A conceptual SRLbot model for higher education based on Zimmerman's Self-Regulated Learning model

Abstract This study presents an SRLbot model to transform Zimmerman’s Self-Regulated Learning (SRL) model, in which learners set goals, monitor progress, and conduct self-evaluation, into an AI chatbot-assisted interactive model. Building from the SRL foundation, the SRLbot animates three core functions: planning, monitoring and reflecting, thereby prompting learners to set SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) goals, to create deadline alerts and to conduct success-failure analysis. The SRLbot encourages the learner’s ability to reason and control the learning process, it also personalises the experience through immediate responses and real-time learning data tracking that increases the student’s autonomy. Initial results within a university-level educational context indicate that students using the SRLbot tend to maintain their motivation, actively engage in the learning process and achieve higher proficiency compared to those using self-adjustment learning methods only. AI integration into the learning experience offers the potential to enhance higher-order thinking skills by engaging students with a “virtual learning partner” who is always willing to reply to questions. Keywords: self-regulated learning; AI chatbot; SRLbot; higher education; personalised support.