Chapter 4: Learning engagement and self-regulation along with other aspects of instruction. In other contemporary examples, the computer ITS might focus on problem solving practice, while teachers work with students in small groups. Further, students can be in the loop with AI, as is the case with “open learner models”—a type of AI-enabled system that provides information to support student self-monitoring and reflection. Although R&D along the lines of an ITS should not limit the view of what’s possible, such an example is useful because so much research and evaluation has been done on the ITS approach. Researchers have looked across all the available high-quality studies in a meta-analysis and concluded that ITS approaches are effective. Right now, many school systems are looking at high-intensity human tutoring to help students with unfinished learning. Human tutoring is very expensive, and it is hard to find enough high-quality human tutors. With regard to large-scale needs, if it is possible for an ITS to supplement what human tutors do, it might be possible to extend beyond the amount of tutoring that people can provide to students. 4.3. Important Directions for Expanding AI-Based Adaptivity Adaptivity is sometimes referred to as “personalization.” Although this is a convenient term, many observers have noted how imprecise it is. For some educators, personalization means giving learners “voice and choice,” and for others it means that a learning management system recommends an individual “playlist” of activities to each student. Hidden in that imprecision is the reality that many edtech products that personalize do so in limited ways. Adjusting the difficulty and the order of lesson materials are among the two most common ways that edtech products adapt. And yet, any teacher knows there is more to supporting learning than adjusting the difficulty and sequence of P a g e | 33
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