Algorithms Without Foundations—Quantifying the Technocentric Bias in Construction AI Research Against Practitioner-Identified Adoption Barriers

The construction industry accounts for approximately 13% of global GDP but suffers from chronic productivity stagnation. Although artificial intelligence (AI) offers transformative potential, its adoption is constrained by three key barriers: data integrity issues (H1), socio-technical challenges (H2), and system integration problems (H3). This study investigates whether academic research attention aligns with these practitioner-identified barriers through a bibliometric analysis of 4668 publications from OpenAlex (1990–2025), applying a five-pillar analytical framework synthesized into composite scores (0–100 scale) via min-max normalization, weighted summation, and bootstrap validation. H3 achieved a nominal 15.9% prevalence rate (adjusted to ~13.0% after correcting for an 18.2% false positive rate in keyword classification), robust growth (R2 = 0.654), significant overrepresentation in top-cited works (risk ratio = 1.31, p = 0.003), and received a composite score of 62/100 (confirmed). H1 (2.7%, score: 17/100) and H2 (4.6%, score: 13/100) were both rejected. The rank ordering by prevalence (H3 > H2 > H1) remains robust under all adjustment scenarios. These findings contrast notably with the RICS Global Construction Monitor (2025, n = 2200+), where practitioners most frequently reported socio-technical barriers (46%), followed by system integration (37%) and data quality (30%), yielding practitioner-to-publication ratios of 4.7:1, 5.2:1, and 1.1:1, respectively. This apparent research–practice paradox appears primarily volume-driven rather than clearly quality-driven: H1/H2 publications receive citation attention broadly comparable to the baseline, though this comparison is limited by control group heterogeneity. We call for rebalanced research agendas addressing data governance frameworks, competency development, and organizational change management.