Chapter 4: Learning AI’s powers of work with natural forms of input and the foundational strengths of AI models (as discussed in the What is AI? section), AI can be an especially strong toolkit for expanding the adaptivity provided to students. And yet, especially with AI, adaptivity is always more specific and limited than what a broad phrase like “meet students where they are” might suggest. Core limits arise from the nature of the model at the heart of any specific AI-enabled system. Models are approximations of reality. When important parts of human learning are left out of the model or less fully developed, the resulting adaptivity will also be limited, and the resulting supports for learning may be brittle or narrow. Consequently, this section on learning focuses on one key concept: Work toward AI models that fit the fullness of visions for learning— and avoid limiting learning to what AI can currently model well. AI models are demonstrating greater skills because of advances in what are called “large language models” or sometimes “foundational models.” These very general models still have limits. For example, generative AI models discussed in the mainstream news can quickly generate convincing essays about a wide variety of topics while other models can draw credible images based on just a few prompts. Despite the excitement about foundational models, experts in our listening sessions warned that AI models are narrower than visions for human learning and that designing learning environments with these limits in mind remains very important. The models are also brittle and can’t perform well when contexts change. In addition, they don’t have the same “common sense” judgment that people have, often responding in ways that are unnatural or incorrect. Given the unexpected ways in which foundational models miss the mark, keeping humans in the loop remains highly important. P a g e | 31
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