Personality-Based Tailored Explainable Recommendation for Trustworthy Smart Learning System in the Age of Artificial Intelligence

Autor/inn/en	Takami, Kyosuke; Flanagan, Brendan; Dai, Yiling; Ogata, Hiroaki
Titel	Personality-Based Tailored Explainable Recommendation for Trustworthy Smart Learning System in the Age of Artificial Intelligence
Quelle	In: Smart Learning Environments, 10 (2023), Artikel 65 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Takami, Kyosuke)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40561-023-00282-6
Schlagwörter	Artificial Intelligence; Personality; Cognitive Processes; Public Health; Individualized Instruction; High School Students; Intervention; Tests; Electronic Learning + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Personalität; Cognitive process; Kognitiver Prozess; Gesundheitswesen; Individualisierender Unterricht; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Examination; Prüfung; Examen
Abstract	In the age of artificial intelligence (AI), trust in AI systems is becoming more important. Explainable recommenders, which explain why an item is recommended, have recently been proposed in the field of learning technology to improve transparency, persuasiveness, and trustworthiness. However, the methods for generating explanations are limited and do not consider the learner's cognitive perceptions or personality. This study draws inspiration from tailored intervention research in public health and investigates the effectiveness of personality-based tailored explanations by implementing them for the recommended quizzes in an explainable recommender system. High school students (n = 217) were clustered into three distinct profiles labeled Diligent (n = 77), Fearful (n = 72), and Agreeable (n = 68), based on the Big Five personality traits. The students were divided into a tailored intervention group (n = 106) and a control group (n = 111). In the tailored intervention group, personalized explanations for recommended quizzes were provided based on student profiles, with explanations based on quiz characteristics. In the control group, only non-personalized explanations based on quiz characteristics were provided. An 18-day A/B experiment showed that the tailored intervention group had significantly higher recommendation usage than the control group. These results suggest that personality-based tailored explanations with a recommender approach are effective for e-learning engagement and imply improved trustworthiness of AI learning systems. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01