Chapter 8: Recommendations Research that focuses on how AI-enabled systems can adapt to context (including variability among learners) in instructional approaches and across educational settings is essential to answering the question of, “Do specific applications of AI work in education, and if so, for whom and under what conditions?” The italicized phrase points to variability among learners and diversity in the settings for learning. We call upon innovators in R&D to focus their efforts to advance AI on the long tail of learning variability, where large populations of students would benefit from customization of learning. We also call on R&D to lead by establishing how trust can be strengthened in AI-enabled systems, building on the Blueprint’s call for safe and effective systems yet also including education-specific requirements, such as how teachers can be meaningfully involved in design phases, not only in implementation and evaluation. Although many products today are adaptive, some adapt on just one or a few dimensions of variability, such as student’s accuracy in problem solving. As teachers know, there are many more important ways to adapt to students’ strengths and needs. Students are neurodiverse and may have specific disabilities. They bring different assets from their experiences at home, in communities, and in their cultures. They have different interests and motivations. They are in different places in their journeys to master the English language. And they learn in varied settings. Classrooms and schools are different, and at home, students learn in informal settings in ways that could complement school learning. We recommend attention to “context” as a means for expressing the multiple dimensions that must be considered when elaborating the phrase “for whom and under what conditions.” We also acknowledge the role of researchers in conducting evaluations, which must now consider 100 | P a g e
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