A Teacher-Driven Framework for Reliable and Personalised AITutors
Stefano Valtolina, Ricardo Anibal Matamoros, Francesco Epifania · 2025
This paper presents a software framework that enables teachers to design reliable, personalised conversational agents tailored to their pedagogical goals and student learning preferences. The system combines a Retrieval-Augmented Generation (RAG) architecture with a visual configuration environment, allowing educators to upload, validate, and organise domain-specific teaching materials into a teacher-curated content corpus. Educators can configure adaptive tutoring strategies based on the VARK model (Visual, Auditory, Reading/Writing, Kinesthetic), allowing the conversational agents to address diverse learning preferences and educational contexts. Unlike fully autonomous or black-box educational AI systems, this approach foregrounds teacher agency and pedagogical alignment, enabling intuitive control over content and interaction style. A preliminary evaluation with university educators assessed usability (SUS), perceived utility (UTAUT), cognitive load (NASA-TLX), and creative-technical capacity (CTS), revealing promising results and informing future design directions. The system supports the development of human-centred AI tutors that are transparent, configurable, and grounded in teacher expertise.