Closing the Global Education Gap

AI Enables Adaptivity in Learning Adaptivity has been recognized as a key way in which technology can improve learning. AI can be a toolset for improving the adaptivity of edtech. AI may improve a technology’s ability to meet students where they are, build on their strengths, and grow their knowledge and skills. Because of 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.

Intelligent Tutoring Systems
An Example of AI Models One long-standing type of AI-enabled technology is an Intelligent Tutoring System (ITS). In an early success, scientists were able to build accurate models of how human experts solve mathematical problems. The resulting model was incorporated into a system that would observe student problem-solving as they worked on mathematical problems on a computer. Researchers who studied human tutors found that feedback on specific steps (and not just right or wrong solutions) is a likely key to why tutoring is so effective. For example, when a student diverged from the expert model, the system gave feedback to help the student get back on track. Importantly, this feedback went beyond right or wrong, and instead, the model was able to provide feedback on specific steps of a solution process. A significant advancement of AI, therefore, can be its ability to provide adaptivity at the step-by-step level and its ability to do so at scale with modest cost. As a research and development (R&D) field emerged to advance ITS, the work has gone beyond mathematics problems to additional important issues beyond step-by-step problem-solving. In the early work, some limitations can be observed. The kinds of problems that an ITS could support were logical or mathematical, and they were closed tasks, with clear expectations for what a solution and solution process should look like. Also, the “approximation of reality” in early AI models related to cognition and not to other elements of human learning, for example, social or motivational aspects. Over time, these early limitations have been addressed in two ways: by expanding the AI models and by involving humans in the loop, a perspective that is also important now. Today, for example, if an ITS specializes in feedback as a student practices, a human teacher could still be responsible for motivating student 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.

https://www2.ed.gov/documents/ai-report/ai-report.pdf