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Generative AI: Advanced Technologies, Applications, and Impacts
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Generative AI: Advanced Technologies, Applications, and Impacts

A special issue of Sci (ISSN 2413-4155). This special issue belongs to the section "Computer Sciences, Mathematics and AI".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 5280

Special Issue Editors

Prof. Dr. Claus Jacob

E-Mail Website
Guest Editor
Division of Bioorganic Chemistry, School of Pharmacy, Saarland University, D-66123 Saarbruecken, Germany
Interests: bioorganic chemistry; catalytic sensor/effector agents; epistemology; intracellular diagnostics; nanotechnology; natural products; reactive sulfur and selenium species; redox regulation via the cellular thiolstat
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Bellavista

E-Mail Website
Guest Editor
Department of Computer Science & Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: wireless sensor and actuator networks; middleware for sensor and actuator networks; vehicular sensor networks; edge computing; fog computing; online stream processing of sensing dataflows; IoT and big data processing; pervasive and mobile computing; cooperative networking; cyber physical systems for Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Ognjen Arandjelović

E-Mail Website
Guest Editor
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK
Interests: computer vision; pattern recognition; machine learning; bioinformatics statistics mathematical modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent successes and the widespread popularity of Generative AI have attracted growing interest and opened up new opportunities, not only in terms of the exploitation of Generative AI tools in several application domains, but also in terms of its novel scientific/technological challenges, e.g., making it more decentralized and edge-based in cloud continuum deployment environments. As a notable example, Generative AI models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are capable of creating high-quality content in diverse modes (including text, images, videos, etc.) and improving/accelerating several research/business tasks in many sectors, such as via dataset augmentation for simulations and digital twins. These capabilities bring enormous opportunities to a wide range of research areas, for both basic and applied research. Actual applications range from humanities and social sciences to pharmacy and medicine and include innovative AI-driven drug design and healthcare.

This Special Issue aims to provide a fresh and up-to-date publication venue for researchers and practitioners to share novel and high-quality contributions related to all aspects of Generative AI, from associated technologies and advancements to novel fields of applications and related tools and lessons learned from performance evaluation and experiences over large-scale testbeds.

Topics of interest for the Special Issue include, but are not limited to, the following:

  • Generative AI for digital twinning;
  • Generative AI for edge computing and edge-based Generative AI;
  • Metaverse applications exploiting Generative AI;
  • Generative AI under runtime and dynamic quality (e.g., time latency) constraints;
  • Internet and B5G infrastructure support for Generative AI;
  • Privacy and security of Generative AI impactive federated learning and trusted execution environments;
  • Benchmarking Generative AI solutions in different scientific and application domains, including AI-guided drug design and healthcare;
  • Generative AI’s use of software tools.
  • Applications and case studies. 

Prof. Dr. Claus Jacob
Prof. Dr. Paolo Bellavista
Dr. Ognjen Arandjelović
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Sci is an international peer-reviewed open access monthly 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

  • generative AI
  • digital twins
  • edge computing
  • cloud continuum
  • AI in medicine

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

Published Papers (3 papers)

