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Future Internet
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Artificial Intelligence: Innovation, Applications and Transformative Experiences
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Artificial Intelligence: Innovation, Applications and Transformative Experiences

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 11279

Special Issue Editors

Dr. Diego Vergara

E-Mail Website
Guest Editor
Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Ávila, 05005 Ávila, Spain
Interests: mechanical behavior of materials; failure analysis; fracture; materials; microstructure; fractography; mechanical engineering; materials engineering; tensile testing; corrosion; mechanical properties; metals; material characterization; materials testing; energy efficiency; hydrogen; serious games; virtual reality; gamification; active learning; finite element method; engineering design; mechanical testing; fracture mechanics; hydrogen embrittlement
Special Issues, Collections and Topics in MDPI journals
Dr. Pablo Fernández-Arias

E-Mail Website
Guest Editor
Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Ávila, 05005 Ávila, Spain
Interests: machine learning; virtual reality; deep learning; collaborative learning; active learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is widely recognized that artificial intelligence (AI) is bringing about a technological revolution in a variety of sectors. This Special Issue will explore how AI, when merged with advanced technologies such as machine learning and computer vision, is driving innovation in fields as diverse as healthcare, education, finance, and industry, among others. From algorithms that accurately diagnose diseases to autonomous systems that redefine mobility, AI has emerged as a catalyst for change and a strategic resource for solving complex problems on a global scale.

There is great interest in learning about the uses and applications of AI in a format that is reproducible by others. In this sense, this Special Issue will delve into practical applications of AI, highlighting success stories that have transformed the way organizations operate and create value. Also of interest are analyses of the advantages and/or disadvantages of using AI in certain sectors. Future trends in the use of AI are also welcome. This Special Issue will reflect on the transformative experiences that AI has generated at both the individual and collective levels. Ethical challenges, the need for responsible governance, and the social implications of the mass adoption of these technologies will be addressed. Beyond technical achievements, it will examine how AI redefines human interactions, work dynamics, and the perception of progress.

This Special Issue is open to submissions of original articles, literature reviews, case reports, or short communications, subject to a rigorous peer review, covering quantitative or qualitative analyses with good scientific soundness, providing new contributions in a specific sector or a comparison between different sectors. We warmly invite researchers to submit their contributions to this Special Issue. Potential topics include, but are not limited to, the following:

  • Artificial intelligence;
  • Technological innovation;
  • Machine learning;
  • Neural networks;
  • Big Data;
  • Automation;
  • Natural language processing;
  • Computer vision;
  • Robotics;
  • Predictive analytics;
  • Business intelligence;
  • Expert systems;
  • User experiences;
  • Digital transformation;
  • Industrial applications;
  • Generative models;
  • Emerging technology;
  • Intelligent solutions.

Dr. Diego Vergara
Dr. Pablo Fernández-Arias
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 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. Future Internet 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 1600 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
  • user experience
  • practical applications

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

Published Papers (6 papers)

