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.

翻译：研究者广泛认识到，推荐系统的终端用户通过控制机制能够更好地定制推荐内容。然而，目前少有在线学习环境融入此类机制，例如用于引导推荐练习。此外，针对青少年的相关研究也较为匮乏。为弥补这些不足，我们通过与青少年及教师进行的迭代设计过程，设计了一种控制机制及其影响的可视化方案，并探究了这些功能如何影响青少年对推荐数学练习的在线学习平台的信任度。一项针对76名初高中青少年的随机对照实验表明，可视化操作控制的影响能显著提升信任度。此外，对自身掌握水平的控制似乎促使青少年合理挑战自我，并反思底层的推荐算法。最后，感知透明度的显著提升表明，可视化引导行为可间接解释推荐内容的适宜性，这为可解释人工智能领域的更广泛研究开辟了有趣的探索方向。