20.500.12556/RUL-175498 Custom generative artificial intelligence tutors in action an experimental evaluation of prompt strategies in STEM education The integration of generative artificial intelligence, particularly large language models, into education presents opportunities for both personalised learning and pedagogical challenges. This study focuses on electrical engineering laboratory education. We developed a configurable prototype of a generative artificial intelligence powered tutoring tool, implemented it in an undergraduate electrical engineering laboratory course, and analysed 208 student–tutoring tool interactions using a mixed-methods approach that combined research team evaluation with learner feedback. The findings show that student prompts were predominantly procedural or factual, with limited conceptual or metacognitive engagement. Structured prompt styles produced clearer and more coherent responses and were rated the highest by students, while approaches aimed at fostering reasoning and reflection were valued mainly by the research team for their pedagogical depth. This contrast highlights a consistent preference–pedagogy gap, indicating the need to embed stronger instructional guidance into artificial intelligence tutoring. To bridge this gap, a promising direction is the development of pedagogically enriched AI tutors that integrate features such as adaptive prompting, hybrid strategy blending, and retrieval-augmented feedback to balance clarity, engagement, and depth. The results provide practical and conceptual value relevant to educators, developers, and researchers interested in artificial intelligence tutors that are both engaging and pedagogically sound. For educators, the study clarifies how students interact with tutors, helping align artificial intelligence use with instructional goals. For developers, it highlights the importance of designing systems that combine usability with educational value. For researchers, the findings identify directions for further study on how design choices in artificial intelligence tutoring affect learning processes and pedagogical alignment across STEM contexts. On a broader level, the study contributes to a more transparent, equitable, and sustainable integration of generative AI in education. generative artificial intelligence generative AI tutors prompting strategies STEM education pedagogical design tutorji strategije spodbujanja izobraževanje STEM oblikovanje generativna umetna inteligenca vzgoja in izobraževanje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-29 13:04:47 2025-10-29 13:04:48 2025-12-11 04:03:16 0000-00-00 00:00:00 2025 0 0 20 str. iss. 21, art. 9508 Vol. 17 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 378:004 2071-1050 10.3390/su17219508 255233283 RAZ_Gabrovsek_Rok_2025.pdf RAZ_Gabrovsek_Rok_2025.pdf 1 8D9E3E021A04951B8A0B31FD8B972811 8b78d4c0bd95202ef21969a0b2e45b58385a512c0dbab8340276069a5a8a5035 cb6bcf7d-b4c1-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=221114 https://www.mdpi.com/2071-1050/17/21/9508 1 2ae0ae19-b4bf-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=221113 Pedagoška fakulteta 0 0 0