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Research

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27 pages, 432 KB  
Article
Refactoring Loops in the Era of LLMs: A Comprehensive Study
by Alessandro Midolo and Emiliano Tramontana
Future Internet 2025, 17(9), 418; https://doi.org/10.3390/fi17090418 - 12 Sep 2025
Viewed by 272
Abstract
Java 8 brought functional programming to the Java language and library, enabling more expressive and concise code to replace loops by using streams. Despite such advantages, for-loops remain prevalent in current codebases as the transition to the functional paradigm requires a significant shift [...] Read more.
Java 8 brought functional programming to the Java language and library, enabling more expressive and concise code to replace loops by using streams. Despite such advantages, for-loops remain prevalent in current codebases as the transition to the functional paradigm requires a significant shift in the developer mindset. Traditional approaches for assisting refactoring loops into streams check a set of strict preconditions to ensure correct transformation, hence limiting their applicability. Conversely, generative artificial intelligence (AI), particularly ChatGPT, is a promising tool for automating software engineering tasks, including refactoring. While prior studies examined ChatGPT’s assistance in various development contexts, none have specifically investigated its ability to refactor for-loops into streams. This paper addresses such a gap by evaluating ChatGPT’s effectiveness in transforming loops into streams. We analyzed 2132 loops extracted from four open-source GitHub repositories and classified them according to traditional refactoring templates and preconditions. We then tasked ChatGPT with the refactoring of such loops and evaluated the correctness and quality of the generated code. Our findings revealed that ChatGPT could successfully refactor many more loops than traditional approaches, although it struggled with complex control flows and implicit dependencies. This study provides new insights into the strengths and limitations of ChatGPT in loop-to-stream refactoring and outlines potential improvements for future AI-driven refactoring tools. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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20 pages, 1328 KB  
Article
From Divergence to Alignment: Evaluating the Role of Large Language Models in Facilitating Agreement Through Adaptive Strategies
by Loukas Triantafyllopoulos and Dimitris Kalles
Future Internet 2025, 17(9), 407; https://doi.org/10.3390/fi17090407 - 6 Sep 2025
Viewed by 373
Abstract
Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study [...] Read more.
Achieving consensus in group decision-making often involves overcoming significant challenges, particularly reconciling diverse perspectives and mitigating biases hindering agreement. Traditional methods relying on human facilitators are usually constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel real-time facilitation framework, employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. This framework is distinguished by its real-time adaptive system architecture, which enables dynamic adjustments to facilitation strategies based on ongoing discussion dynamics. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs—ChatGPT 4.0, Mistral Large 2, and AI21 Jamba-Instruct—to synthesize consensus proposals that align with participants’ viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to refine consensus proposals based on user feedback iteratively. Experimental results indicate that ChatGPT 4.0 achieved the highest alignment with participant opinions and required fewer iterations to reach consensus. A one-way ANOVA confirmed that differences in performance between models were statistically significant. Moreover, descriptive analyses revealed nuanced differences in model behavior across various sustainability-focused discussion topics, including climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the promise of LLM-driven facilitation for improving collective decision-making processes and underscore the need for further research into robust evaluation metrics, ethical considerations, and cross-cultural adaptability. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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34 pages, 5804 KB  
Article
AI-MDD-UX: Revolutionizing E-Commerce User Experience with Generative AI and Model-Driven Development
by Adel Alti and Abderrahim Lakehal
Future Internet 2025, 17(4), 180; https://doi.org/10.3390/fi17040180 - 20 Apr 2025
Cited by 1 | Viewed by 1780
Abstract
E-commerce applications have emerged as key drivers of digital transformation, reshaping consumer behavior and driving demand for seamless online transactions. Despite the growth of smart mobile technologies, existing methods rely on fixed UI content that cannot adjust to local cultural preferences and fluctuating [...] Read more.
E-commerce applications have emerged as key drivers of digital transformation, reshaping consumer behavior and driving demand for seamless online transactions. Despite the growth of smart mobile technologies, existing methods rely on fixed UI content that cannot adjust to local cultural preferences and fluctuating user behaviors. This paper explores the combination of generative Artificial Intelligence (AI) technologies with Model-Driven Development (MDD) to enhance personalization, engagement, and adaptability in e-commerce. Unlike static adaptation approaches, generative AI enables real-time, adaptive interactions tailored to individual needs, providing a more engaging and adaptable user experience. The proposed framework follows a three-tier architecture: first, it collects and analyzes user behavior data from UI interactions; second, it leverages MDD to model and personalize user personas and interactions and third, AI techniques, including generative AI and multi-agent reinforcement learning, are applied to refine and optimize UI/UX design. This automation-driven approach uses a multi-agent system to continuously enhance AI-generated layouts. Technical validation demonstrated strong user engagement across diverse platforms and superior performance in UI optimization, achieving an average user satisfaction improvement of 2.3% compared to GAN-based models, 18.6% compared to Bootstrap-based designs, and 11.8% compared to rule-based UI adaptation. These results highlight generative AI-driven MDD tools as a promising tool for e-commerce, enhancing engagement, personalization, and efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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Review

