Explaining Recommendations in E-Learning: Effects on Adolescents' Trust

In the scope of explainable artificial intelligence, explanation techniques are heavily studied to increase trust in recommender systems. However, studies on explaining recommendations typically target adults in e-commerce or media contexts; e-learning has received less research attention. To address these limits, we investigated how explanations affect adolescents' initial trust in an e-learning platform that recommends mathematics exercises with collaborative filtering. In a randomized controlled experiment with 37 adolescents, we compared real explanations with placebo and no explanations. Our results show that real explanations significantly increased initial trust when trust was measured as a multidimensional construct of competence, benevolence, integrity, intention to return, and perceived transparency. Yet, this result did not hold when trust was measured one-dimensionally. Furthermore, not all adolescents attached equal importance to explanations and trust scores were high overall. These findings underline the need to tailor explanations and suggest that dynamically learned factors may be more important than explanations for building initial trust. To conclude, we thus reflect upon the need for explanations and recommendations in e-learning in low-stakes and high-stakes situations.