{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T17:35:56Z","timestamp":1770140156538,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Publications"],"abstract":"<jats:p>The scientific literature is expanding at an unprecedented pace, making it increasingly difficult for researchers, students, and professionals to extract relevant insights efficiently. Traditional academic publishing offers static, one-size-fits-all content that does not cater to the diverse backgrounds, expertise levels, and interests of readers. This paper explores how generative AI can dynamically personalize scholarly content by tailoring summaries and key takeaways to individual user profiles. Nine scientific articles from a single journal issue were used to create the dataset, and prompt engineering was applied to generate tailored insights for exemplary personas: a digital humanities and open science researcher, and a mining and raw materials industry specialist. The effectiveness of AI-generated content modifications in enhancing readability, comprehension, and relevance was evaluated. The results indicate that generative AI can successfully emphasize different aspects of an article, making it more accessible and engaging to specific audiences. However, challenges such as content oversimplification, potential biases, and ethical considerations remain. The implications of AI-powered personalization in scholarly communication are discussed, and future research directions are proposed to refine and optimize AI-driven adaptive reading experiences.<\/jats:p>","DOI":"10.3390\/publications13020018","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T16:26:31Z","timestamp":1743611191000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tailoring Scientific Knowledge: How Generative AI Personalizes Academic Reading Experiences"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5411-5426","authenticated-orcid":false,"given":"Anna Ma\u0142gorzata","family":"Kami\u0144ska","sequence":"first","affiliation":[{"name":"Institute of Culture Studies, University of Silesia in Katowice, ul. Uniwersytecka 4, 40-007 Katowice, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"ref_1","unstructured":"Alvarez, E., Lamagna, F., Miquel, C., and Szewc, M. (2020). Intelligent arxiv: Sort daily papers by learning users topics preference. arXiv, Available online: https:\/\/arxiv.org\/abs\/2002.02460."},{"key":"ref_2","unstructured":"Antu, S. A., Chen, H., and Richards, C. K. (, January July). Using LLM (Large Language Model) to improve efficiency in literature review for undergraduate research. Workshop on Empowering Education with LLMs-the Next-Gen Interface and Content Generation 2023 co-located with 24th International Conference on Artificial Intelligence in Education (AIED 2023), Tokyo, Japan."},{"key":"ref_3","unstructured":"arXiv (2025, February 20). arXiv statistics: 2021 submissions by area, Available online: https:\/\/info.arxiv.org\/help\/stats\/2021_by_area\/index.html."},{"key":"ref_4","unstructured":"Boboris, K. 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