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World
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AI-Powered Horizons: Shaping Our Future World
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AI-Powered Horizons: Shaping Our Future World

A special issue of World (ISSN 2673-4060).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5175

Special Issue Editors

Dr. Seongjin Lee

E-Mail Website
Guest Editor
Department of AI Convergence Engineering, Gyeongsang National University, Jinjusi 52828, Republic of Korea
Interests: AI; digital transformation; digital twin; autonomous system
Special Issues, Collections and Topics in MDPI journals
Dr. Euteum Choi

E-Mail Website
Guest Editor
Department of Information Sciences, Gyeongsang National University, Jinjusi 52828, Republic of Korea
Interests: avionics; SW health management; AI
Dr. Joontaek Oh

E-Mail Website
Guest Editor
School of Computer, Data & Information Science, University of Wisconsin–Madison, Madison, WI 53706-1613, USA
Interests: operating systems; system software

Special Issue Information

Dear Colleagues,

The field of artificial intelligence (AI) is undergoing rapid advancements, significantly influencing various industries and fundamentally altering human interaction with technology. The scientific community recognizes AI's transformative potential, particularly in autonomous systems, digital transformation, and intelligent automation domains. This Special Issue, "AI-Powered Horizons: Shaping Our Future World", is dedicated to exploring AI's critical roles in shaping our future, addressing its groundbreaking applications and the profound ethical considerations it raises.

The primary aim of this Special Issue is to provide a comprehensive examination of how AI is revolutionizing fields such as healthcare, urban development, and human–machine interaction. By focusing on AI-enabled manned and unmanned teaming, intelligent automation, and the development of smart cities, this issue aligns with the journal's broader scope of exploring technological innovations that drive societal progress. As we near the concept of technological singularity, where AI may surpass human intelligence, this issue also emphasizes the urgent need for developing robust ethical frameworks to guide AI's integration into critical areas of our lives.

Suggested themes for this issue include the development and impact of autonomous systems, the ethical challenges of AI in healthcare, the role of AI in fostering smart cities, and the implications of human–machine interaction in a digitally transformed world. Through these themes, this issue aims to provide insights into how AI is reshaping industries and shaping the future of human society.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: 

  1. Autonomous Systems;
  2. AI Enabled Manned and Unmanned Teaming;
  3. Digital Transformation; 
  4. AI-enabled Digital Twins;
  5. Intelligent Automation;
  6. Human–Machine Interaction;
  7. Smart Cities;
  8. AI Ethics;
  9. AI in Healthcare;
  10. Technological Singularity.

We look forward to receiving your contributions.

Dr. Seongjin Lee
Dr. Euteum Choi
Dr. Joontaek Oh
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. World 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 1200 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

  • autonomous system
  • AI enabled manned and unmanned teaming
  • digital transformation
  • AI enabled digital twin
  • intelligent automation
  • human–machine interaction
  • smart cities
  • AI ethics
  • AI in healthcare
  • technological singularity

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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

18 pages, 644 KiB  
Article
Responsible and Ethical Use of AI in Education: Are We Forcing a Square Peg into a Round Hole?
by Alexander Amigud and David J. Pell
World 2025, 6(2), 81; https://doi.org/10.3390/world6020081 - 3 Jun 2025
Viewed by 2325
Abstract
The emergence of generative AI has caused a major dilemma—as higher education institutions prepare students for the workforce, the development of digital skills must become a normative aim, while simultaneously preserving academic integrity and credibility. The challenge they face is not simply a [...] Read more.
The emergence of generative AI has caused a major dilemma—as higher education institutions prepare students for the workforce, the development of digital skills must become a normative aim, while simultaneously preserving academic integrity and credibility. The challenge they face is not simply a matter of using AI responsibly but typically of reconciling two opposing duties: (A) preparing students for the future of work, and (B) maintaining the traditional role of developing personal academic skills, such as critical thinking, the ability to acquire knowledge, and the capacity to produce original work. Higher education institutions must typically balance these objectives while addressing financial considerations, creating value for students and employers, and meeting accreditation requirements. Against this need, this multiple-case study of fifty universities across eight countries examined institutional response to generative AI. The content analysis revealed apparent confusion and a lack of established best practices, as proposed actions varied widely, from complete bans on generated content to the development of custom AI assistants for students and faculty. Oftentimes, the onus fell on individual faculty to exercise discretion in the use of AI, suggesting an inconsistent application of academic policy. We conclude by recognizing that time and innovation will be required for the apparent confusion of higher education institutions in responding to this challenge to be resolved and suggest some possible approaches to that. Our results, however, suggest that their top concern now is the potential for irresponsible use of AI by students to cheat on assessments. We, therefore, recommend that, in the short term, and likely in the long term, the credibility of awards is urgently safeguarded and argue that this could be achieved by ensuring at least some human-proctored assessments are integrated into courses, e.g., in the form of real-location examinations and viva voces. Full article
(This article belongs to the Special Issue AI-Powered Horizons: Shaping Our Future World)
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33 pages, 4288 KiB  
Article
An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques
by Salman Mahmood, Raza Hasan, Saqib Hussain and Rochak Adhikari
World 2025, 6(1), 15; https://doi.org/10.3390/world6010015 - 14 Jan 2025
Cited by 5 | Viewed by 2321
Abstract
Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a [...] Read more.
Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a comprehensive dataset of demographic, clinical, and respiratory function data, we employed AutoGluon to automate model selection, optimization, and ensembling, resulting in a model with 98.99% accuracy and a 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to provide both global and local interpretability, ensuring that clinicians can trust and understand model predictions. Additionally, counterfactual analysis enabled hypothetical scenario exploration, supporting personalized asthma management by allowing clinicians to assess potential interventions for individual patient risk profiles. To facilitate clinical adoption, a Streamlit v1.41.0 application was developed for real-time access to predictions and interpretability. This study addresses key gaps in asthma prediction, notably in model transparency and generalizability, while providing a practical tool for enhancing personalized care. Future research could expand the validation across diverse patient populations to reinforce the model’s robustness in broader clinical environments. Full article
(This article belongs to the Special Issue AI-Powered Horizons: Shaping Our Future World)
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