Artificial Intelligence and Multimodal Data in the Service of Human Decision-Making: A Case Study in Debate Tutoring

Autor/inn/en	Cukurova, Mutlu; Kent, Carmel; Luckin, Rosemary
Titel	Artificial Intelligence and Multimodal Data in the Service of Human Decision-Making: A Case Study in Debate Tutoring
Quelle	In: British Journal of Educational Technology, 50 (2019) 6, S.3032-3046 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cukurova, Mutlu)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.12829
Schlagwörter	Artificial Intelligence; Data Use; Decision Making; Debate; Tutoring; Accuracy; Classification + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Decision-making; Entscheidungsfindung; Debating; Streitgespräch; Förderkonzept; Nachhilfeunterricht; Classification system; Klassifikation; Klassifikationssystem
Abstract	The question: "What is an appropriate role for AI?" is the subject of much discussion and interest. Arguments about whether AI should be a "human replacing" technology or a "human assisting" technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should play, and as with many other professional areas, the exact role of AI in education is not easy to predict. Here, we argue that one potential role for AI in education is to provide opportunities for human intelligence augmentation, with AI supporting us in decision-making processes, rather than replacing us through automation. To provide empirical evidence to support our argument, we present a case study in the context of debate tutoring, in which we use prediction and classification models to increase the transparency of the intuitive decision-making processes of expert tutors for advanced reflections and feedback. Furthermore, we compare the accuracy of unimodal and multimodal classification models of expert human tutors' decisions about the social and emotional aspects of tutoring while evaluating trainees. Our results show that multimodal data leads to more accurate classification models in the context we studied. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01