Author name cluster

Carrie Demmans Epp

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.

4 papers

1 author row

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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IJCAI Conference 2022 Conference Paper

Learning Curricula for Humans: An Empirical Study with Puzzles from The Witness

Levi H. S. Lelis
João G. G. V. Nova
Eugene Chen
Nathan R. Sturtevant
Carrie Demmans Epp
Michael Bowling

The combination of tree search and neural networks has achieved super-human performance in challenging domains. We are interested in transferring to humans the knowledge these learning systems generate. We hypothesize the process in which neural-guided tree search algorithms learn how to solve a set of problems can be used to generate curricula for helping human learners. In this paper we show how the Bootstrap learning system can be modified to learn curricula for humans in a puzzle domain. We evaluate our system in two curriculum learning settings. First, given a small set of problem instances, our system orders the instances to ease the learning process of human learners. Second, given a large set of problem instances, our system returns a small ordered subset of the initial set that can be presented to human learners. We evaluate our curricula with a user study where participants learn how to solve a class of puzzles from the game `The Witness. ' The user-study results suggest one of the curricula our system generates compares favorably with simple baselines and is competitive with the curriculum from the original `The Witness' game in terms of user retention and effort.

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