Towards an AI-Augmented Textbook

Classés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.

Les articles les plus proches

1. Très proche Pas pertinent

   GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models

   Ryugo Morita, Kousuke Watanabe, Jin-Jia Zhou et al. · 2025 · NASA/ESA Conference on Adaptive Hardware and Systems · cité 19 fois · Pas encore résumé

   Résumé scientifique

   Cognitive augmentation is a cornerstone in advancing education, particularly through personalized learning. However, personalizing extensive textual materials, such as narratives and academic textbooks, remains challenging due to their heavy use, which can hinder learner engagement and understanding. Building on cognitive theories like Dual Coding Theory—which posits that combining textual and visual information enhances comprehension and memory—this study explores the potential of Generative AI (GenAI) to enrich educational materials. We utilized large language models (LLMs) to generate concise text summaries and image generation models (IGMs) to create visually aligned content from textual…

2. Très proche Cite cet article Utilise cet article en profondeur Pas pertinent

   An experimental evaluation of an AI-powered interactive learning platform

   Courtney Heldreth, Diana Akrong, Laura M. Vardoulakis et al. · 2025 · Frontiers Artif. Intell. · Pas encore résumé

   Résumé scientifique

   Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more …

3. Très proche Pas pertinent

   Empowering Education by Developing and Evaluating Generative AI-Powered Tutoring System for Enhanced Student Learning

   Shivraj Banjade, Hiran Patel, Sangita Pokhrel · 2024 · Journal of Artificial Intelligence and Capsule Networks · cité 33 fois · Pas encore résumé

   Résumé scientifique

   Personalized learning has always been a dream for schools, educators, and students but until recently, educators didn’t have the time or resources to implement it on a large scale. With the advancements in AI, Generative AI can automate many of a teacher’s core tasks, such as creating lesson resources. providing lesson structures and key talking points, designing infographics, creating slideshows, and converting text into videos and images. This study details the development and evaluation of an AI-powered tutoring system designed to enhance student learning experiences. Motivated by the transformative potential of AI in education, the research aims to utilize large language models, includin…

4. Proche Pas pertinent

   Generative AI for Education: A Retrieval-Augmented System for Effective Feedback in Self-Assessment

   Juan Martínez-Romo, Lourdes Araujo, Laura Plaza et al. · 2025 · IEEE Global Engineering Education Conference · cité 3 fois · Pas encore résumé

   Résumé scientifique

   The application of generative AI in education has shown significant potential to enhance learning outcomes by providing personalized, adaptive feedback to students. In this work, we present a novel Retrieval-Augmented Generation (RAG) system designed to improve the explanations and feedback provided to students during self-assessment activities. The system we developed is grounded in the course's reference material, ensuring that the feedback remains accurate, consistent, and contextually relevant to the student's curriculum. The system retrieves information directly from the textbook, reducing ambiguity and interpretation errors, and generates responses tailored to the specific needs of eac…

5. Proche Pas pertinent

   AdaptLearn AI: A Generative AI Framework for Bidirectional Personalization in Education

   Shuryansh Gupta, Sanjay Tiwari, M. Arya et al. · 2025 · 2025 International Conference on Computing, Intelligence, and Application (CIACON) · cité 2 fois · Pas encore résumé

   Résumé scientifique

   Teacher burnout and student disengagement are twin crises crippling modern education, with 72% of educators overwhelmed by administrative tasks and 61% of students underserved by one-size-fits-all curricula. While generative AI tools like GPT-4 offer transformative potential, existing systems address only isolated needs—automating grading or personalizing content, but never both. This paper introduces AdaptLearn AI, the first framework to unify teacher support and student adaptation through bidirectional generative AI. Our architecture combines fine-tuned Large Large Language Models(LLMs) for dynamic content generation (e.g., adjusting STEM problems via few-shot learning) with Retrieval-Augm…

6. Proche Pas pertinent

   Teaching with Generative AI: moving forward with content creation

   T. Ullmann, Duygu Bektik, Chris Edwards et al. · 2024 · Ubiquity Proceedings · cité 11 fois · Pas encore résumé

   Résumé scientifique

   Generative AI is widely available and has raised the expectation that it will impact Education. Models, such as ChatGPT, can quickly produce plausible texts on a wide range of topics, and this capability may be of potential use in course content production. This paper selects several important course content production tasks, describes the prompts used, and assesses the quality of the automatically generated texts by a team of experts. Across all tasks, the Generative AI produced content that could help solve specific tasks by aiding with brainstorming, creating outlines, and adhering to particular writing guidance. In all cases, the content required adjustments and checking by human experts…

7. Proche Pas pertinent

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

   Xiaoyu Zhang, Vincent Dörig, Peng Cui et al. · 2025 · International Journal of Artificial Intelligence in Education · cité 1 fois · Pas encore résumé

   Résumé scientifique

   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, …

8. Proche Pas pertinent

   Generative AI in Teaching and Learning: Benefits and Challenges

   Umesh Silwal, R. Kafle, Ramesh Dhungana · 2025 · Journal of Nepal Physical Society · cité 1 fois · Pas encore résumé

