iTextbooks 2026

Table of Contents

• PDF
  
• LLM feedback isn’t automatically better: Static scaffolds outperform dynamic feedback in textbook-embedded practice 4-18 PDF
  Benny G. Johnson, Jeffrey S. Dittel, Oscar J. Ortiz, Rodrigo Bistolfi, Michelle W. Clark, Bill Jerome, Richard Benton, Rachel Van Campenhout
• Balancing Efficacy and Engagement in Interactive Texts 19-25 PDF
  Jonah Bard
• From concepts to learning objectives to questions: a pedagogically-aware prompting pipeline to generate questions from textbooks 26-36 PDF
  Sergey Sosnovsky, Keyang He, Andre Bekema
• Mitigating the “Triple Burden”: Grade-Level Translation of Science Texts Using Multiagent Systems and Prompt Optimization 37-50 PDF
  Joel Walsh, Oscar Arenas, Ashley Marquez, Karina Méndez Pérez, Benjamin Nye
• InkSpire: An LLM-Powered System for Designing and Generating Disciplinarily Aligned and Context-Aware Reading Scaffolds 51-59 PDF
  Yuwei Liang, Ruiqian Wang, Bodong Chen
• Automated Textbook Auditing with Multi-Agent LLM Systems 60-66 PDF
  Ciprian Cristescu, Adrian-Marius Dumitran, Angela-Liliana Dumitran, Gabriel Stefan
• Embedding In-Browser Linux Labs as Smart Content for Intelligent Textbooks 67-72 PDF
  Andrzej Wójtowicz
• Representing Image-Required Mathematics for Intelligent Textbooks: A Controlled Comparison of Diagram Encodings 73-86 PDF
  Ethan Croteau, Neil Heffernan
• Textbook-Informed Lecture Video Segmentation for Fine-Grained OER Reuse 87-94 PDF
  Zhaoyi Shi, Lubna Ali, Ulrik Schroeder