Exploring engineers’ adoption of artificial intelligence: Insights into knowledge absorptive capacity, transparency, and risk awareness using BIBLO AI 5.0

This study examines how knowledge absorptive capacity, AI system transparency, and perceived risk influence the adoption of artificial intelligence in engineering contexts. Drawing on bibliometric data from 2015 to 2026 and employing thematic, co-word, and knowledge network analyses, the findings reveal that engineers are more inclined to adopt AI systems that provide clear, interpretable, and task-relevant information. Knowledge absorptive capacity emerges as a critical enabler in converting AI-generated insights into practical value, whereas perceived risk continues to represent a substantial impediment to adoption. These results refine the understanding of the determinants of engineers’ technology acceptance and highlight pathways for developing AI systems that are more transparent, responsible, and aligned with the specific demands of the engineering domain.