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Michael Guevarra

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
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3

AAAI Conference 2026 System Paper

TPR: A Training Procedure Representation to Augment XR Simulations with LLMs

  • Michael Guevarra
  • Christabel Wayllace
  • Srijita Das
  • Carrie Demmans Epp
  • Alan Tay

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.

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AAAI Conference 2025 System Paper

An LLM-Guided Tutoring System for Social Skills Training

  • Michael Guevarra
  • Indronil Bhattacharjee
  • Srijita Das
  • Christabel Wayllace
  • Carrie Demmans Epp
  • Matthew E. Taylor
  • Alan Tay

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.

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AAAI Conference 2023 System Paper

Augmenting Flight Training with AI to Efficiently Train Pilots

  • Michael Guevarra
  • Srijita Das
  • Christabel Wayllace
  • Carrie Demmans Epp
  • Matthew Taylor
  • Alan Tay

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.

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