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Adaptive E-Learning Technologies and Experiences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 4236

Special Issue Editors


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Guest Editor
IT, Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Interests: NonSQL databases; analytics; e-learning; self-determined learning; eHealth; semantics
Special Issues, Collections and Topics in MDPI journals

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Special Issue Information

Dear Colleagues,

Adaptive learning is an innovative approach that focuses on personalizing the learning experience for each student, adapting to their individual needs and learning styles. Adaptive learning may use technologies, algorithms, and analytics to design learning experiences tailored to each student, which can significantly improve learning outcomes and student motivation. Even though, in recent years, adaptive learning has advanced due to new technologies and the increase in distance learning activity due to lockdown, the use of generative intelligence poses new opportunities while creating new issues related to its use within learning experiences.

For this Special Issue, we invite contributions that showcase the convergence of learning design with the implementation of innovative technologies to cultivate purposeful, inclusive, and personalized learning experiences. Furthermore, we encourage explorations into the symbiotic relationship between generative intelligence and e-learning, envisioning the transformative potential this alliance holds for the future of education.

We welcome submissions on a variety of topics, including, but not limited to, the following:

  • Experiences in adaptive learning;
  • Integration of machine learning algorithms into online learning platforms;
  • Impact of adaptive learning on student retention and achievement;
  • Challenges and limitations in the implementation of adaptive learning;
  • Evaluation and optimization of machine learning algorithms;
  • Impact on higher education;
  • Personalization of content and pace of learning;
  • Data analysis and real-time feedback;
  • Integration with other educational systems and tools;
  • Evaluation of effectiveness and efficiency;
  • Development of skills and competencies.

Authors are encouraged to submit their research articles, case studies, and insights on these topics to advance our understanding of how these technologies can shape the future of e-learning.

Dr. Jordi Conesa Caralt
Prof. Dr. Antonio Sarasa Cabezuelo
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. Applied Sciences 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

  • adaptive learning
  • e-learning
  • personalization of learning
  • self-determined learning
  • artificial intelligence
  • machine learning
  • real-time feedback
  • analysis of data
  • learning algorithms
  • learning analytics
  • online learning platforms
  • skill development
  • artificial intelligence
  • retrieval augmented generation

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Published Papers (2 papers)

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Research

20 pages, 889 KiB  
Article
Framework Design for Reinforcing the Potential of XR Technologies in Transforming Inclusive Education
by Monica Barbu, Dragos-Daniel Iordache, Ionuț Petre, Dragos-Cătălin Barbu and Lidia Băjenaru
Appl. Sci. 2025, 15(3), 1484; https://doi.org/10.3390/app15031484 - 31 Jan 2025
Viewed by 1123
Abstract
This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR [...] Read more.
This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR and generative AI to facilitate personalized, accessible, and interactive learning experiences tailored to individual requirements. The framework incorporates an intuitive Unity XR-based interface alongside a generative AI module to enable near real-time customization of content and interactions. Additionally, the study examines related generative AI initiatives that promote inclusion through enhanced communication tools, educational support, and customizable assistive technologies. The motivation for this study arises from the pressing need to address the limitations of traditional educational methods, which often fail to meet the diverse needs of learners with special educational requirements. The integration of XR and generative AI offers transformative potential by creating adaptive, immersive, and inclusive learning environments. This approach ensures real-time adaptability to individual progress and accessibility, addressing critical barriers such as static content and lack of inclusivity in existing systems. The research outlines a pathway toward more inclusive and equitable education, significantly enhancing opportunities for learners with diverse needs and contributing to broader social integration and equity in education. Full article
(This article belongs to the Special Issue Adaptive E-Learning Technologies and Experiences)
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21 pages, 721 KiB  
Article
Evaluating Post-Pandemic Undergraduate Student Satisfaction with Online Learning in Saudi Arabia: The Significance of Self-Directed Learning
by Sahar Alshathry and Mohammed Alojail
Appl. Sci. 2024, 14(19), 8889; https://doi.org/10.3390/app14198889 - 2 Oct 2024
Viewed by 2422
Abstract
Presently, numerous universities in Saudi Arabia have embraced online learning following the experience gained during the COVID-19 pandemic. While several studies have assessed the quality of online learning and student satisfaction during that period, limited research has explored students’ satisfaction post-pandemic control despite [...] Read more.
Presently, numerous universities in Saudi Arabia have embraced online learning following the experience gained during the COVID-19 pandemic. While several studies have assessed the quality of online learning and student satisfaction during that period, limited research has explored students’ satisfaction post-pandemic control despite several universities planning to offer some courses online. Investigating student satisfaction post-pandemic is crucial for predicting the quality of online learning and assisting universities in enhancing the outcomes of online learning. Consequently, this study aims to examine student satisfaction with online learning by proposing a model derived from the updated Information System Success Model. The updated model factors include system quality, service quality, and information quality, supplemented by student–student interaction quality and self-directed learning. Data were collected from a sample of 150 undergraduates at King Saud University in the second semester of the 2023–2024 academic year. The research model was validated using the PLS approach. The findings indicated that only self-directed learning significantly affects students’ satisfaction with online learning. This study contributes theoretically by expanding the existing literature and enhancing the understanding of the factors that drive student satisfaction. Moreover, it provides practical contributions to decision-makers and educators developing online learning strategies focusing on enhancing self-directed learning abilities. Full article
(This article belongs to the Special Issue Adaptive E-Learning Technologies and Experiences)
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