Prompt‐based emotional scaffolding in generative AI : Effects on self‐regulated learning and socio‐emotional outcomes

While generative AI (GenAI) tools are emerging as educational resources, general‐purpose applications like ChatGPT are not explicitly designed to support the metacognitive and socio‐emotional dimensions of learning, often failing to address students' emotional states or promote independent thinking. In response, this study introduces a CVT‐SRL GenAI Support Framework integrating control‐value theory (CVT) and self‐regulated learning (SRL) and tests a learner‐centred GenAI chatbot design employing prompt‐based SRL scaffolding and emotional support. In a preregistered experiment in an authentic educational setting, 144 high school students were randomly assigned to one of three conditions: (1) ChatGPT, (2) SRLbot (GenAI with SRL scaffolding), (3) emotional SRLbot (GenAI with SRL and emotional support). Outcomes included conceptual knowledge and transfer, trace‐based and self‐reported engagement in SRL phases (forethought, performance, self‐reflection), enjoyment, boredom, hopelessness, self‐efficacy and trust. Data were collected at pretest, posttest and 4‐week follow‐up. Across conditions, students showed improvements in conceptual knowledge and transfer from pretest to posttest, with no significant condition differences in post‐to‐follow‐up change. Both SRLbot and Emotional SRLbot were associated with higher self‐reported enjoyment and lower boredom than ChatGPT and elicited trace‐based SRL behaviours. Moreover, Emotional SRLbot prompted greater engagement in performance‐phase and self‐reflection processes than the other conditions. These findings provide preliminary evidence that GenAI agents integrating SRL and emotional scaffolding can support more positive emotional states and task‐level SRL behaviours than general‐purpose GenAI, highlighting control‐value appraisals as a promising target for emotionally supportive SRL design. Future research should explore longitudinal effects on SRL skill development and independent regulation. Practitioner notes What is already known about this topic Generative AI (GenAI) tools such as ChatGPT have shown promise in enhancing learning outcomes, but they also present challenges in supporting socio‐emotional needs, fostering metacognition and promoting higher‐order thinking. Applying self‐regulated learning (SRL) frameworks in chatbot‐assisted learning has been found to foster learner autonomy, long‐term engagement and improved learning outcomes. Achievement emotions experienced during learning play a critical role in supporting self‐regulated learning, motivation and academic achievement. Interventions that target learners' perceptions of control and value through reappraisal can effectively foster positive emotions. What this paper adds An investigation of the impact of three GenAI conversational agents (standard ChatGPT, an SRL agent and an emotionally supportive SRL agent) on cognitive and socio‐emotional outcomes as well as task‐level self‐regulatory behaviours among high school students in an authentic classroom setting. Preliminary evidence that integrating prompts which target self‐regulated learning phases and learners' emotional states, as well as address perceptions of control and value can increase students' enjoyment of concrete learning situations and reduce boredom more effectively than standard ChatGPT. Insights into how theoretically grounded GenAI agents that incorporate SRL and emotional scaffolding may support students' emotional engagement and task‐level self‐regulatory behaviours during a short classroom learning activity. Implications for practice and/or policy Prompt engineering can be used to simulate emotional support and personalize guidance based on SRL and emotion regulation principles, providing design considerations for future learner‐centred GenAI systems. Developers and educators aiming to design pedagogically meaningful GenAI tools should focus on scaffolding SRL both cognitively and emotionally, to support the affective and self‐regulatory dimensions of learning. We make the technical specifications and system prompts publicly available.