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Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology...
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Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 3160

Editor

Prof. Dr. Aneta Poniszewska-Maranda

E-Mail Website
Guest Editor
Institute of Information Technology, Lodz University of Technology, Lodz, Poland
Interests: software engineering; security of information systems; multi-agent systems; artificial intelligence; machine learning; large language models; generative AI; natural language processing; educational technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of Artificial Intelligence (AI), particularly in the domains of Generative AI (GenAI) and Large Language Models (LLMs), has fundamentally transformed how we interact with technology and process information. From ChatGPT and GPT-4 to Claude, Gemini, and emerging multimodal systems, these technologies are reshaping industries, education, research methodologies, and social interactions.

This Special Issue aims to explore the multifaceted impact of AI, GenAI, and LLMs across various domains, addressing the signficant opportunities and challenges that these technologies present. We invite researchers, practitioners, and thought leaders to contribute original research, comprehensive reviews, and innovative applications that will advance our understanding of these transformative technologies.

Topics of Interest Include, but are not limited to, the following:

Technical Advances and Methodologies:

  • Novel architectures and training methodologies for Large Language Models;
  • Multimodal AI systems integrating text, image, audio, and video;
  • Prompt engineering and optimization techniques;
  • Fine-tuning and adaptation strategies for domain-specific applications;
  • Evaluation metrics and benchmarking for generative AI systems.

Applications and Use Cases:

  • AI in education: Personalized learning, automated assessment, and pedagogical applications.
  • Healthcare applications: Medical diagnosis, drug discovery, and patient care.
  • Creative industries: Content generation, artistic creation, and media production.
  • Software engineering: Code generation, debugging, and automated programming.
  • Scientific research: Hypothesis generation, literature review automation, and data analysis.

Ethical and Societal Implications:

  • AI bias, fairness, and algorithmic accountability;
  • Privacy and security concerns in AI systems;
  • Intellectual property and copyright issues in generative AI;
  • Impact on employment and workforce transformation;
  • AI governance, regulation, and policy frameworks.

Human-AI Interaction:

  • User experience design for AI-powered systems;
  • Trust, transparency, and explainability in AI;
  • Collaborative intelligence and human–AI partnerships;
  • Psychological and cognitive aspects of AI interactions;
  • Digital literacy and AI education.

Technical Challenges and Solutions:

  • Hallucination detection and mitigation in LLMs;
  • Computational efficiency and environmental impact;
  • Data quality, curation, and training methodologies;
  • Robustness, reliability, and safety in AI systems;
  • Federated learning and distributed AI approaches.

Industry and Economic Impacts:

  • Startup ecosystems and AI entrepreneurship;
  • Digital transformation strategies.

Prof. Dr. Aneta Poniszewska-Maranda
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 1800 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

  • artificial intelligence
  • generative AI
  • large language models
  • machine learning
  • natural language processing
  • deep learning
  • neural networks
  • transformer models
  • AI ethics
  • human-AI interaction
  • prompt engineering
  • multimodal AI
  • AI applications
  • digital transformation
  • AI governance

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

Published Papers (4 papers)

