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1. Co-designing AI-based orchestration tools to support dynamic transitions: Design narratives through conjecture mapping

   Lawrence, L.; Guo, B.; Echeverria, V.; Kang, Z.; Bathala, V.; Li, C.; Huang, W.; Aleven, V.; & Rummel, N. (January 2022, Computersupported collaborative learning)

   Weinberger, A.; Chen, W.; Hernández-Leo, D.; Chen, B. (Ed.)

   Dynamically transitioning between individual and collaborative learning has been hypothesized to have positive effects, such as providing the optimal learning mode based on students’ needs. There are, however, challenges in orchestrating these transitions in real-time while managing a classroom of students. AI-based orchestration tools have the potential to alleviate some of the orchestration load for teachers. In this study, we describe a sequence of three design sessions with teachers where we refine prototypes of an orchestration tool to support dynamic transitions. We leverage design narratives and conjecture mapping for the design of our novel orchestration tool. Our contributions include the orchestration tool itself; a description of how novel tool features were revised throughout the sessions with teachers, including shared control between teachers, students, and AI and the use of AI to support dynamic transitions, and a reflection of the changes to our design and theoretical conjectures. 

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2. Using Participatory Design Studies to Collaboratively Create Teacher Dashboards

   Dey, Indrani; Dickler, Rachel; Hirshfield, Leanne; Goss, William; Tissenbaum, Mike; Puntambekar, Sadhana (July 2022, International Conference on Artificial Intelligence in Education)

   Classroom orchestration requires teachers to concurrently manage multiple activities across multiple social levels (individual, group, and class) and under various constraints. Real-time dashboards can support teachers; however, designing actionable dashboards is a huge challenge. This paper describes a participatory design study to identify and inform critical features of a dashboard for displaying relevant, actionable, real-time data. We leveraged a Sense-Assess-Act framework to present dashboard mockups to teachers for feedback. Although the participating teachers differed in how they would use the presented information (during class or after class as a post hoc analysis tool), two common emerging themes were that they wanted to use the data to a) better support their students and b) to make broader instructional decisions. We present data from our study and propose a customizable, mobile dashboard, that can be adapted to a teacher's specific needs at a specific time, to help them better facilitate learning activities. 

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3. 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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4. Explorations of Designing Spatial Classroom Analytics with Virtual Prototyping

   https://doi.org/10.1145/3448139.3448192  

   Jang, JiWoong; Lee, Jaewook; Echeverria, Vanessa; Lawrence, LuEttaMae; Aleven, Vincent (April 2021, Proceedings of the 11th International Learning Analytics & Knowledge Conference (LAK21))

   null (Ed.)

   Despite the potential of spatial displays for supporting teachers’ classroom orchestration through real-time classroom analytics, the process to design these displays is a challenging and under-explored topic in the learning analytics (LA) community. This paper proposes a mid-fidelity Virtual Prototyping method (VPM), which involves simulating a classroom environment and candidate designs in virtual space to address these challenges. VPM allows for rapid prototyping of spatial features, requires no specialized hardware, and enables teams to conduct remote evaluation sessions. We report observations and findings from an initial exploration with five potential users through a design process utilizing VPM to validate designs for an AR-based spatial display in the context of middle-school orchestration tools. We found that designs created using virtual prototyping sufficiently conveyed a sense of three-dimensionality to address subtle design issues like occlusion and depth perception. We discuss the opportunities and limitations of applying virtual prototyping, particularly its potential to allow for more robust co-design with stakeholders earlier in the design process. 

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5. Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning

   https://doi.org/10.1007/978-3-030-57717-9_17  

   Echeverria, V. (January 2020, Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020)

   Dynamically transitioning between individual and collaborative learning activities during a class session (i.e., in an un-planned way, as-the-need-arises) has advantages for students, but existing orchestration tools are not designed to support such transitions. This work reports findings from a technology probe study that explored alternative designs for classroom co-orchestration support for dynamically transitioning between individual and collaborative learning, focused on how control over the transitions should be divided or shared among teachers, students, and orchestration system. This study involved 1) a pilot in an authentic classroom scenario with AI support for individual and collaborative learning; and 2) design workshops and interviews with students and teachers. Findings from the study suggest the need for hybrid control between students, teachers, and AI systems over transitions as well as for adaptivity and/or adaptability for different classrooms, teachers, and students’ prior knowledge. This study is the first to explore human–AI control over dynamic transitions between individual and collaborative learning in actual classrooms. 

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