   Résumé scientifique

   Recent advancements in Artificial Intelligence (AI), particularly Generative AI (GenAI), have brought both transformative opportunities and significant challenges to the future of teaching and learning practices. This article provides an overview of popular GenAI tools and explores strategies for integrating them into classroom instructions, their ethical considerations, and challenges. This work includes narratives of implementation demonstrating how specific GenAI tools were utilized by the authors, including the use of ChatGPT for drafting an assignment, NotebookLM for curating content, developing formative assessments, and providing study materials for students, and the application of AI…

Les travaux antérieurs sur lesquels il s’appuie

1. Apparenté* Pas pertinent

   Instruction Based on Adaptive Learning Technologies

   V. Aleven, Elizabeth Mclaughlin, A. Glenn et al. · 2016 · cité 273 fois · Pas encore résumé
2. Apparenté* Pas pertinent

   Assessment as Learning: Using Classroom Assessment to Maximize Student Learning

   L. Earl · 2003 · cité 920 fois · Pas encore résumé
3. Apparenté Pas pertinent

   Personalization through Adaptivity or Adaptability? A Meta-analysis on Simulation-based Learning in Higher Education

   O. Chernikova, D. Sommerhoff, Matthias Stadler et al. · 2024 · Educational Research and Reviews · cité 42 fois · Pas encore résumé

   Résumé scientifique

   This meta-analysis builds on 217 empirical studies in higher education and investigates the role of the different forms of adaptivity and adaptability as personalization strategies in simulation-based learning environments for complex skills in higher education. The strategies used to personalize scaffolding and task progression were the central point in this meta-analysis. We identified conditions under which personalization advances complex skills in higher education. The results indicate that whereas adaptivity (i.e., computer makes decisions) is more effective for scaffolding, adaptability (the decisions made by individual learners) seem more beneficial for task progression. We conclude …

4. Apparenté* Pas pertinent

   A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?

   Matthew L. Bernacki, Meghan J. Greene, Nikki G. Lobczowski · 2021 · Educational Psychology Review · cité 433 fois · Pas encore résumé
5. Apparenté Pas pertinent

   Toward a taxonomy of adaptivity for learning

   J. Plass, S. Pawar · 2020 · Journal of Research on Technology in Education · cité 185 fois · Pas encore résumé

   Résumé scientifique

   Abstract Adaptive learning and personalization have long been of great interest to learning designers and educators, and recent technological advances that have opened up a range of new possibilities for adaptivity. However, we lack clear definitions of the terms adaptivity and personalization, and the theoretical and empirical soundness of implementations of corresponding systems varies greatly. We therefore first provide definitions for key concepts related to adaptivity. We then discuss what variable systems should adapt for, how these variables can be measured, and what modifications to the learner experience can be made based on these variables. We propose a taxonomy of adaptivity that …

Les articles qui le citent

1. Proche Pas pertinent

   A Semi-Automated System for Generating Dialogue-Based TTS Lessons Using Large Language Models: An Exploratory Study of Educational Potential

   G. Kumoi, Fumie Watanabe, Tota Suko et al. · 2026 · arXiv.org · preprint, non relu par les pairs · cité 2 fois · Pas encore résumé

   Résumé scientifique

   This study proposes a semi-automated system for generating dialogue-based lessons using Large Language Models (LLMs) and Text-to-Speech (TTS) technology, and exploratorily examines its educational potential via a practical quasi-experiment. The system augments rather than replaces educators through a three-stage human-in-the-loop workflow (LLM-based slide/narration generation, educator review, automated audiovisual integration), and introduces a novel method for generating Expert-Novice dialogue narration based on cognitive apprenticeship theory. In a study of 245 first-year high school students who sequentially experienced three lesson formats (instructor voice, single-speaker TTS, dialogue…

2. Proche Pas pertinent

   DeepTutor: Towards Agentic Personalized Tutoring

   Bin Zhao, Jiahao Zhang, Xubin Ren et al. · 2026 · arXiv.org · preprint, non relu par les pairs · cité 2 fois · Pas encore résumé

   Résumé scientifique

   Education is one of the most promising real-world applications for Large Language Models (LLMs). However, current LLMs rely on static pre-training knowledge and lack adaptation to individual learners, while existing RAG systems fall short in delivering personalized, guided feedback. To bridge this gap, we present DeepTutor, a fully open-source agentic framework that unifies citation-grounded problem tutoring with difficulty-calibrated question generation. A hybrid personalization engine couples static knowledge grounding with dynamic learner memory, continuously adapting each interaction to the student's evolving needs. The same personalization substrate further extends to adaptive learning …

3. Apparenté Pas pertinent

   Greenwashing AI in Higher Ed: An environmental argument for universities’ resistance to Big Tech

   Matt Riemland · 2026 · Open Research Europe · Pas encore résumé

   Résumé scientifique

   Higher education’s (“Higher Ed”) rushed embrace of commercial AI technologies has not allowed for sufficient reflection on their serious ecological consequences. This issue is complicated by Big Tech corporations becoming increasingly intertwined in university ecosystems while profiting enormously from AI expansion. As a contribution to Open Research Europe’s Universities for Sustainability collection, this essay explores universities’ potential actions in response to AI’s growing environmental harms, urging higher education institutions (HEIs) to resist Big Tech’s encroachment into the sector. First, the paper outlines the increasing entanglements between universities and Big Tech. It then …