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Research

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22 pages, 8252 KB  
Article
Event-Based Sentiment Analysis of Financial News Using Large Language Models: A Comprehensive Framework Integrating RAG, GNNs, and Multi-Agent Systems
by Amit Kulkarni and Varun Dogra
Information 2026, 17(6), 558; https://doi.org/10.3390/info17060558 - 5 Jun 2026
Viewed by 460
Abstract
The proliferation of digital financial news offers unprecedented opportunities for automated analysis of market-moving events. This paper presents a framework for event-based sentiment analysis of financial news that leverages Large Language Models (LLMs). The approach brings together three complementary ideas: Retrieval-Augmented Generation (RAG) [...] Read more.
The proliferation of digital financial news offers unprecedented opportunities for automated analysis of market-moving events. This paper presents a framework for event-based sentiment analysis of financial news that leverages Large Language Models (LLMs). The approach brings together three complementary ideas: Retrieval-Augmented Generation (RAG) for contextual enhancement, Graph Neural Networks (GNNs) for modeling relationships between events, and a multi-agent ensemble for orchestrated reasoning. The methodology targets well-known difficulties in financial text processing, including domain-specific terminology, implicit event detection, and temporal reasoning, and it combines transformer-based event extraction with sentiment classification enhanced by external knowledge retrieval. We evaluate six model configurations on an aggregated corpus of 14,851 financial news samples. On the event-detection task, every configuration reaches a weighted F1-score of 100%; we show that this is a ceiling effect produced by a binary event/no-event formulation over a highly imbalanced dataset rather than evidence of a difficult problem being solved, and we discuss what it implies for how such systems should be evaluated. On three-way sentiment classification, the strongest configuration—the multi-agent ensemble—reaches 87.4% accuracy, narrowly ahead of a RoBERTa (Robustly Optimized BERT Pretraining Approach) baseline at 87.2%; however, because the gaps reported between models are small and we did not run significance testing, we report them as indicative rather than definitive. The GNN component is described as part of the proposed design, but it has not yet been validated experimentally, and we state this limitation explicitly. The framework produces interpretable, structured outputs suited to downstream use in algorithmic trading, risk assessment, and investment decision support, and the paper contributes a reusable financial NLP pipeline together with a candid account of where the current evidence is, and is not, convincing. Full article
(This article belongs to the Special Issue Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society)
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26 pages, 953 KB  
Article
A Modular Approach to Automated News Generation Using Large Language Models
by Omar Juárez Gambino, Consuelo Varinia García Mendoza, Braulio Hernandez Minutti, Carol-Michelle Zapata-Manilla, Marco-Antonio Bernal-Trani and Hiram Calvo
Information 2026, 17(4), 319; https://doi.org/10.3390/info17040319 - 25 Mar 2026
Viewed by 886
Abstract
Advances in Generative Artificial Intelligence have enabled the development of models capable of generating text, images, and audio that are similar to what humans can create. These models often have valuable general knowledge thanks to their training on large datasets. Through fine-tuning or [...] Read more.
Advances in Generative Artificial Intelligence have enabled the development of models capable of generating text, images, and audio that are similar to what humans can create. These models often have valuable general knowledge thanks to their training on large datasets. Through fine-tuning or prompt-based adaptation, this knowledge can be applied to specific tasks. In this work, we propose a modular approach to automated news generation using Large Language Models, composed of an information retrieval module and a text generation module. The proposed system leverages both publicly available (open-weight) and proprietary Large Language Models, enabling a comparative evaluation of their behavior within the proposed news generation pipeline. We describe the experiments carried out with a total of five representative Large Language Models spanning both categories, detailing their configurations and performance. The results demonstrate the feasibility of using Large Language Models to automate this task and identify systematic differences in behavior across model categories, as well as the problems that remain to be solved to enable fully autonomous news generation. Full article
(This article belongs to the Special Issue Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society)
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21 pages, 1728 KB  
Article
Cyberbullying Detection Based on Hybrid Neural Networks and Multi-Feature Fusion
by Junkuo Cao, Yunpeng Xiong, Weiquan Wang and Guolian Chen
Information 2026, 17(2), 205; https://doi.org/10.3390/info17020205 - 16 Feb 2026
Viewed by 858
Abstract
Cyberbullying demonstrates notable metaphorical and contextual traits, characterized by a high-dimensional sparse semantic space and dynamic evolution. Pre-trained models utilize extensive textual data for learning and employ transformer-based word vector generation techniques to accurately capture intricate semantics and nuanced syntax in text. However, [...] Read more.
Cyberbullying demonstrates notable metaphorical and contextual traits, characterized by a high-dimensional sparse semantic space and dynamic evolution. Pre-trained models utilize extensive textual data for learning and employ transformer-based word vector generation techniques to accurately capture intricate semantics and nuanced syntax in text. However, although a single pre-trained model demonstrates strong performance in contextual modeling, it still faces challenges including inadequate feature representation and limited generalization capability in classifying cyberbullying texts. This study proposes a cyberbullying detection model employing BERT-BiGRU-CNN (BBGC) to address this issue. The BBGC model initially employs BERT to produce word embeddings, subsequently inputs them into a BiGRU layer to acquire sequence features, and finally utilizes a CNN for the extraction of local features. The features derived from BERT, BiGRU, and CNN are integrated, followed by the application of the softmax function to yield the final outcome of cyberbullying detection. Experimental findings indicate that the BBGC fusion model surpasses individual pre-trained models in the task of detecting cyberbullying text. Furthermore, in comparison to hybrid neural network models utilizing RoBERTa, ALBERT, DistilBERT and other pre-trained models, the BBGC model demonstrates considerable advantages. Full article
(This article belongs to the Special Issue Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society)
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Review

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22 pages, 670 KB  
Review
From Prediction to Stewardship: Framing Educational Data Science in the Age of Generative AI
by Danielle S. McNamara and Linh Huynh
Information 2026, 17(6), 610; https://doi.org/10.3390/info17060610 - 19 Jun 2026
Viewed by 309
Abstract
As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI [...] Read more.
As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI governance to propose stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional, epistemic, and institutional work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Rather than treating stewardship as a general ethics checklist, the article positions it as the governance of epistemic and pedagogical authority: who determines what counts as evidence, interpretation, and educational action when AI systems help produce those judgments. The synthesis suggests that while GenAI can support bounded analytic tasks, evidence for systemic educational transformation remains limited and uneven. The field’s primary challenge is therefore not technical performance alone, but the governance of interpretation, validation, delegation, and action. By centering provenance, uncertainty, accountable oversight, learner agency, and institutional learning, stewardship provides an actionable framework for anchoring analytic innovation in responsible educational improvement. Full article
(This article belongs to the Special Issue Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society)
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