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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. 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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4. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   null (Ed.)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps us see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years. 

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5. 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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