Purpose This study investigates how structured training impacts LLM adoption in architectural structural design. Despite LLMs’ potential, adoption barriers persist due to unclear implementation guidelines and insufficient training. Using UTAUT, this research examines how training influences performance expectancy, effort expectancy, social influence and facilitating conditions, while considering individual factors like gender and experience. Design/methodology/approach This study employs a longitudinal research design to examine how professional training influences the adoption of Large Language Models (LLMs) in architectural structural design. A total of 144 professionals participated in structured training, with pre- and post-training assessments measuring changes in technology acceptance using the unified theory of acceptance and use of technology (UTAUT) framework. Structural equation modeling (SEM) analyzed UTAUT constructs and behavioral intention, while multi-group analysis explored variations by gender and experience. Findings Results show significant improvements in technology acceptance post-training, especially in performance expectancy (ß: 0.509 → 0.810, p < 0.001). Training reduced gender disparities, with female professionals exhibiting greater confidence and behavioral intention. Junior professionals showed the highest gains. While training improved ease of use and social influence, its impact on facilitating conditions was minimal. Social implications This study demonstrates how structured training can reduce gender disparities in technology adoption within male-dominated professional fields. The finding that comprehensive LLM training significantly enhanced confidence and behavioral intention among female professionals provides actionable guidance for organizations seeking to promote diversity and inclusion in AI adoption. By accommodating diverse learning preferences and addressing experience-based differences, professional training programs can democratize access to emerging technologies and create more equitable opportunities for career advancement in technical disciplines. Originality/value This study extends UTAUT by showing how professional training dynamically influences technology adoption. Unlike prior research, it empirically demonstrates how structured training mitigates gender disparities and enhances confidence in AI use. The longitudinal design provides insights into evolving acceptance factors, offering guidance for organizations integrating LLMs in architecture and engineering.