Carrie Demmans Epp

Michael Guevarra, Srijita Das, Christabel Wayllace et al.

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.

Michael Guevarra, Indronil Bhattacharjee, Srijita Das et al.

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 do not fit the student's response.

Minghao Cai, Gokce Akcayir, Carrie Demmans Epp

Augmented Reality (AR) learning games, on average, have been shown to have a positive impact on student learning. However, the exploration of AR learning games in special education settings, where accessibility is a concern, has not been well explored. Thus, the purpose of this study is to explore the use of AR games in accessible learning applications and to provide a comprehensive understanding of its advantages over traditional learning approaches. In this paper, we present our systematic review of previous studies included in major databases in the past decade. We explored the characteristics of user evaluation, learning effects on students, and features of implemented systems mentioned in the literature. The results showed that AR game applications can promote students learning activities from three perspectives: cognitive, affective, and retention. We also found there were still several drawbacks to current AR learning game designs for special needs despite the positive effects associated with AR game use. Based on our findings, we propose potential design strategies for future AR learning games for accessible education.

Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan et al.

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.