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50 pages, 2359 KB  
Review
The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges
by Ajay Bandi, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor and Sri Vidya Vilipala
Future Internet 2025, 17(9), 404; https://doi.org/10.3390/fi17090404 - 4 Sep 2025
Viewed by 2242
Abstract
Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, [...] Read more.
Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input–output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system’s performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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32 pages, 2348 KB  
Review
The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
by Francesco Branda, Massimo Stella, Cecilia Ceccarelli, Federico Cabitza, Giancarlo Ceccarelli, Antonello Maruotti, Massimo Ciccozzi and Fabio Scarpa
Future Internet 2025, 17(4), 145; https://doi.org/10.3390/fi17040145 - 26 Mar 2025
Cited by 3 | Viewed by 5308
Abstract
The rapid emergence of infectious disease outbreaks has underscored the urgent need for effective communication tools to manage public health crises. Artificial Intelligence (AI)-based chatbots have become increasingly important in these situations, serving as critical resources to provide immediate and reliable information. This [...] Read more.
The rapid emergence of infectious disease outbreaks has underscored the urgent need for effective communication tools to manage public health crises. Artificial Intelligence (AI)-based chatbots have become increasingly important in these situations, serving as critical resources to provide immediate and reliable information. This review examines the role of AI-based chatbots in public health emergencies, particularly during infectious disease outbreaks. By providing real-time responses to public inquiries, these chatbots help disseminate accurate information, correct misinformation, and reduce public anxiety. Furthermore, AI chatbots play a vital role in supporting healthcare systems by triaging inquiries, offering guidance on symptoms and preventive measures, and directing users to appropriate health services. This not only enhances public access to critical information but also helps alleviate the workload of healthcare professionals, allowing them to focus on more complex tasks. However, the implementation of AI-based chatbots is not without challenges. Issues such as the accuracy of information, user trust, and ethical considerations regarding data privacy are critical factors that need to be addressed to optimize their effectiveness. Additionally, the adaptability of these chatbots to rapidly evolving health scenarios is essential for their sustained relevance. Despite these challenges, the potential of AI-driven chatbots to transform public health communication during emergencies is significant. This review highlights the importance of continuous development and the integration of AI chatbots into public health strategies to enhance preparedness and response efforts during infectious disease outbreaks. Their role in providing accessible, accurate, and timely information makes them indispensable tools in modern public health emergency management. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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Other

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33 pages, 2477 KB  
Systematic Review
Patient-Oriented Smart Applications to Support the Diagnosis, Rehabilitation, and Care of Patients with Parkinson’s: An Umbrella Review
by Rute Bastardo, João Pavão, Ana Isabel Martins, Anabela G. Silva and Nelson Pacheco Rocha
Future Internet 2025, 17(8), 376; https://doi.org/10.3390/fi17080376 - 19 Aug 2025
Viewed by 507
Abstract
This umbrella review aimed to identify, analyze, and synthesize the results of existing literature reviews related to patient-oriented smart applications to support healthcare provision for patients with Parkinson’s. An electronic search was conducted on Scopus, Web of Science, and PubMed, and, after screening [...] Read more.
This umbrella review aimed to identify, analyze, and synthesize the results of existing literature reviews related to patient-oriented smart applications to support healthcare provision for patients with Parkinson’s. An electronic search was conducted on Scopus, Web of Science, and PubMed, and, after screening using predefined eligibility criteria, 85 reviews were included in the umbrella review. The included studies reported on smart applications integrating wearable devices, smartphones, serious computerized games, and other technologies (e.g., ambient intelligence, computer-based objective assessments, or online platforms) to support the diagnosis and monitoring of patients with Parkinson’s, improve physical and cognitive rehabilitation, and support disease management. Numerous smart applications are potentially useful for patients with Parkinson’s, although their full clinical potential has not yet been demonstrated. This is because the quality of their clinical assessments, as well as aspects related to their acceptability and compliance with requirements from regulatory bodies, have not yet been adequately studied. Future research requires more aligned methods and procedures for experimental assessments, as well as collaborative efforts to avoid replication and promote advances on the topic. Full article
(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)
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