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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. Multimodal analytics for collaborative teacher reflection of human-ai hybrid teaching: design opportunities and constraints

   https://doi.org/10.1007/978-3-031-42682-7_45  

   Karumbaiah, S.; Liu, P.; Maksimova, A.; De Vylder, L.; Rummel, N.; Aleven, V. (August 2023, Springer)

   Viberg, O.; Jivet, I.; Muñoz-Merino, P.; Perifanou, M.; Papathoma, T. (Ed.)

   Past research shows that teachers benefit immensely from reflecting on their classroom practices. At the same time, adaptive and artificially intelligent (AI) tutors are shown to be highly effective for students, especially when teachers are involved in supporting students’ learning. Yet, there is little research on how to support teachers to reflect on their practices around AI tutors. We posit that analytics built on multimodal data from the classroom (e.g., teacher position, student-AI interaction) would be beneficial in providing effective scaffolding and evidence for teachers’ collaborative reflection on human-AI hybrid teaching. To better understand the design opportunities and constraints of a future tool for teacher reflection, we conducted storyboarding sessions with seven in-service teachers. Our analysis revealed that certain modalities (e.g., position v. video) might be more beneficial and less constrained than others in identifying reflection-worthy moments and trends. We discuss teachers’ needs for reflection in classrooms with AI tutors and their boundaries in using multimodal analytics. 

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3. A conceptual framework for human–AI hybrid adaptivity in education

   https://doi.org/10.1007/978-3-030-52237-7_20  

   Holstein K., Aleven V. (January 2020, Artificial Intelligence in Education. AIED 2020)

   Educational AI (AIEd) systems are increasingly designed and evaluated with an awareness of the hybrid nature of adaptivity in real-world educational settings. In practice, beyond being a property of AIEd systems alone, adaptivity is often jointly enacted by AI systems and human facilitators (e.g., teachers or peers). Despite much recent research activity, theoretical and conceptual guidance for the design of such human–AI systems remains limited. In this paper we explore how adaptivity may be shared across AIEd systems and the various human stakeholders who work with them. Based on a comparison of prior frameworks, which tend to examine adaptivity in AIEd systems or human coaches separately, we first synthesize a set of dimensions general enough to capture human–AI hybrid adaptivity. Using these dimensions, we then present a conceptual framework to map distinct ways in which humans and AIEd systems can augment each other’s abilities. Through examples, we illustrate how this framework can be used to characterize prior work and envision new possibilities for human–AI hybrid approaches in education. 

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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. Envisioning AI for K-12: What Should Every Child Know about AI?

   https://doi.org/10.1609/aaai.v33i01.33019795  

   Touretzky, David; Gardner-McCune, Christina; Martin, Fred; Seehorn, Deborah (July 2019, Proceedings of the AAAI Conference on Artificial Intelligence)

   The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work. 

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