The Level of AI Application in University STEM Study Programs: A Comprehensive Review

Dafina Xhako; Niko Hyka; Altin Gjevori; Valbona Muda; Clirim Duro; Majlinda Demirneli; Elda Spahiu; Suela Hoxhaj

doi:10.15157/ijitis.2025.8.4.1244-1283

Authors

Dafina Xhako Department of Physics Engineering, Polytechnic University of Tirana, Tirana, Albania https://orcid.org/0000-0002-0937-2471
Niko Hyka Institute for Quality in Education, Tirana, Albania https://orcid.org/0000-0002-4645-3844
Altin Gjevori Department of Physics Engineering, Polytechnic University of Tirana, Tirana, Albania https://orcid.org/0000-0002-4302-2483
Valbona Muda Department of Physics Engineering, Polytechnic University of Tirana, Tirana, Albania https://orcid.org/0000-0001-7535-4856
Clirim Duro Directory of Curricula, University of Tirana, Tirana, Albania https://orcid.org/0009-0009-0316-4653
Majlinda Demirneli Public Agency of Accreditation for Higher Education, Tirana, Albania https://orcid.org/0009-0004-2170-3853
Elda Spahiu Institute of Applied Nuclear Physics, Tirana, Albania https://orcid.org/0009-0004-5205-8893
Suela Hoxhaj Department of Physics Engineering, Polytechnic University of Tirana, Tirana, Albania https://orcid.org/0009-0006-4686-3663

DOI:

https://doi.org/10.15157/ijitis.2025.8.4.1244-1283

Keywords:

Artificial Intelligence, STEM Education, Higher Education, Curriculum Integration, Engineering Pedagogy, Educational Technology

Abstract

Artificial intelligence (AI) is reshaping engineering, science, and technology education, yet little is known about how AI is embedded in Science Technology Engineering Mathematics (STEM) curricula in late-adopter higher education systems. This paper examines the extent and depth of AI integration in accredited STEM study programs in Albania. Using a two-step approach, we first describe the national landscape of 390 STEM programs and then conduct a detailed curriculum analysis of 35 programs from eight public and private higher education institutions. For each program, we code the presence of AI-related modules, the volume of AI-related to European Credit Transfer and Accumulation System (ECTS), the number of AI courses, and the integration level (introductory, applied, or advanced). We then estimate multilevel regression models to explore how institutional characteristics (public vs. private status, international partnerships, language of instruction) and disciplinary profiles shape the probability and intensity of AI integration. The findings show that around 60% of the analysed programs include AI-related content, with higher intensity and deeper integration in computer science and engineering compared to other STEM fields. Private institutions and English-taught programs display a systematically higher likelihood of AI integration than public institutions. At the same time, explicit AI ethics components remain limited, and AI-related learning outcomes are rarely embedded across non-Information and Communication Technology (ICT) STEM disciplines. The paper discusses these results in relation to European policy frameworks and outlines concrete implications for curriculum reform, quality assurance, and capacity building in Western Balkan higher education.