This work presents a study of Virtual Tutor: a system designed to enhance essay-writing skills among undergraduate students. The system integrates the eBICA cognitive architecture with advanced large language models (LLMs), specifically GPT-4o, enabling the cognitive framework to operate within semantic spaces and moral schemas while adhering to the principles of Self-Regulated Learning (SRL). By combining these technologies, the platform fosters adaptive, context-dependent learning strategies that promote learner autonomy and critical thinking. The system architecture comprises four core components: visualization module, interaction module, reasoning module, and answer generation module. A set of experiments in educational settings were conducted with the implemented system to evaluate its efficacy in fostering students’ autonomous learning and improving their writing proficiency. 48 college students participated in the essay writing experiment. Results showed a positive effect of Virtual Tutor on the quality of essays evaluated on 16 criteria (ANOVA p < 0.011). The effect persisted in subsequent essay writing sessions when Virtual Tutor was not used. Therefore, experimental results demonstrate a clear, positive impact of Virtual Tutor on the writing quality with minimal resource overhead. The solution scales readily via internet and browser-based access, requiring no specialized hardware. Directions for improvement to further increase the Virtual Tutor’s effectiveness include enhancing the agent’s visualization quality and enabling multimodal interfaces. Virtual Tutor is expected to be equally efficient in other learning domains (STEM, foreign language, creative writing), indicating a considerable potential for the combination of three approaches—SRL, cognitive modeling and advanced LLMs—in educational technology.