Designing for human–AI complementarity in K‐12 education. Issue 2 (23rd June 2022)

• Record Type: Journal Article
• Authors: Holstein, Kenneth; Aleven, Vincent
• Is Part Of: AI magazine. Volume 43:Issue 2 (2022)
• Page Start: 239
• Page End: 248
• Publication Date: 2022-06-23
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; System design -- Periodicals; Artificial intelligence -- Technological innovations -- Periodicals; Artificial intelligence -- Industrial applications -- Periodicals; Artificial intelligence -- Educational applications -- Periodicals; Intelligence artificielle -- Périodiques; Intelligence artificielle -- Logiciels -- Périodiques; Conception de systèmes -- Périodiques; Intelligence artificielle -- Innovations -- Périodiques; Intelligence artificielle -- Applications industrielles -- Périodiques; Intelligence artificielle -- Applications en éducation -- Périodiques; Artificial intelligence; Artificial intelligence -- Computer programs; Artificial intelligence -- Educational applications; Artificial intelligence -- Industrial applications; System design; Kunstmatige intelligentie; Electronic journals; Periodicals; Periodicals; 006.305
• DOI: 10.1002/aaai.12058
• Languages: English
• ISSNs: 0738-4602

Abstract

Abstract: 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.