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AI in Open Access
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AI in Open Access

A special issue of Publications (ISSN 2304-6775).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3524

Special Issue Editor

Dr. Alexandre López-Borrull

E-Mail Website
Guest Editor
Faculty of Information and Communication Sciences, Open University of Catalonia, Av. Tibidabo, 39-43, 08035 Barcelona, Catalunya, Spain
Interests: social media; open science; fake news; disinformation; scholarly communication

Special Issue Information

Dear Colleagues,

We aim to discuss how AI can be harnessed to prioritize community-driven knowledge dissemination, ensuring that academic research remains accessible, equitable, and free from commercial constraints.

The academic landscape is rapidly evolving, with AI offering unprecedented opportunities to enhance the peer-review process, improve research accessibility, and promote sustainable publishing practices. However, these advancements also bring challenges in balancing innovation with the core values of openness and community empowerment. We look forward to your contributions to this Special Issue as we navigate the possibilities that AI brings to open access publishing, with a shared commitment to fostering inclusive, sustainable, and transparent scholarly communication.

We solicit and encourage manuscripts that deal with the following and related topics:

  1. Application of generative AI in open-access;
  2. Open science;
  3. Community-engaged knowledge-sharing mechanism;
  4. Sustainable development strategies for the publishing industry;
  5. Digital transformation of academic journals and innovation in open-access publishing models;
  6. Legal and intellectual property challenges;
  7. Promotion of diverse and transparent academic communication systems.

Dr. Alexandre López-Borrull
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Publications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in open-access
  • open science
  • community-engaged knowledge-sharing mechanism
  • innovation in open-access publishing models
  • legal and intellectual property challenges
  • promotion of diverse and transparent academic communication systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

28 pages, 365 KiB  
Article
Tailoring Scientific Knowledge: How Generative AI Personalizes Academic Reading Experiences
by Anna Małgorzata Kamińska
Publications 2025, 13(2), 18; https://doi.org/10.3390/publications13020018 - 2 Apr 2025
Viewed by 434
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue AI in Open Access)
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23 pages, 665 KiB  
Article
The Origins and Veracity of References ‘Cited’ by Generative Artificial Intelligence Applications: Implications for the Quality of Responses
by Dirk H. R. Spennemann
Publications 2025, 13(1), 12; https://doi.org/10.3390/publications13010012 - 12 Mar 2025
Viewed by 1818
Abstract
The public release of ChatGPT in late 2022 has resulted in considerable publicity and has led to widespread discussion of the usefulness and capabilities of generative Artificial intelligence (Ai) language models. Its ability to extract and summarise data from textual sources and present [...] Read more.
The public release of ChatGPT in late 2022 has resulted in considerable publicity and has led to widespread discussion of the usefulness and capabilities of generative Artificial intelligence (Ai) language models. Its ability to extract and summarise data from textual sources and present them as human-like contextual responses makes it an eminently suitable tool to answer questions users might ask. Expanding on a previous analysis of the capabilities of ChatGPT3.5, this paper tested what archaeological literature appears to have been included in the training phase of three recent generative Ai language models: ChatGPT4o, ScholarGPT, and DeepSeek R1. While ChatGPT3.5 offered seemingly pertinent references, a large percentage proved to be fictitious. While the more recent model ScholarGPT, which is purportedly tailored towards academic needs, performed much better, it still offered a high rate of fictitious references compared to the general models ChatGPT4o and DeepSeek. Using ‘cloze’ analysis to make inferences on the sources ‘memorized’ by a generative Ai model, this paper was unable to prove that any of the four genAi models had perused the full texts of the genuine references. It can be shown that all references provided by ChatGPT and other OpenAi models, as well as DeepSeek, that were found to be genuine, have also been cited on Wikipedia pages. This strongly indicates that the source base for at least some, if not most, of the data is found in those pages and thus represents, at best, third-hand source material. This has significant implications in relation to the quality of the data available to generative Ai models to shape their answers. The implications of this are discussed. Full article
(This article belongs to the Special Issue AI in Open Access)
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