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TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach. 

This paper presents findings from a study of middle school science teachers’ professional learning activities designed to support the development of their debugging pedagogies. In two iterations of a professional learning activity, teachers worked to find bugs planted by facilitators in physical computing systems they were learning to integrate into their middle school science classrooms. We examine how teachers navigated the tension between developing their own troubleshooting skills versus supporting students’ skills in resolving inconsistencies between what they expect of the DaSH and what it actually does. We conclude with implications for the design of PL activities for supporting teachers’ debugging pedagogies.