Chapter 7: Research and Development We look forward to new meanings of “adaptive” that broaden outward from what the term has meant in the past decade. For example, “adaptive” should not always be a synonym of “individualized” because people are social learners. Researchers therefore are broadening “adaptivity” to include support for what students do as they learn in groups, a form of learning that is prevalent in schools across the U.S. The focus on context is not an accident. Context is a traditional challenge in AI. Thus, researchers and developers are wise to prioritizing context. Unless we invest more in AI that is contextsensitive, it is quite likely that AI will break and fail to achieve educational goals. Agreeing to prioritize context won’t be easy. As illustrated above in Figure 12, there will be a tension between depth of context and pace of technological advances in AI R&D. On the one hand, AI is sometimes presented as a race to be the first to advance new techniques or scale new applications—innovation is sometimes portrayed as rapidly going to scale with a minimally viable product, failing fast, and only after failure, dealing with context. On the other hand, researchers and developers see that achieving good innovations with AI in education will clearly require bringing more context into the process early and often. For example, researchers highlight that humans must be continually adjusting the goals for technology and have noted that when we set forth goals, we often don’t yet fully understand context; and as we learn about context, the goals must change. This suggests that context must be prioritized early and habitually in R&D; we don’t want to win a race to the wrong finish line. Figure 12: The tension between depth of context and pace of technological advances in AI 73 | P a g e
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