The construction industry is increasingly embracing Large Language Models (LLMs), to enhance efficiency and decision-making. However, Small and Medium-sized Enterprises (SMEs) face significant barriers, including limited digital literacy, resource constraints, and uncertainty regarding AI’s practical application. This study examines an AI upskilling course tailored specifically for SMEs in the building industry, designed around cooperative learning principles to foster knowledge-sharing and enhance AI competencies. Through pre- and post-course evaluations involving 45 participants across diverse professional roles, significant improvements were observed in AI knowledge, prompting skills, and practical application within workflows. Cooperative learning methodologies enabled effective peer-to-peer interaction and collaborative problem-solving, significantly increasing participants’ confidence and practical AI capabilities. Nevertheless, notable challenges included varying levels of digital literacy and resistance to adopting AI technologies, highlighting the need for targeted support and ongoing training initiatives. Future courses should include preliminary digital literacy assessments, more extensive hands-on practice, and further tailored content addressing specific industry applications of LLMs. Continuous, peer-driven learning models can sustain AI adoption, ensuring long-term integration and impact. This study contributes insights into AI upskilling strategies, emphasizing cooperative learning as a pivotal approach for helping construction SMEs adopt AI innovations.