Current quality assurance frameworks in highway construction are not equipped to detect or mitigate the rapidly emerging risks posed by artificial intelligence (AI)–driven data manipulation. As AI tools evolve faster than industry protocols, the risks of undetected fraud and compromised infrastructure quality increase, necessitating updates to detection methods and a critical review of current quality assurance (QA) processes.

The objectives of this research are to identify unexplored and emerging risks that AI may pose to highway construction QA processes and to develop a comprehensive framework for detection and mitigation. The framework may include:

• Exploring the potential misuse of AI tools in falsifying and manipulating construction data;
• Investigating how AI-generated scripts can alter key project metrics and compliance with specifications;
• Developing countermeasures to prevent opportunities for AI data manipulation used for construction acceptance;
• Developing advanced detection mechanisms and QA protocols that can identify AI-driven data manipulation in real time; and
• Providing recommendations for leveraging AI to enhance QA processes while augmenting existing best practices.