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1. Surveying Teachers’ Preferences and Boundaries regarding Human-AI Control in Dynamic Pairing of Students for Collaborative Learning

   Yang, K.; Lawrence, L.; Echeverria, V.; Guo, B.; Holstein, K.; Rummel, N.; Aleven, V. (January 2021, European Conference on Technology Enhanced Learning, EC-TEL 2021)

   null (Ed.)

   Orchestration tools may support K-12 teachers in facilitating student learning, especially when designed to address classroom stakeholders’ needs. Our previous work revealed a need for human-AI shared control when dynamically pairing students for collaborative learning in the classroom, but offered limited guidance on the role each agent should take. In this study, we designed storyboards for scenarios where teachers, students and AI co-orchestrate dynamic pairing when using AI-based adaptive math software for individual and collaborative learning. We surveyed 54 math teachers on their co-orchestration preferences. We found that teachers would like to share control with the AI to lessen their orchestration load. As well, they would like to have the AI propose student pairs with explanations, and identify risky proposed pairings. However, teachers are hesitant to let the AI auto-pair students even if they are busy, and are less inclined to let AI override teacher-proposed pairing. Our study contributes to teachers’ needs, preference, and boundaries for how they want to share the task and control of student pairing with the AI and students, and design implications in human-AI co-orchestration tools. 

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2. A spatiotemporal analysis of teacher practices in supporting student learning and engagement in an AI-enabled classroom

   https://doi.org/10.1007/978-3-031-36272-9_37  

   Karumbaiah, S.; Borchers, C.; Falhs, A. C.; Holstein, K.; Rummel, N.; Aleven, V. (July 2023, Springer)

   Wang, N.; Rebolledo-Mendez, G.; Matsuda, N.; Santos, O.C.; Dimitrova, V. (Ed.)

   Research indicates that teachers play an active and important role in classrooms with AI tutors. Yet, our scientific understanding of the way teacher practices around AI tutors mediate student learning is far from complete. In this paper, we investigate spatiotemporal factors of student-teacher interactions by analyzing student engagement and learning with an AI tutor ahead of teacher visits (defined as episodes of a teacher being in close physical proximity to a student) and immediately following teacher visits. To conduct such integrated, temporal analysis around the moments when teachers visit students, we collect fine-grained, time-synchronized data on teacher positions in the physical classroom and student interactions with the AI tutor. Our case study in a K12 math classroom with a veteran math teacher provides some indications on factors that might affect a teacher’s decision to allocate their limited classroom time to their students and what effects these interactions have on students. For instance, teacher visits were associated more with students’ in-the-moment behavioral indicators (e.g., idleness) than a broader, static measure of student needs such as low prior knowledge. While teacher visits were often associated with positive changes in student behavior afterward (e.g., decreased idleness), there could be a potential mismatch between students visited by the teacher and who may have needed it more at that time (e.g., students who were disengaged for much longer). Overall, our findings indicate that teacher visits may yield immediate benefits for students but also that it is challenging for teachers to meet all needs - suggesting the need for better tool support. 

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3. Pattern analysis of ambitious science talk between preservice teachers and AI-powered student agents

   https://doi.org/10.1145/3706468.3706570  

   Barrett, Alex; Ke, Fengfeng; Zhang, Nuodi; Dai, Chih-Pu; Bhowmik, Saptarshi; Yuan, Xin (March 2025, ACM)

   New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of teacher training simulations, but little is known about how preservice teachers interact with AI-powered student agents. This study analyzed the discourse behavior of 15 preservice teachers who undertook simulation-based training with AI-powered student agents. Using a framework of ambitious science teaching, we conducted a pattern analysis of teacher and student talk moves, looking for evidence of academically productive discourse. Comparisons are made with patterns found in real classrooms with professionally trained science teachers. Results indicated that preservice teachers generated academically productive discourse with AI-powered students by using ambitious talk moves. The pattern analysis also revealed coachable moments where preservice teachers succumbed to cycles of unproductive discourse. This study highlights the utility of analyzing classroom discourse to understand human-AI communication in simulation-based teacher training. 

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4. How Expertise Levels Shape Preferences and Reflection Needs: Towards AI Reflection Systems for Teacher Empowerment

   https://doi.org/10.1007/978-3-032-03873-9_11  

   Falhs, Ann-Christin; Borchers, Conrad; Echeverria, Vanessa; Yang, Kexin; Rummel, Nikol; Aleven, Vincent (September 2025, Springer Nature Switzerland)

   Tammets, K; Sosnovsky, S; Ferreira_Mello, R; Pishtari, G; Nazaretsky, T (Ed.)

   The increasing use of AI systems opens up opportunities to use learner and teacher-related data for reflection, supporting instruction and professional growth. However, teacher-related data is rarely collected in technology-enhanced learning environments. Understanding teacher preferences can inform the design of AI reflection systems that align with their priorities. The current survey study explores how K-12 teachers' reflection practices and preferences for analytic data types (e.g., learning-related vs. engagement-related) vary by experience, an aspect often overlooked in prior works. Findings from our survey study with N=100 teachers in the U.S. revealed significant differences. Less experienced teachers focus on classroom behavior and prefer analytics on classroom issues, while experienced teachers prefer broader strategies to improve student learning and favor sustained data analysis. Our results reflect the need to consider teachers’ experience level by tuning the complexity and concreteness of analytic recommendations to support ongoing professionalization. 

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5. RAISE: Robotics & AI to improve STEM and social skills for elementary school students

   https://doi.org/10.3389/frvir.2022.968312  

   Hughes, Charles E.; Dieker, Lisa A.; Glavey, Eileen M.; Hines, Rebecca A.; Wilkins, Ilene; Ingraham, Kathleen; Bukaty, Caitlyn A.; Ali, Kamran; Shah, Sachin; Murphy, John; et al (October 2022, Frontiers in Virtual Reality)

   The authors present the design and implementation of an exploratory virtual learning environment that assists children with autism (ASD) in learning science, technology, engineering, and mathematics (STEM) skills along with improving social-emotional and communication skills. The primary contribution of this exploratory research is how educational research informs technological advances in triggering a virtual AI companion (AIC) for children in need of social-emotional and communication skills development. The AIC adapts to students’ varying levels of needed support. This project began by using puppetry control (human-in-the-loop) of the AIC, assisting students with ASD in learning basic coding, practicing their social skills with the AIC, and attaining emotional recognition and regulation skills for effective communication and learning. The student is given the challenge to program a robot, Dash™, to move in a square. Based on observed behaviors, the puppeteer controls the virtual agent’s actions to support the student in coding the robot. The virtual agent’s actions that inform the development of the AIC include speech, facial expressions, gestures, respiration, and heart color changes coded to indicate emotional state. The paper provides exploratory findings of the first 2 years of this 5-year scaling-up research study. The outcomes discussed align with a common approach of research design used for students with disabilities, called single case study research. This type of design does not involve random control trial research; instead, the student acts as her or his own control subject. Students with ASD have substantial individual differences in their social skill deficits, behaviors, communications, and learning needs, which vary greatly from the norm and from other individuals identified with this disability. Therefore, findings are reported as changes within subjects instead of across subjects. While these exploratory observations serve as a basis for longer term research on a larger population, this paper focuses less on student learning and more on evolving technology in AIC and supporting students with ASD in STEM environments. 

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