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16 pages, 275 KB  
Article
Towards Responsible Artificial Intelligence Adoption: Emerging and Existing Ethical Issues in Africa
by Dolapo Faith Sule
Sci 2026, 8(3), 60; https://doi.org/10.3390/sci8030060 - 5 Mar 2026
Viewed by 534
Abstract
This study investigats both emerging and existing ethical issues associated with the adoption of artificial intelligence (AI) in Africa, a region characterised by unique socio-economic and cultural complexities. Even though AI adoption is rapidly transforming and delivering substantial benefits in sectors such as [...] Read more.
This study investigats both emerging and existing ethical issues associated with the adoption of artificial intelligence (AI) in Africa, a region characterised by unique socio-economic and cultural complexities. Even though AI adoption is rapidly transforming and delivering substantial benefits in sectors such as healthcare, finance, agriculture, education, industry, and governance, its implementation still raises ethical concerns. These ethical issues include digital colonialism, algorithmic bias, job displacement, limited infrastructure, data scarcity, linguistic diversity, and the risk of imposing foreign values that may undermine indigenous knowledge and social cohesion. Grounded in Afro-communitarianism and stakeholder theory, which emphasises communal values such as Ubuntu and cooperative engagement among stakeholders, this desk-based research identifies these major challenges and introduces a culturally grounded framework for responsible AI adoption in Africa. The framework calls for stronger governance, capacity building, collaboration among stakeholders, and tailored strategies across multiple stakeholders to ensure AI supports Africa’s inclusive and sustainable progress. Full article
(This article belongs to the Special Issue Generative AI: Advanced Technologies, Applications, and Impacts)
23 pages, 735 KB  
Article
Generative AI as a Student Research Assistant: The Relationship of Academic and Research Practices in Higher Education
by Walery Okulicz-Kozaryn
Sci 2026, 8(1), 24; https://doi.org/10.3390/sci8010024 - 22 Jan 2026
Viewed by 859
Abstract
This study analyzes the observed patterns of Generative Artificial Intelligence (Generative AI) use by students in higher education through the lens of the sociotechnical systems (STS) theory, focusing on the academic subsystem. The empirical basis is a survey of 2083 students (3686 responses) [...] Read more.
This study analyzes the observed patterns of Generative Artificial Intelligence (Generative AI) use by students in higher education through the lens of the sociotechnical systems (STS) theory, focusing on the academic subsystem. The empirical basis is a survey of 2083 students (3686 responses) from seven countries in Central and Eastern Europe, Central Asia, and Central Africa. Based on these data, two proxy indicators are proposed: A1, reflecting the overall academic use of Generative AI and A2, characterizing the use of Generative AI in a research context. The results show that Generative AI is widely incorporated into students’ academic activities (A1 = 79.06%), while research-oriented use remains less common (A2 = 46.66%) and varies significantly across subsamples. A joint analysis of A1 and A2, visualized as a zoned space A1–A2, reveals different configurations of academic practices: from a predominance of routine educational use to a more pronounced focus on research tasks. Cross-country comparisons show that in certain contexts (e.g., Kazakhstan and one of the Ukrainian subsamples), Generative AI is more often used in a research context, while in other cases, its use remains predominantly educational and routine. In this sense, the results indicate that Generative AI is beginning to fulfill the role of an emerging student research assistant in students’ academic life: technology has already become a familiar tool for completing educational tasks, but its use in supporting research activities remains fragmented. The proposed model and proxy indicators allow us to describe and compare current configurations of Generative AI use in the academic subsystem. The obtained results provide a basis for further research aimed at a deeper understanding of the factors determining the inclusion of Generative AI in student research practice, as well as for the development of management approaches regarding its use in higher education. Full article
(This article belongs to the Special Issue Generative AI: Advanced Technologies, Applications, and Impacts)
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14 pages, 278 KB  
Article
Transformers and State-Space Models: Fine-Tuning Techniques for Solving Differential Equations
by Vera Ignatenko, Anton Surkov, Vladimir Zakharov and Sergei Koltcov
Sci 2025, 7(3), 130; https://doi.org/10.3390/sci7030130 - 11 Sep 2025
Viewed by 3008
Abstract
Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from [...] Read more.
Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied mathematics, physics, and engineering—remains insufficiently explored. For the first time, we applied LLMs as translators from the textual form of an equation into the textual representation of its analytical solution for a broad class of equations. More precisely, we introduced a benchmark and fine-tuning protocol for differential equation solving with pre-trained LLMs. We curated a dataset of 300,000 differential equations and corresponding solutions to fine-tune T5-small, Phi-4-mini, DeepSeek-R1-Distill-Qwen, and two Mamba variants (130M and 2.8B parameters). Performance was evaluated using BLEU and TeXBLEU metrics. Phi-4-mini achieved the best results, with average BLEU > 0.9 and TeXBLEU > 0.78 across all considered equation classes, which shows the strong generalization abilities of the model. Therefore, this model should be further investigated on a broader class of differential equations and potentially can be used as a part of mathematical agents for solving more complex particular tasks, for example, from physics or engineering. Based on our results, DeepSeek-R1-Distill-Qwen consistently underperformed, while T5 showed strong results for the most frequent equation type but degraded on less common ones. Mamba models achieved the highest TeXBLEU scores despite relatively low BLEU, attributable to their production of lengthy outputs mixing correct expressions with irrelevant ones. Full article
(This article belongs to the Special Issue Generative AI: Advanced Technologies, Applications, and Impacts)
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