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Artificial Intelligence: Innovation, Applications and Transformative Experiences

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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: closed (31 January 2026) | Viewed by 89654

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Editors

Dr. Diego Vergara

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Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Universidad Católica de Ávila, 05005 Ávila, Spain
Interests: technology-enhanced learning; virtual learning environments; virtual reality; augmented reality; metallography; material characterization; mechanical properties; mechanical behavior of materials; mechanical testing; materials testing; stress and strain analysis; fracture mechanics; fractography; corrosion science; failure analysis; fatigue; finite element analysis; nuclear energy; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Dr. Pablo Fernández-Arias

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Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Universidad Católica de 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 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. 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 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
user experience
practical applications

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Related Special Issue

Artificial Intelligence: Innovation, Applications and Transformative Experiences—2nd Edition in Future Internet (2 articles)

Published Papers (12 papers)

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Editorial

Jump to: Research, Review, Other

5 pages, 158 KB

Open AccessEditorial

Editorial for the Special Issue “Artificial Intelligence: Innovation, Applications and Transformative Experiences”

by Diego Vergara and Pablo Fernández-Arias

Future Internet 2026, 18(4), 219; https://doi.org/10.3390/fi18040219 - 21 Apr 2026

Viewed by 485

Abstract

Artificial intelligence (AI) is driving profound changes in the digital transformation of society [...] Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

Research

Jump to: Editorial, Review, Other

19 pages, 6254 KB

Open AccessArticle

Earthquake Magnitude Detection Utilizing a Novel Hybrid Earth–Transformer–LSTM Architecture

by Amir A. Ghavifekr, Elman Ghazaei, Mohsen Mirzajani and Paolo Visconti

Future Internet 2026, 18(3), 143; https://doi.org/10.3390/fi18030143 - 11 Mar 2026

Cited by 2 | Viewed by 1024

Abstract

One of the complicated and demanding tasks in seismology is the reliable detection of earthquakes. The key challenge is that the detection models must be applied to a specific region, and models trained on one region may not perform as well in others. The limitations of datasets for most regions of the world pose another task. Comprehensive, high-quality datasets are essential for developing robust earthquake detection algorithms. Despite these challenges, developing effective earthquake detection systems is critically important. This paper proposes a novel deep network, Earth–Transformer–LSTM (ETL), to estimate earthquake magnitude with high precision. The proposed method uses Transformer encoders as its first layer to extract profound features from the dataset. To obtain highly accurate results, the extracted data is used as the input to the Long Short-Term Memory (LSTM) neural network. Additionally, one-dimensional convolution is replaced by Multi-Layer Perceptron (MLP), which performs better in Transformer encoders’ feed-forward networks. The Turkey earthquake dataset 2000–2018 was used in this research because significant earthquakes have occurred in this region in recent years. According to the obtained results, the proposed method’s Root Mean Squared Error (RMSE) is 0.7, representing a noticeable improvement over advanced conventional models. Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

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25 pages, 1817 KB

Open AccessArticle

Perceived Learning vs. Engagement in AI-Assisted Homework: A Comparative Study of ChatGPT Use Across High School, University, and Teachers in Sonora, Mexico (2024–2025)

by Raquel Torres-Peralta, Federico Cirett-Galán, María del Carmen Heras-Sanchez, Karla Lerma-Molina and Ilse Espinoza-Flores

Future Internet 2026, 18(3), 122; https://doi.org/10.3390/fi18030122 - 28 Feb 2026

Cited by 2 | Viewed by 2351

Abstract

This study examines how generative AI is adopted and experienced across educational levels in Sonora, Mexico, and whether students’ perceived learning aligns with engagement behaviors during AI-assisted homework. We analyze survey data from 2024–2025 covering 1477 participants (high school and university students and teachers) from public and private institutions, including adoption, perceived learning and time savings, help-seeking preferences (teachers vs. ChatGPT vs. Google), and ethical concerns. To move beyond self-reports alone, we introduce a Learning Engagement Index (LEI; 0–1) based on three student behaviors when using ChatGPT to complete academic tasks: reading AI responses, modifying outputs, and integrating personal ideas. Adoption was widespread but consistently higher in university than in high school for both students and teachers. University students reported slightly higher perceived learning and greater time savings. LEI scores were generally high and higher among university students, indicating more frequent engagement behaviors such as reading and adapting AI outputs rather than copying them. However, perceived learning showed only weak alignment with LEI, suggesting that students’ self-assessments do not consistently track the engagement actions measured by the index. A complementary GitHub Copilot Free (version GPT-4) experiment (n = 16) indicated faster task completion and improved task completeness, while also highlighting the risk of reduced algorithmic reasoning when AI suggestions are used uncritically. Overall, the findings point to the need for pedagogical approaches that emphasize guided use, verification practices, and assessment designs that more directly evidence learning in AI-mediated settings. Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

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28 pages, 2702 KB

Open AccessArticle

Adaptive and Sustainable Smart Environments Using Predictive Reasoning and Context-Aware Reinforcement Learning

by Abderrahim Lakehal, Boubakeur Annane, Adel Alti, Philippe Roose and Soliman Aljarboa

