“AI technologies are grounded in models, and these models are inevitably incomplete in some way. It is up to humans to name educational goals and measure the degree to which models fit and are useful—or don’t fit and might be harmful. Such an assessment of how well certain tools serve educational priorities may seem obvious, but the romance of technology can lead to a “let’s see what the tech can do'' attitude, which can weaken the focus on goals and cause us to adopt models that fit our priorities poorly.” (Cardona et al., 2023, p. 54)“align priorities, educational strategies, and technology adoption decisions to place the educational needs of students ahead of the excitement about emerging AI capabilities.” (Cardona et al., 2023, p. 54)“Every conversation about AI (or any emerging technology) should start with the educational needs and priorities of students front and center and conclude with a discussion about the evaluation of effectiveness re-centered on those needs and priorities. Equity, of course, is one of those priorities that requires constant attention, especially given the worrisome consequences of potentially biased AI models. We especially call upon leaders to avoid romancing the magic of AI or only focusing on promising applications or outcomes, but instead to interrogate with a critical eye how AI-enabled systems and tools function in the educational environment.” (Cardona et al., 2023, p. 54)“we center teaching and learning in all considerations about the suitability of an AI model for an educational use. Humans remain in the loop of defining, refining, and using AI models. We highlight the six desirable characteristics of AI models for education” (Cardona et al., 2023, p. 55)“
1. Alignment of the AI Model to Educators’ Vision for Learning: When choosing to use AI in educational systems, decision makers prioritize educational goals, the fit to all we know about how people learn, and alignment to evidence-based best practices in education.
1. Data Privacy: Ensuring security and privacy of student, teacher, and other human data in AI systems is essential.
1. Notice and Explanation: Educators can inspect edtech to determine whether and how AI is being incorporated within edtech systems. Educators’ push for AI models can explain the basis for detecting patterns and/or for making recommendations, and people retain control over these suggestions.
1. Algorithmic Discrimination Protections: Developers and implementers of AI in education take strong steps to minimizing bias and promoting fairness in AI models.” (Cardona et al., 2023, p. 55)“
1. Safe and Effective Systems: The use of AI models in education is based on evidence of efficacy (using standards already established in education for this purpose) and work for diverse learners and in varied educational settings.
1. Human Alternatives, Consideration and Feedback: AI models that support transparent, accountable, and responsible use of AI in education by involving humans in the loop to ensure that educational values and principles are prioritized.” (Cardona et al., 2023, p. 56)