Artificial intelligence (AI) is becoming more prevalent in the construction bidding process, assisting human decision-makers. However, little is known about the tactical shifts that may arise from AI participation in the market, particularly regarding bid pricing. This study aims to predict the strategic decisions AI bidders may make and their impact on bid pricing when they become dominant players in the construction bidding market. An experiment was conducted in which AI bidders competed repeatedly in an environment that simulates the decision-making process in the construction bidding phase. AI bidders were built with Q-learning algorithms, which is a popular reinforcement learning algorithm in repetitive games. Bid notice data from public construction projects in the Washington Department of Transportation (WSDOT) was given to the AI bidders, who set bid prices based on prior bidding experiences. As a result of repeated competition and learning, it was found that the AI bidders gradually learn to cooperate rather than to compete with each other, sustaining higher bid prices compared to human bidders. The study suggests the possibility of collusive behavior by AI bidders in a scenario where they are the dominant participants in the construction bidding process. These findings highlight the need to monitor and regulate the AI participants to prevent anti-competitive behavior in the construction bidding market.