Extended reality (XR) is well suited to support the situated learning of technical procedures. At the same time, AI-driven intelligent tutoring systems (ITS) can complement XR by providing adaptive pedagogical support. Many domains would benefit from this combination, especially when trainers, equipment, or team members are limited. We present a domain-agnostic XR-based ITS that integrates a training procedure representation (TPR), XR simulation, and an LLM-driven instructor. We demonstrate the tutor's use for tissue sample handling and engine repair, showing how it delivers adaptive feedback, collaborative roleplay, and dynamic scenario management to create realistic and pedagogically meaningful training experiences.

Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication — one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduces a framework that allows instructors to collaborate with large language models to dynamically design realistic scenarios for students to communicate. Our framework uses these scenarios to enable student rehearsal, provide immediate feedback and visualize performance for both students and instructors. Unlike traditional intelligent tutoring systems, instructors can easily co-create scenarios with a large language model without technical skills. Additionally, the system generates new scenario branches in real time when existing options don't fit the student's response.

Limited training capacity has contributed to a critical shortage of licensed commercial pilots.Adaptive educational technologies and simulators could alleviate current training bottlenecks if these technologies could assess trainee performance and provide appropriate feedback.Agents can be used to assess trainee performance, but there is insufficient guidance on how to provide concurrent feedback in simulation-based learning environments.So, we designed 4 feedback conditions that provide varying degrees of elaboration and used a within-subject study (𝑛 = 20) to compare feedback approaches.Trainee performance was best when they received highly-elaborative feedback that modeled expert behaviour.Variability in participant performance and preferences indicates a need to adapt the feedback type to individual learners and provides insight into the use of concurrent feedback in simulation-based learning environments.Specifically, learners appreciated the expert model because it facilitated a sense of control which was associated with lower negative affect and lower extraneous cognitive load.

We propose an AI-based pilot trainer to help students learn how to fly aircraft. First, an AI agent uses behavioral cloning to learn flying maneuvers from qualified flight instructors. Later, the system uses the agent's decisions to detect errors made by students and provide feedback to help students correct their errors. This paper presents an instantiation of the pilot trainer. We focus on teaching straight and level flying maneuvers by automatically providing formative feedback to the human student.