guided instruction capabilities in STEM education with a Socratic interdisciplinary dialogues dataset.