From Chatbot to Adaptive Syllabus-aware AI-mechanism: LLM Personalized Teaching Assistant in Higher Education

Abstract

The integration of Artificial Intelligence (AI) into higher education offers scalable support for students but raises concerns regarding over-reliance, reduced effort, and diminished deep learning. This study introduces Michael, a syllabus-aware AI teaching assistant designed to scaffold reasoning through structured, hint-first dialogue aligned with course progression, rather than providing direct solutions. The system was deployed in an undergraduate Structured Query Language (SQL) course across three consecutive semesters and evaluated using a mixed-methods design combining interaction logs, pre–post questionnaires (N = 170), and classroom observations. Results indicate high perceived ease of use (M = 4.43) and a moderate but statistically significant increase in trust following exposure (from M = 3.29 to M = 3.58), while AI self-efficacy showed only minor changes. Usage patterns revealed a bifurcated structure, with students engaging in both short troubleshooting interactions and extended tutoring dialogues. Qualitative findings highlight adoption waves, tensions between efficiency and depth, and the sensitivity of trust to system reliability. These findings suggest that curriculum-aligned constraints and hint-first scaffolding can support instructional integration without displacing pedagogical goals. Rather than demonstrating causal learning gains, this study contributes design principles and in-situ evidence for deploying domain-specific AI assistants in technical higher-education contexts.