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1. Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom

   https://doi.org/10.1145/3544548.3581398  

   Yang, Kexin Bella ; Echeverria, Vanessa ; Lu, Zijing ; Mao, Hongyu ; Holstein, Kenneth ; Rummel, Nikol ; Aleven, Vincent ( April 2023 , CHI ‘23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Schmidt, A. ; Väänänen, K. ; Goyal, T. ; Kristensson, P. O. ; Peters, A. ; Mueller, S. ; Williamson, J. R. ; Wilson, M. L. (Ed.)

   Enabling students to dynamically transition between individual and collaborative learning activities has great potential to support better learning. We explore how technology can support teachers in orchestrating dynamic transitions during class. Working with five teachers and 199 students over 22 class sessions, we conducted classroom-based prototyping of a co-orchestration technology ecosystem that supports the dynamic pairing of students working with intelligent tutoring systems. Using mixed-methods data analysis, we study the resulting observed classroom dynamics, and how teachers and students perceived and experienced dynamic transitions as supported by our technology. We discover a potential tension between teachers’ and students’ preferred level of control: students prefer more control over the dynamic transitions that teachers are hesitant to grant. Our study reveals design implications and challenges for future human-AI co-orchestration in classroom use, bringing us closer to realizing the vision of highly-personalized smart classrooms that can address the unique needs of each student. 

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2. How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process

   https://doi.org/10.1111/bjet.13372  

   Lawrence, LuEttaMae ; Echeverria, Vanessa ; Yang, Kexin ; Aleven, Vincent ; Rummel, Nikol ( August 2023 , British Journal of Educational Technology)

   Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, calledPair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control withPair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems.

   What is already known about this topic:

   Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.

   Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.

   What this paper adds:

   Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.

   Describes how we adapted six design methods to design AI features.

   Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.

   Implications for practice:

   We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.

   Our work also includes a reflection on our design process and implications for future design processes.

    

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3. 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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4. 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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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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