The rapid growth of computer science (CS) and artificial intelligence (AI) in K–12 schools has outpaced the teacher preparation pipeline, leaving states and institutions grappling with how to recruit, certify, and retain qualified CS teachers. While many states have adopted CS standards, graduation requirements, or AI literacy initiatives, most lack sufficient numbers of qualified educators to deliver on these commitments. States and institutions now face critical challenges:

• Recruitment: How to attract a diverse pool of preservice and in-service teachers into CS and AI education pathways, especially in rural and underserved areas.
• Certification: How to design state certification and endorsement policies that are rigorous yet accessible, and that align with the evolving knowledge base required to teach both CS and AI.
• Preparation: How higher education programs can integrate CS and AI into teacher education curricula, ensuring that future teachers are equipped with both technical knowledge and pedagogical strategies.
• Retention: How to support teachers in navigating the additional workload, shifting expectations, and in some cases, resistance to adopting new content or technologies like AI.

This affiliated event will convene higher education, researchers, and state leaders to tackle these pressing questions and share and ideate models for building sustainable pipelines. The session will highlight both the systemic levers and on-the-ground practices needed to expand the CS/AI teaching workforce. Participants will engage in dialogue that bridges research and policy, while also identifying actionable steps for future work. Key discussion areas will include:

• Teacher Capacity Models: Methods and tools to calculate the supply–demand gap for CS teachers, including forecasting future needs as AI literacy becomes a universal expectation.
• Certification Pathways: State policies and program models that expand certification opportunities, including preservice and alternative routes, with attention to their implications for higher education institutions.
• Preparing Teachers for AI: What content knowledge and pedagogical skills are necessary for teachers to teach about AI as well as to teach with AI tools.
• Teacher Pushback and Professional Identity: Understanding resistance to certification changes, workload demands, or integration of AI, and identifying strategies to support teacher agency and professionalization.
• Promising Models: Case studies of effective preservice preparation programs and state-university partnerships that have scaled teacher certification pathways.

Participants will leave with frameworks and practical tools for building sustainable teacher pipelines, along with clearly defined research opportunities for studying certification, capacity, and teacher preparation in the context of AI.