Designing Visual Explanations and Learner Controls to Engage Adolescents in AI-Supported Exercise Selection

TL;DR

This paper presents a novel control mechanism with visual explanations for AI-supported exercise selection in e-learning, aiming to enhance adolescent engagement and transparency.

Contribution

It introduces an interactive control system with visual explanations allowing adolescents to influence AI-generated exercises, addressing a gap in engagement during AI decision-making.

Findings

'Why' explanations are more useful for teachers than learners.

'What-if' explanations increase motivation among adolescents.

Learners benefit from visual tools that show the impact of their control.

Abstract

E-learning platforms that personalise content selection with AI are often criticised for lacking transparency and controllability. Researchers have therefore proposed solutions such as open learner models and letting learners select from ranked recommendations, which engage learners before or after the AI-supported selection process. However, little research has explored how learners - especially adolescents - could engage during such AI-supported decision-making. To address this open challenge, we iteratively designed and implemented a control mechanism that enables learners to steer the difficulty of AI-compiled exercise series before practice, while interactively analysing their control's impact in a 'what-if' visualisation. We evaluated our prototypes through four qualitative studies involving adolescents, teachers, EdTech professionals, and pedagogical experts, focusing on different types of visual explanations for recommendations. Our findings suggest that 'why' explanations do not always meet the explainability needs of young learners but can benefit teachers. Additionally, 'what-if' explanations were well-received for their potential to boost motivation. Overall, our work illustrates how combining learner control and visual explanations can be operationalised on e-learning platforms for adolescents. Future research can build upon our designs for 'why' and 'what-if' explanations and verify our preliminary findings.