Abstract
AbstractIn 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.
Funder
JSPS
New Energy and Industrial Technology Development Organization
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference57 articles.
1. Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision making. IEEE Security & Privacy, 3(1), 26–33.
2. Alkış, N., & Taşkaya Temizel, T. (2015). The impact of individual differences on influence strategies. Personality and Individual Differences, 87, 147–152.
3. Asendorpf, J. B. (2002). The puzzle of personality types. European Journal of Personality, 16(1_suppl), S1–S5.
4. Badrinath, A., Wang, F., & Pardos, Z. (2021). pyBKT: An accessible python library of Bayesian knowledge tracing models (pp. 468–474).
5. Barria-Pineda, J., Akhuseyinoglu, K., Želem-Ćelap, S., Brusilovsky, P., Milicevic, A. K., & Ivanovic, M. (2021). Explainable recommendations in a personalized programming practice system. Artificial Intelligence in Education, 64–76.