Integrating AI applications into university STEM study programs using Python

Dafina Xhako (), Suela Hoxhaj (), Niko Hyka () and Elda Spahiu ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 1, 2374-2384

Abstract: This study aims to evaluate the current integration of Artificial Intelligence (AI) in STEM (Science, Technology, Engineering, and Mathematics) curricula at European universities, focusing on its impact on student outcomes such as problem-solving, analytical skills, and job readiness. A mixed-methods approach was employed, combining a content analysis of 25 STEM curricula with quantitative data from faculty surveys (n = 120) and qualitative insights from student focus groups (n = 50). The study also leveraged recent developments in STEM pedagogy, AI education frameworks, and institutional reporting. The results reveal that although 92% of faculty recognize the importance of AI in STEM education, only 40% feel prepared to teach AI-related content, and just 30% have access to adequate resources. Additionally, only 40% of the analyzed STEM curricula include dedicated AI coursework. Students highlighted the critical role of AI for their future careers but expressed concerns over the limited availability of practical, real-world learning opportunities. The study concludes that despite a broad acknowledgment of AI's significance in STEM, there exists a pronounced gap in faculty preparedness, resource availability, and curriculum integration. These shortcomings may impede the development of the essential skills needed to meet contemporary industry demands. To address these issues, the paper recommends enhancing faculty training programs, making targeted investments in AI infrastructure and technology, and undertaking a comprehensive overhaul of STEM curricula to embed AI-focused courses. Such initiatives are vital to overcoming institutional constraints and unlocking the full transformative potential of AI in STEM education.

Keywords: AI Course; AI education; AI projects; Course evaluation; Data analytics; Educational outcomes; Machine learning; Python programming; Statistical analysis; Student engagement; Student performance. (search for similar items in EconPapers)
Date: 2025
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