Designing Adaptive Affective Chatbots for Online Learning: Trade-offs Between User Control, Automation, and Transparency

Abstract

Online students often face emotional challenges such as frustration, isolation, and fluctuating motivation, which can hinder sustained engagement in online learning. While affective chatbots have shown promise in healthcare and wellness, most learning support chatbots focus primarily on cognitive assistance with limited attention to learners' emotional and motivational experiences. In this work, we investigate how adaptive affective chatbot support can be designed for online learning through an iterative design process. Through a mixed-methods needfinding study (n=38), we identified substantial variation in how learners respond to affective support, with no single strategy universally preferred. This led us to develop three adaptive design alternatives: user-controlled, AI-driven, and hybrid co-controlled adaptation, and examine key trade-offs between automation, transparency, and user control. A final evaluation (n=25) showed that 88% of participants favored the adaptive hybrid design over a non-adaptive alternative, highlighting the value of balancing transparency, user control, and interaction simplicity. We discuss design implications for adaptive affective chatbot systems in emotionally challenging online learning contexts.