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

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Title: Harmonizing Assistance: Moderating Visual and Textual Aids in AI-Enhanced Textbook Reading with 'IRead'
Language: English
Authors: Xiaoyu Zhang, Vincent Dörig, Peng Cui, Vilém Zouhar, Torbjørn Netland, Mrinmaya Sachan
Source: International Journal of Artificial Intelligence in Education. 2025 35(6):3780-3812.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 33
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Visual Aids, Artificial Intelligence, Textbooks, Natural Language Processing, Visualization, Reading Processes, Computer Mediated Communication, Taxonomy
DOI: 10.1007/s40593-025-00515-4
ISSN: 1560-4292
1560-4306
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 "IRead," 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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1500040
Database: ERIC
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  Data: Harmonizing Assistance: Moderating Visual and Textual Aids in AI-Enhanced Textbook Reading with 'IRead'
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  Data: <searchLink fieldCode="AR" term="%22Xiaoyu+Zhang%22">Xiaoyu Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Vincent+Dörig%22">Vincent Dörig</searchLink><br /><searchLink fieldCode="AR" term="%22Peng+Cui%22">Peng Cui</searchLink><br /><searchLink fieldCode="AR" term="%22Vilém+Zouhar%22">Vilém Zouhar</searchLink><br /><searchLink fieldCode="AR" term="%22Torbjørn+Netland%22">Torbjørn Netland</searchLink><br /><searchLink fieldCode="AR" term="%22Mrinmaya+Sachan%22">Mrinmaya Sachan</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 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 "IRead," 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.
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