“AI models in edtech will be approximations of reality and, thus, constituents can always ask these questions: How precise are the AI models? Do they accurately capture what is most important? How well do the recommendations made by an AI model fit educational goals? What are the broader implications of using AI models at scale in educational processes? Building on what was heard from constituents, the sections of this report develop the theme of evaluating the quality of AI systems and tools using multiple dimensions as follows: About AI: AI systems and tools must respect data privacy and security. Humans must be in the loop. Learning: AI systems and tools must align to our collective vision for high-quality learning, including equity. Teaching: AI systems and tools must be inspectable, explainable, and provide human alternatives to AI-based suggestions; educators will need support to exercise professional judgment and override AI models, when necessary.” (Cardona et al., 2023, p. 9)“ Formative Assessment: AI systems and tools must minimize bias, promote fairness, and avoid additional testing time and burden for students and teachers. Research and Development: AI systems and tools must account for the context of teaching and learning and must work well in educational practice, given variability in students, teachers, and settings. Recommendations: Use of AI systems and tools must be safe and effective for students. They must include algorithmic discrimination protections, protect data privacy, provide notice and explanation, and provide a recourse to humans when problems arise. The people most affected by the use of AI in education must be part of the development of the AI model, system, or tool, even if this slows the pace of adoption.” (Cardona et al., 2023, p. 10)