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Title: Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning
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. more »« less
Yang, K.; Lu, Z.; Echeverria, V.; Sewall, J.; Lawrence, L.; Rummel, N.; & Aleven, V.
(, Proceedings of the 23rd International Conference on Artificial Intelligence in Education, AIED 2023)
Rodrigo, M.M.; Matsuda, N.; Cristea, A.I.; Dimitrova, V.
(Ed.)
It might be highly effective if students could transition dynamically between individual and collaborative learning activities, but how could teachers manage such complex classroom scenarios? Although recent work in AIED has focused on teacher tools, little is known about how to orchestrate dynamic transitions between individual and collaborative learning. We created a novel technology ecosystem that supports these dynamic transitions. The ecosystem integrates a novel teacher orchestration tool that provides monitoring support and pairing suggestions with two AI-based tutoring systems that support individual and collaborative learning, respectively. We tested the feasibility of this ecosystem in a classroom study with 5 teachers and 199 students over 22 class sessions. We found that the teachers were able to manage the dynamic transitions and valued them. The study contributes a new technology ecosystem for dynamically transitioning between individual and collaborative learning, plus insight into the orchestration functionality that makes these transitions feasible.
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.
Yang, Kexin Bella; Echeverria, Vanessa; Lu, Zijing; Mao, Hongyu; Holstein, Kenneth; Rummel, Nikol; Aleven, Vincent
(, 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.
Yang, K.; Lawrence, L.; Echeverria, V.; Guo, B.; Holstein, K.; Rummel, N.; Aleven, V.
(, 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.
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.
Echeverria, V. Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning. Retrieved from https://par.nsf.gov/biblio/10189165. Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020 . Web. doi:10.1007/978-3-030-57717-9_17.
Echeverria, V. Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning. Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020, (). Retrieved from https://par.nsf.gov/biblio/10189165. https://doi.org/10.1007/978-3-030-57717-9_17
Echeverria, V.
"Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning". Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020 (). Country unknown/Code not available. https://doi.org/10.1007/978-3-030-57717-9_17.https://par.nsf.gov/biblio/10189165.
@article{osti_10189165,
place = {Country unknown/Code not available},
title = {Exploring Human–AI Control Over Dynamic Transitions Between Individual and Collaborative Learning},
url = {https://par.nsf.gov/biblio/10189165},
DOI = {10.1007/978-3-030-57717-9_17},
abstractNote = {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.},
journal = {Proceedings of the 15th European Conference on Technology-Enhanced Learning, EC-TEL 2020},
author = {Echeverria, V.},
}
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