Harmonizing Assistance: Moderating Visual and Textual Aids in AI-Enhanced Textbook Reading with IRead

Abstract Textbooks continue to be one of primary mediums of learning. Students often need additional support during the process of reading textbooks, leading to several research efforts that aim to increase student engagement and provide tailored experiences in textbook reading. However, providing excessive information beyond the textbook can also distract students from the reading task. When enhancing the reading experience, one has to strike a delicate balance between providing sufficient informational support and maintaining students’ focus on textbook reading. Fusing together latest developments in large language models (LLMs) and their applications in education and several pedagogical theories, we design a textbook reading guidance mechanism. We introduce IR ead , an interactive tool for textbook reading which uses LLMs with visualization and interaction techniques, to enhance students’ reading and learning experiences. IRead incorporates conceptual visualizations that reflect the textbook’s content and features an AI-driven question bot that generates questions in response to student reading and interaction history. We evaluate IRead with a between-subject user study and measure the effectiveness of our methodology in supporting the students’ reading experience based on the Bloom’s Taxonomy and the ARCS model. We collect feedback from participants ranging from undergraduate to doctorate students. The results highlight the effectiveness of simple yet intuitive visualizations, such as the concept tree in IRead . We also derive general insights for the development of tools that enhance educational reading experiences.