Artificial intelligence (AI) systems built on machine learning have inherent limitations due to training and data quality, and may appear surprisingly unintelligent when their behavior does not match what people expect from an intelligent system. Despite these limitations, imperfect AI can still be very helpful and has been widely adopted, raising concerns about being left behind as these technologies advance. This project seeks to support non-AI experts in living and working effectively with imperfect artificial intelligence tools by studying the workarounds people generate when encountering imperfect AI, what affects their ability to adapt these AI tools to their needs, and what knowledge and skills should be prioritized when equipping non-AI experts to work with such tools. The insights gained can inform the design of human-AI interfaces and the development of training programs, enabling the general public to better utilize AI tools. The project will also identify essential competencies for AI literacy, helping equip the future workforce with the ability and confidence to explore, question, and critically assess new AI tools they encounter. To support non-AI experts in living and working effectively with imperfect AI, the project includes four research tasks that combine mixed-methods studies and controlled experiments. The first task is to develop and validate a general framework of AI-related workarounds by collecting a diverse sample of non-expert users adapting to imperfect AI tools. The second task focuses on studying factors that influence workaround generation. It involves exploring how users’ mental model, sensemaking, and trust calibration relate to workaround generation. It also investigates the influence of task effort and time constraints. The third task focuses on designing and testing interventions to support effective workarounds. It compares three types of training, namely machine learning knowledge, failure cases, and workaround strategies. It