Critical Success Factors in Integrating Artificial Intelligence in Engineering Education

DOI

10.2991/978-94-6463-970-4_16How to use a DOI?

Keywords

Artificial intelligence; critical success factors; curriculum alignment; engineering education; faculty competency; institutional support

Abstract

This study explores the critical success factors influencing the effective integration of Artificial Intelligence (AI) into engineering education, focusing on accredited UK tertiary institutions. It examines perspectives from both faculty and students to identify pedagogical and institutional determinants of successful AI adoption. A quantitative, descriptive cross-sectional survey design was employed with 300 participants (200 students and 100 faculty). Data were collected using a structured 5-point Likert-scale questionnaire and analyzed in SPSS v27.0 through descriptive statistics, reliability testing (Cronbach’s α = 0.89), Pearson correlation, and multiple regression analysis. Results revealed that faculty competency (β = 0.38, p < 0.01), institutional support (β = 0.29, p = 0.04), and curriculum alignment (β = 0.25, p = 0.01) were the strongest predictors of successful AI integration, collectively explaining 58% of the variance (R2 = 0.58) in perceived integration success. Infrastructure availability and student readiness had minor yet positive effects. Faculty competency influenced both faculty and student perceptions most strongly, underscoring the centrality of educator preparedness. The cross-sectional design limits causal inference, suggesting future longitudinal research. The findings highlight the need for continuous faculty development, institutional investment, and curriculum realignment to embed AI in engineering pedagogy effectively. Broader implications include strengthening workforce readiness for AI-driven technological transformation. This study contributes a dual-stakeholder empirical model integrating UTAUT2 and TPACK frameworks, advancing understanding of AI adoption in engineering education within accredited institutional contexts.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - F. Osumanu
AU  - S. N. O. Wellington
AU  - E. B. Osei
PY  - 2025
DA  - 2025/12/31
TI  - Critical Success Factors in Integrating Artificial Intelligence in Engineering Education
BT  - Proceedings of the International Conference on Engineering, Science, and Urban Sustainability (ICESUS 2025)
PB  - Atlantis Press
SP  - 254
EP  - 274
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-970-4_16
DO  - 10.2991/978-94-6463-970-4_16
ID  - Osumanu2025
ER  -