Chapter 7: Research and Development could complement school learning. These are all important dimensions of “context.” Zone 3 indicates highly adaptive learning, where standardization is less successful and where we need to discover a wider variety of approaches to engage learners and sustain powerful learning. Researchers in our listening sessions noted the promise of Zone 3 because AI’s ability to recognize patterns in data can extend beyond the most common patterns and because AI’s ability to generate customized content can extend beyond what people can reasonably generate on their own. Notice that although the Zone 1 bar appears to be the tallest, and thus tends to attract initial attention, there are more students in Zones 2 and 3, the regions where AI can provide more help. Thus, it’s important to ask where AI researchers and developers are directing their attention. When we say a model “fits,” are we saying it fits the most common and typical uses by teachers and learners? This sort of R&D is easier to do. However, machine learning and AI also can tailor a model to the less common and more culturally specific contexts, too. Therefore, how can constituents cultivate interdisciplinary expertise to direct attention among researchers and developers to focus on the long tail? If we do, the quality of what we do for those represented in that tail can be more adaptive and more context-sensitive. And to be most effective, it will require the integration of contextual, content, and technical expertise. Within the long-tail challenge, the community is wondering how we can get to research insights that are both general and specific enough. When research produces very general abstractions about learning, it often doesn’t give developers enough guidance on exactly how to adjust their learning environments. Conversely, when research produces a specific 77 | P a g e
88 Publizr Home