This research employs a qualitative case study methodology to examine the experiences of eight teacher candidates enrolled in a Bachelor of Education program as they used Teachable Machine, an online tool for developing simple machine learning models. Informed by teacher candidate identity theory (Birch et al., 2025; Birch & Pike, in press), the study analyzed participants’ engagement with an assignment designed to foster familiarity with machine learning models. Findings reveal that hands-on experience with Teachable Machine enabled teacher candidates to recognize biases and limitations inherent in AI technologies, as well as devise strategies for integrating AI tools in classroom settings which may promote meaningful learning and mitigate bias. Participants identified strategies for integrating AI into their future classrooms, emphasizing the importance of fostering critical AI literacy and promoting equitable, inclusive teaching practices. This research provides data to inform teacher education programs as they seek to prepare their teacher candidates for the AI-infused classroom.