How to Improve the Explanatory Power of an Intelligent Textbook: a Case Study in Legal Writing

Explanatory processes are at the core of scientific investigation, legal reasoning, and education. However, effectively explaining complex or large amounts of information, such as that contained in a textbook or library, in an intuitive, user-centered way is still an open challenge. Indeed, different people may search for and request different types of information, even though texts typically have a predefined exposition and content. With this paper, we investigate how explanatory AI can better exploit the full potential of the vast and rich content library at our disposal. Based on a recent theory of explanations from Ordinary Language Philosophy, which frames the explanation process as illocutionary question-answering, we have developed a new type of interactive and adaptive textbook. Using the latest question-answering technology, our e-book software (YAI4Edu, for short) generates on-demand, expandable explanations that can help readers effectively explore teaching materials in a pedagogically productive way. It does this by extracting a specialized knowledge graph from a collection of books or other resources that helps identify the most relevant questions to be answered for a satisfactory explanation. We tested our technology with excerpts from a textbook that teaches how to write legal memoranda in the U.S. legal system. Then, to see whether YAI4Edu-enhanced textbooks are better than random and existing, general-purpose explanatory tools, we conducted a within-subjects user study with more than 100 English-speaking students. The students rated YAI4Edu's explanations the highest. According to the students, the explanatory content generated by YAI4Edu is, on average, statistically better than two baseline alternatives (P values below .005).