This is my work together with Leen Dereu and Katrien Verbert for IUI 2023, a conference on intelligent user interfaces. I am particularly proud of this work because it is based on Leen's master's thesis, which I guided. We studied how a control mechanism and a visualisation that depicts the control's impact affected adolescents' trust in a platform that recommends mathematics exercises.
Paper
Download the preprint underneath or read the paper on ResearchGate or ACM Digital Library.
Abstract
Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address these limitations, we designed a control mechanism and a visualisation of the control’s impact through an iterative design process with adolescents and teachers. Then, we investigated how these functionalities affect adolescents’ trust in an e-learning platform that recommends maths exercises. A randomised controlled experiment with 76 middle school and high school adolescents showed that visualising the impact of exercised control significantly increases trust. Furthermore, having control over their mastery level seemed to inspire adolescents to reasonably challenge themselves and reflect upon the underlying recommendation algorithm. Finally, a significant increase in perceived transparency suggested that visualising steering actions can indirectly explain why recommendations are suitable, which opens interesting research tracks for the broader field of explainable AI.
