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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.13348 |
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| _version_ | 1866911185161420800 |
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| author | LearnLM Team : Martín, Alicia Globerson, Amir Wang, Amy Shekhawat, Anirudh Iurchenko, Anna Choudhury, Anisha Hassidim, Avinatan Çakmakli, Ayça Evron, Ayelet Shasha Yang, Charlie Heldreth, Courtney Akrong, Diana Elidan, Gal Mu, Hairong Li, Ian Cohen, Ido Chou, Katherine Singh, Komal Borovoi, Lev Hackmon, Lidan Belinsky, Lior Fink, Michael Efron, Niv Singh, Preeti Levitt, Rena Agarwal, Shashank Sharon, Shay Lee-Joe, Tracey Hao, Xiaohong Gold-Zamir, Yael Haramaty, Yael Mor, Yishay Sinai, Yoav Bar Matias, Yossi |
| author_facet | LearnLM Team : Martín, Alicia Globerson, Amir Wang, Amy Shekhawat, Anirudh Iurchenko, Anna Choudhury, Anisha Hassidim, Avinatan Çakmakli, Ayça Evron, Ayelet Shasha Yang, Charlie Heldreth, Courtney Akrong, Diana Elidan, Gal Mu, Hairong Li, Ian Cohen, Ido Chou, Katherine Singh, Komal Borovoi, Lev Hackmon, Lidan Belinsky, Lior Fink, Michael Efron, Niv Singh, Preeti Levitt, Rena Agarwal, Shashank Sharon, Shay Lee-Joe, Tracey Hao, Xiaohong Gold-Zamir, Yael Haramaty, Yael Mor, Yishay Sinai, Yoav Bar Matias, Yossi |
| contents | Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach as Learn Your Way. We report pedagogical evaluations of the different transformations and augmentations, and present the results of a a randomized control trial, highlighting the advantages of learning with Learn Your Way over regular textbook usage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards an AI-Augmented Textbook LearnLM Team : Martín, Alicia Globerson, Amir Wang, Amy Shekhawat, Anirudh Iurchenko, Anna Choudhury, Anisha Hassidim, Avinatan Çakmakli, Ayça Evron, Ayelet Shasha Yang, Charlie Heldreth, Courtney Akrong, Diana Elidan, Gal Mu, Hairong Li, Ian Cohen, Ido Chou, Katherine Singh, Komal Borovoi, Lev Hackmon, Lidan Belinsky, Lior Fink, Michael Efron, Niv Singh, Preeti Levitt, Rena Agarwal, Shashank Sharon, Shay Lee-Joe, Tracey Hao, Xiaohong Gold-Zamir, Yael Haramaty, Yael Mor, Yishay Sinai, Yoav Bar Matias, Yossi Computers and Society Human-Computer Interaction Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach as Learn Your Way. We report pedagogical evaluations of the different transformations and augmentations, and present the results of a a randomized control trial, highlighting the advantages of learning with Learn Your Way over regular textbook usage. |
| title | Towards an AI-Augmented Textbook |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.13348 |