Osuizugbo, I. C.; Awuzie, B. O. and Olorunlogbon, O. O. (2026) Assessing the proficiency and application of artificial intelligence skills among construction professionals in a developing country: Evidence from Nigeria. Journal of Engineering, Design and Technology, ISSN 1726-0531
Abstract
Purpose – The rapid evolution of artificial intelligence (AI) is redefining professional practices across industries, offering unprecedented opportunities to enhance decision-making, innovation and productivity. This study aims to examine the current state of proficiency and application of AI skills among construction professionals in a developing country by using Nigeria as a case study. Design/methodology/approach – This research adopts a quantitative approach, using a structured questionnaire survey administered to 209 construction professionals in Nigeria, with a 60% response rate. The collected data were analysed using descriptive statistics and inferential techniques, including frequency and percentage distribution, mean, the Shapiro–Wilk test and Kruskal–Wallis H-test. Findings – The findings reveal uneven digital competency levels among respondents, with relatively stronger skills in data handling and Building Information Modelling, but significantly weaker proficiency in advanced AI-related competencies such as automation and digital project management. Overall proficiency remains low, constrained by limited formal education, curriculum–industry misalignment and a persistent knowledge–practice gap. While awareness of AI exists, its application remains largely operational rather than strategic, indicating slow but emerging adoption with opportunities to leverage global best practices through improved policy frameworks, leadership and cultural change. Originality/value – This research provides one of the first detailed evaluations of AI skill proficiency and application in Nigeria. The research contributes a novel regional perspective by exploring AI skills in the construction sector within Lagos, Nigeria. By uncovering the gap between proficiency and the practical application of AI skills, this study not only enriches academic understanding but also calls on researchers, policymakers and industry leaders to address this shortfall through targeted research, responsive policy frameworks and innovative construction practices.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | application; artificial intelligence; construction professionals; developing country; skills |
| Index terms: | cultural change, construction professional, evidence, misalignment, project management, professional practice, evolution, case study, construction sector, decision-making, developing country, best practice, Nigeria, artificial intelligence, building information modelling, Lagos, survey, questionnaire, methodology, automation, statistics, productivity |
| Subjects: | data collection methods, project management theory and practice, engineering problems, business, environmental science, mathematical modelling, decision analysis, information systems, professional development, automation and robotics, artificial intelligence, management, development economics, research methods, evaluation and assessment methods, industry analysis, Geography, sociology |
| Topics: | Geographical Context, Digital Applications, Business Strategy, Project Management, Organizational Design, Research Practice, Information Management, Risk Management, Engineering Principles, International Construction, Sustainability |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here