Future Internet 2026, 18(1), 40; https://doi.org/10.3390/fi18010040 - 8 Jan 2026

Cited by 1 | Viewed by 1484

Abstract

Smart environments play a key role in improving user comfort, energy efficiency, and sustainability through intelligent automation. Nevertheless, real-world deployments still face major challenges, including network instability, delayed responsiveness, inconsistent AI decisions, and limited adaptability under dynamic conditions. Many existing approaches lack advanced context-awareness, effective multi-agent coordination, and scalable learning, leading to high computational cost and reduced reliability. To address these limitations, this paper proposes MACxRL, a lightweight Multi-Agent Context-Aware Reinforcement Learning framework for autonomous smart-environment control. The system adopts a three-tier architecture consisting of real-time context acquisition, lightweight prediction, and centralized RL-based decision learning. Local agents act quickly at the edge using rule-based reasoning, while a shared CxRL engine refines actions for global coordination, combining fast responsiveness with continuous adaptive learning. Experiments show that MACxRL reduces energy consumption by 45–60%, converges faster, and achieves more stable performance than standard and deep RL baselines. Future work will explore self-adaptive reward tuning and extend deployment to multi-room environments toward practical real-world realization. Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

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21 pages, 1171 KB

Open AccessArticle

Methodology for Detecting Suspicious Claims in Health Insurance Using Supervised Machine Learning

by Jose Villegas-Ortega, Luis Napoleon Quiroz Aviles, Juan Nazario Arancibia, Wilder Carpio Montenegro, Rosa Delgadillo and David Mauricio

Future Internet 2025, 17(12), 584; https://doi.org/10.3390/fi17120584 - 18 Dec 2025

Cited by 3 | Viewed by 1347

Abstract

Health insurance fraud (HIF) places a substantial economic burden on global health systems. While supervised machine learning (SML) offers a promising solution for its detection, most approaches are ad hoc and lack a systematic methodological framework that ensures replicability, adaptability, and effectiveness, especially in contexts with severe class imbalance. We developed PDHIF (Phases for Detecting Fraud in Health Insurance), a six-phase systematic methodology that introduces a holistic focus that integrates fraud theory, actors, manifestations, and factors with the complete SML lifecycle. We applied this methodology in a case study using a dataset of 8.5 million claims from a public health insurance system in Peru. We trained and evaluated three SML models (Random Forest, XGBoost, and multilayer perceptron) in two experimental scenarios: one with the original, highly unbalanced dataset and another with a training set balanced via the K-means SMOTE technique. When PDHIF was applied, the results revealed a stark contrast: in the unbalanced scenario, the models were ineffective at detecting fraud (F1 score < 0.521) despite high accuracy (>98%). In the balanced scenario, the performance improved dramatically. The best-performing model, RF, achieved an F1 score of 0.994, a sensitivity of 0.994, and an AUC of 0.994 on the test set, demonstrating a robust ability to distinguish suspicious claims. Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

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14 pages, 1602 KB

Open AccessArticle

Frame and Utterance Emotional Alignment for Speech Emotion Recognition

by Seounghoon Byun and Seok-Pil Lee

Future Internet 2025, 17(11), 509; https://doi.org/10.3390/fi17110509 - 5 Nov 2025

Cited by 2 | Viewed by 1391

Abstract

Speech Emotion Recognition (SER) is important for applications such as Human–Computer Interaction (HCI) and emotion-aware services. Traditional SER models rely on utterance-level labels, aggregating frame-level representations through pooling operations. However, emotional states can vary across frames within an utterance, making it difficult for models to learn consistent and robust representations. To address this issue, we propose two auxiliary loss functions, Emotional Attention Loss (EAL) and Frame-to-Utterance Alignment Loss (FUAL). The proposed approach uses a Classification token (CLS) self-attention pooling mechanism, where the CLS summarizes the entire utterance sequence. EAL encourages frames of the same emotion to align closely with the CLS while separating frames of different classes, and FUAL enforces consistency between frame-level and utterance-level predictions to stabilize training. Model training proceeds in two stages: Stage 1 fine-tunes the wav2vec 2.0 backbone with Cross-Entropy (CE) loss to obtain stable frame embeddings, and stage 2 jointly optimizes CE, EAL and FUAL within the CLS-based pooling framework. Experiments on the IEMOCAP four-class dataset demonstrate that our method consistently outperforms baseline models, showing that the proposed losses effectively address representation inconsistencies and improve SER performance. This work advances Artificial Intelligence by improving the ability of models to understand human emotions through speech. Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

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27 pages, 432 KB

Open AccessArticle

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

Cited by 6 | Viewed by 2678

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 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

Open AccessArticle

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

Cited by 2 | Viewed by 1695

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 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

Open AccessArticle

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 10 | Viewed by 4709

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 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

Open AccessReview

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

Cited by 91 | Viewed by 58138

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, 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

Open AccessReview

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 19 | Viewed by 11011

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 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

Open AccessSystematic 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

Cited by 1 | Viewed by 1624

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 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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