Advances in AI-Augmented E-Learning for Smart Cities
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 April 2026
Special Issue Editors
Interests: computers and information processing; systems engineering and theory; communications technology; computational and artificial intelligence; formal methods; E-learning; formal concept analysis
Interests: information systems; database systems; machine learning; prediction methods; data mining; engineering education; business data processing; business intelligence
Interests: artificial intelligence; machine learning; statistical learning; prediction methods; big data applications; knowledge management; data analysis; distributed databases; data mining; machine learning algorithms; prediction algorithms; NoSQL databases; relational databases; data handling; data warehouses; data visualization
Interests: design and building information systems; deep learning; artificial intelligence; data science and process mining
Interests: artificial intelligence; distributed systems; web information systems; web intelligence; wildfire management; remote sensing; environmental intelligence
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Special Issue Information
Dear Colleagues,
The evolution of smart cities is significantly reshaping the landscape of e-learning and distance learning, offering innovative pathways for enhancing the accessibility, personalization, and quality of education. Smart cities, with their integrated IoT architecture and high-speed network connectivity, facilitate the growth of dynamic, adaptive and responsive e-learning environments.
This Special Issue aims to explore the latest advances in educational technology and examine the role of smart cities in the development of advanced e-learning solutions such as AI-augmented adaptive learning platforms. These platforms are powered by artificial intelligence tools and dynamically adjust content delivery based on the performance and preferences of learners. Furthermore, ubiquitous connectivity through 5G, edge computing and smart IoT devices ensures seamless access to both learning materials and real-time smart city data sources, and supports the creation of various formal, informal and lifelong learning scenarios. Smart city infrastructure is the foundation of immersive and interactive learning environments that utilize technologies such as augmented reality (AR), virtual reality (VR) and digital twins. AR and VR technology can be employed to build immersive virtual labs and smart classrooms within smart campuses. Digital twins are an advanced tool that can be used to create realistic, data-driven simulations of urban environments that allow learners to experiment, analyze, and optimize city systems in a safe, interactive, and adaptive learning space. Gamification in AI-augmented e-learning for smart cities can enhance learner engagement and motivation by integrating game-like elements such as challenges, rewards, and progress tracking into urban-focused educational experiences. This Special Issue will also examine the data analysis of adaptive e-learning systems integrated into smart city infrastructure using methods such as data mining, process mining and learning analytics. Moreover, this Special Issue will address the application of formal methods in the specification and verification of e-learning systems using model checking.
The scope of this Special Issue encompasses a wide range of topics, including but not limited to, the following:
- Adaptive e-learning technologies;
- Artificial intelligence in e-learning;
- Mobile learning and microlearning;
- Smart cities infrastructure for e-learning;
- Edge computing, IoT and 5G networks in e-learning;
- Usage of digital twins in e-learning;
- Immersive learning environments using augmented and virtual reality;
- Virtual labs and smart classrooms;
- Smart campuses;
- Lifelong learning for Climate change adoption
- Data analysis in e-learning – data mining, process mining and learning analytics;
- Formal methods in e-learning systems specification and verification.
We look forward to receiving your contributions.
Dr. Frano Škopljanac-Mačina
Dr. Mihaela Vranić
Prof. Dr. Damir Pintar
Dr. Ivona Zakarija
Dr. Ljiljana Seric
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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
- E-learning
- Artificial Intelligence
- Smart city
- Smart education
- Immersive education
- AR and VR in e-learning
- Virtual lab
- Digital twin
- Data mining
- Process mining
- Learning analytics
- Formal methods
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