Recent work has explored how complementary strengths of humans and artificial intelligence (AI) systems might be productively combined. However, successful forms of human–AI partnership have rarely been demonstrated in real‐world settings. We present the iterative design and evaluation of Lumilo, smart glasses that help teachers help their students in AI‐supported classrooms by presenting real‐time analytics about students’ learning, metacognition, and behavior. Results from a field study conducted in K‐12 classrooms indicate that students learn more when teachers and AI tutors work together during class. We discuss implications of this research for the design of human–AI partnerships. We argue for more participatory approaches to research and design in this area, in which practitioners and other stakeholders are deeply, meaningfully involved throughout the process. Furthermore, we advocate for theory‐building and for principled approaches to the study of human–AI decision‐making in real‐world contexts.
Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms
As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K-12 teachers and students. Using human-centered design methods rarely employed in AIED research, this work builds on prior findings to contribute: (1) an analysis of teacher and student feedback on 24 design concepts for systems that integrate human and AI instruction; and (2) participatory speed dating (PSD): a new variant of the speed dating design method, involving iterative concept generation and evaluation with multiple stakeholders. Using PSD, we found that teachers desire greater real-time support from AI tutors in identifying when students need human help, in evaluating the impacts of their own help-giving, and in managing student motivation. Meanwhile, students desire better mechanisms to signal help-need during class without losing face to peers, to receive emotional support from human rather than AI tutors, and to have greater agency over how their personal analytics are used. This work provides tools and insights to guide the design of more effective human–AI partnerships for K-12 education.
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- Award ID(s):
- 1822861
- PAR ID:
- 10113024
- Date Published:
- Journal Name:
- Proceedings, 20th International Conference on Artificial Intelligence in Education, AIED 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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.more » « less
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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.more » « less
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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.more » « less
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Wang, N. ; Rebolledo-Mendez, G. ; Matsuda, N. ; Santos, O.C. ; Dimitrova, V. (Ed.)Research indicates that teachers play an active and important role in classrooms with AI tutors. Yet, our scientific understanding of the way teacher practices around AI tutors mediate student learning is far from complete. In this paper, we investigate spatiotemporal factors of student-teacher interactions by analyzing student engagement and learning with an AI tutor ahead of teacher visits (defined as episodes of a teacher being in close physical proximity to a student) and immediately following teacher visits. To conduct such integrated, temporal analysis around the moments when teachers visit students, we collect fine-grained, time-synchronized data on teacher positions in the physical classroom and student interactions with the AI tutor. Our case study in a K12 math classroom with a veteran math teacher provides some indications on factors that might affect a teacher’s decision to allocate their limited classroom time to their students and what effects these interactions have on students. For instance, teacher visits were associated more with students’ in-the-moment behavioral indicators (e.g., idleness) than a broader, static measure of student needs such as low prior knowledge. While teacher visits were often associated with positive changes in student behavior afterward (e.g., decreased idleness), there could be a potential mismatch between students visited by the teacher and who may have needed it more at that time (e.g., students who were disengaged for much longer). Overall, our findings indicate that teacher visits may yield immediate benefits for students but also that it is challenging for teachers to meet all needs - suggesting the need for better tool support.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-23204-7_14
