Motivational GenAI chatbot for promoting SRL in problem solving: Data-driven design principles

Abstract

Self-regulated learning (SRL) is crucial in today‟s educational landscape, championing students' autonomy over their learning journey. Yet, SRL, especially in problem-solving, poses significant difficulties for students. This study innovatively bridges this gap with a set of design principles for a generative AI chatbot specifically shaped to promote students' SRL skills during problem-solving tasks. Leveraging insights from feedback collected from both educators and students involved in a blended SRL program, this research shines a light on the range of difficulties involves in SRL within problem solving context. It pinpoints three main difficulties: Inconsistencies in assessment and perception that create a gap between student self-evaluations and educator assessments; Cognitive strategies difficulties that hinder skill application; and beyond cognitive difficulties such as motivation and task self-management obstacles toward independent learning. Responding to these difficulties, the design principles of the proposed AI chatbot are focused on providing structured guidance, which includes explicit instruction in cognitive and motivational skills, offering personalized feedback based on the specific difficulties, and facilitating the assessment of these skills during task performance. This approach not only advances the theoretical framework of SRL but also translates these concepts into tangible support mechanisms, equipping educators with an innovative tool to enhance SRL. The chatbot stands as a testament to the power of generative AI in transforming educational practices, paving the way for learners to become more self-reliant and adept in navigating the complexities of the digital learning era.