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Applied Sciences
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Emerging Trends in Artificial Intelligence and Computer Science for E-Learning
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Emerging Trends in Artificial Intelligence and Computer Science for E-Learning

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1870

Special Issue Editors

Prof. Dr. Sacha Gómez Moñivas

E-Mail Website
Guest Editor
Department of Computer Engineering, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
Interests: applied computing; learning systems; gamification in learning systems; intelligent tutoring systems
Dr. Beatriz Narbona

E-Mail Website
Guest Editor Assistant
Department of Computer Engineering, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
Interests: applied linguistics; AI for teaching-learning; machine translation

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue “Emerging Trends in Artificial Intelligence and Computer Science for E-Learning”. This Special Issue explores the role of advanced artificial intelligence and computer science techniques for distance learning, covering a wide range of topics—from autonomous student learning to assessment methodologies.

This Special Issue welcomes contributions from diverse academic communities, including education and computer science, engaging all stakeholders involved in these fields.

Suggested themes and article types for submissions:

We invite original research articles and review papers on topics including, but not limited to:

  1. Generative artificial intelligence as a tool for self-regulated learning;
  2. AI-driven support for teachers in and beyond the classroom;
  3. Enhancing assessment through advanced AI systems;
  4. The impact of the new AI methodologies on learning processes;
  5. Advanced numerical methods and learning analytics.

We look forward to receiving your valuable contributions.

Best regards,

Prof. Dr. Sacha Gómez Moñivas
Guest Editor

Dr. Beatriz Narbona
Guest Editor Assistant

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

  • artificial intelligence
  • e-learning
  • learning systems

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

Published Papers (3 papers)

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Research

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18 pages, 620 KB  
Article
Expanding the Team: Integrating Generative Artificial Intelligence into the Assessment Development Process
by Toni A. May, Kathleen Provinzano, Kristin L. K. Koskey, Connor J. Sondergeld, Gregory E. Stone, James N. Archer and Naorah Rimkunas
Appl. Sci. 2025, 15(18), 9976; https://doi.org/10.3390/app15189976 - 11 Sep 2025
Abstract
Effective assessment development requires collaboration between multidisciplinary team members, and the process is often time-intensive. This study illustrates a framework for integrating generative artificial intelligence (GenAI) as a collaborator in assessment design, rather than a fully automated tool. The context was the development [...] Read more.
Effective assessment development requires collaboration between multidisciplinary team members, and the process is often time-intensive. This study illustrates a framework for integrating generative artificial intelligence (GenAI) as a collaborator in assessment design, rather than a fully automated tool. The context was the development of a 12-item multiple-choice test for social work interns in a school-based training program, guided by design-based research (DBR) principles. Using ChatGPT to generate draft items, psychometricians refined outputs through structured prompts and then convened a panel of five subject matter experts to evaluate content validity. Results showed that while most AI-assisted items were relevant, 75% required modification, with revisions focused on response option clarity, alignment with learning objectives, and item stems. These findings provide initial evidence that GenAI can serve as a productive collaborator in assessment development when embedded in a human-in-the-loop process, while underscoring the need for continued expert oversight and further validation research. Full article
(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)
17 pages, 2828 KB  
Article
Augmented Reality in Cardiovascular Education (HoloHeart): Assessment of Students’ and Lecturers’ Needs and Expectations at Heidelberg University Medical School
by Pascal Philipp Schlegel, Florian Kehrle, Till J. Bugaj, Eberhard Scholz, Alexander Kovacevic, Philippe Grieshaber, Ralph Nawrotzki, Joachim Kirsch, Markus Hecker, Anna L. Meyer, Katharina Seidensaal, Thuy D. Do, Jobst-Hendrik Schultz, Norbert Frey and Ann-Kathrin Rahm
Appl. Sci. 2025, 15(15), 8595; https://doi.org/10.3390/app15158595 - 2 Aug 2025
Viewed by 401
Abstract
Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger [...] Read more.
Background: A detailed understanding of cardiac anatomy and physiology is crucial in cardiovascular medicine. However, traditional learning methods often fall short in addressing this complexity. Augmented reality (AR) offers a promising tool to enhance comprehension. To assess its potential integration into the Heidelberger Curriculum Medicinale (HeiCuMed), we conducted a needs assessment among medical students and lecturers at Heidelberg University Medical School. Methods: Our survey aimed to evaluate the perceived benefits of AR-based learning compared to conventional methods and to gather expectations regarding an AR course in cardiovascular medicine. Using LimeSurvey, we developed a questionnaire to assess participants’ prior AR experience, preferred learning methods, and interest in a proposed AR-based, 2 × 90-min in-person course. Results: A total of 101 students and 27 lecturers participated. Support for AR in small-group teaching was strong: 96.3% of students and 90.9% of lecturers saw value in a dedicated AR course. Both groups favored its application in anatomy, cardiac surgery, and internal medicine. Students prioritized congenital heart defects, coronary anomalies, and arrhythmias, while lecturers also emphasized invasive valve interventions. Conclusions: There is significant interest in AR-based teaching in cardiovascular education, suggesting its potential to complement and improve traditional methods in medical curricula. Further studies are needed to assess the potential benefits regarding learning outcomes. Full article
(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)
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19 pages, 1523 KB  
Systematic Review
Implementation of Artificial Intelligence Technologies for the Assessment of Students’ Attentional State: A Scoping Review
by Rosabel Roig-Vila, Paz Prendes-Espinosa and Miguel Cazorla
Appl. Sci. 2025, 15(11), 5990; https://doi.org/10.3390/app15115990 - 26 May 2025
Viewed by 825
Abstract
Artificial intelligence (AI) has recently erupted into the field of education, offering novel opportunities, particularly in the analysis of student behaviour. There is a lack of knowledge on the use of AI in assessing attention; hence, a scoping review (ScR) is proposed. The [...] Read more.
Artificial intelligence (AI) has recently erupted into the field of education, offering novel opportunities, particularly in the analysis of student behaviour. There is a lack of knowledge on the use of AI in assessing attention; hence, a scoping review (ScR) is proposed. The aim is to explore and analyse the scientific literature related to such implementations in educational settings. We included empirical studies published in English between 2017 and 2023, focusing on the application of AI in formal learning environments. Theoretical reviews and studies conducted outside the field of education were excluded. The databases consulted were Scopus, Web of Science, and APA PsycInfo. The studies were selected by three independent reviewers using Rayyan, and the data were organised with predefined forms and analysed using VOSviewer. A total of 26 studies were identified. Research conducted in Asia (China) was predominant, although we found significant contributions from Europe and America. The methodological approaches were primarily experimental, focusing on mechanical observation and AI-based analytical techniques. The approaches adopted and the elements common to AI applications are discussed, highlighting implications for researchers, professionals and teachers. Full article
(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)
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