Chapter 4: Learning invent and create innovative approaches. We want AI models that enable progress on open, creative tasks. 5. From correct answers to additional goals. At the heart of many adaptivity approaches now on the market, the model inside the technology counts students' wrong answers and decides whether to speed up, slow down, or offer a different type of learning support. Yet, right and wrong answers are not the only learning goals. We want students to learn how to self-regulate when they experience difficulties in learning, for example, such as being able to persist in working on a difficult problem or knowing how and when to ask for help. We want learners to become skilled in teamwork and in leading teams. As students grow, we want them to develop more agency and to be able to act on their own to advance toward their own learning goals. Listing every dimension of expansion that we heard in our listening sessions is beyond the scope of this report. Some additional dimensions are presented in the following sections on Teaching, Assessment, and Research. For example, in Research, we discuss all the ways in which AI systems have trouble with context—context that humans readily grasp and consider. Overall, constituents in the listening sessions realized we need an ambitious outlook on learning to respond to the future today’s learners face. Constituents were concerned about ways in which AI might narrow learning. For example, if the incorporation of AI into education slowed attention to students’ skills on creative, open-ended tasks and their ability to lead and collaborate in teams, then school districts may be less able to realize their students’ progress in relation to a Portrait of a Graduate who excels in communication and other skills valued in communities and careers. P a g e | 36
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