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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 11796

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Special Issue Editors

Prof. Dr. Sacha Gómez Moñivas

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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:

Generative artificial intelligence as a tool for self-regulated learning;
AI-driven support for teachers in and beyond the classroom;
Enhancing assessment through advanced AI systems;
The impact of the new AI methodologies on learning processes;
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 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-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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Published Papers (7 papers)

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Research

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28 pages, 4004 KB

Open AccessArticle

Application of Generative Artificial Intelligence for Innovative Teaching

by Nikola Kadoić, Jelena Gusić Munđar and Tena Jagačić

Appl. Sci. 2026, 16(8), 3699; https://doi.org/10.3390/app16083699 - 9 Apr 2026

Viewed by 350

Abstract

There are numerous ways in which generative artificial intelligence (GAI) can be applied in the teaching and learning process. This paper presents one application in the Business Decision Analysis (BDA) course. BDA is considered as the most challenging course in the Graduate Study Program in Economic Entrepreneurship at the University of Zagreb Faculty of Organisation and Informatics; consequently, the teachers continuously analyse possibilities to make the course more attractive for students. The innovative teaching activity at BDA was implemented as a betting shop during the first colloquium (which accounts for 50% of the overall grade). In the activity, GAI analysed learning management system (LMS) data of students’ results (attendance, self-assessment test results, logs in the system) of the initial (pre-course) test, as well as their results of the pub quiz (activity organised a week before the colloquium as a preparatory activity). GAI analysed all the data and predicted the number of points each student will achieve. Additionally, GAI calculated the risk index, average growth (among self-assessment tests) and learning consistency for each student. Finally, GAI created a message for each student that explained what went well in their learning activity, what could be improved, and included a motivational note for the test. The rule was: if a student achieved a higher result than the GAI predicted, the teacher would buy a chocolate for that student. More than 60% percent of students achieved a higher score than was predicted. Surprisingly, exceeding the expected result was not in correlation with the risk indices determined by the GAI. Cluster analysis identified four student profiles consistent with the correlation results, showing weak overall agreement between the predicted and achieved scores, except in the male subgroup, while higher predicted scores were associated with higher average growth and lower risk indices. Qualitative analysis of the GAI application in teaching yielded positive comments, as students perceived the activity as helpful, motivating, and engaging, and would have liked more similar activities. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)

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13 pages, 839 KB

Open AccessArticle

Use of Generative Artificial Intelligence by Final Degree Project Students: Is It Useful in All Steps of Their Work?

by María Elena Cuenca, Laia Subirats, Beatriz Narbona-Reina and Gómez-Moñivas Sacha

Appl. Sci. 2025, 15(20), 11004; https://doi.org/10.3390/app152011004 - 14 Oct 2025

Cited by 1 | Viewed by 1296

Abstract

This study examines the perceived utility of generative artificial intelligence (GenAI), particularly ChatGPT, in the development of final degree projects across diverse academic disciplines. Drawing on a mixed-methods design, the research involved eleven final-year undergraduate students who participated in structured sessions integrating GenAI tools into distinct project phases. Quantitative and qualitative data revealed heterogeneous perceptions of GenAI’s utility, with theoretical framework development rated most favorably and visual presentation tasks least useful. Disciplinary variations were pronounced; students from Chemical Engineering and Psychology reported higher engagement, while those in Philosophy and Computer Science expressed greater skepticism. To ensure methodological rigor, AI-driven linguistic analysis of oral discourse confirmed participant homogeneity in academic maturity, supporting the attribution of perceptual differences to disciplinary and task-specific variables rather than individual disparities. The findings underscore the need for context-sensitive integration of GenAI in higher education, balancing its potential as a cognitive amplifier with critical evaluation of its limitations. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)

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12 pages, 211 KB

Open AccessArticle

A Comparative Study of Large Language Models in Programming Education: Accuracy, Efficiency, and Feedback in Student Assignment Grading

by Andrija Bernik, Danijel Radošević and Andrej Čep

Appl. Sci. 2025, 15(18), 10055; https://doi.org/10.3390/app151810055 - 15 Sep 2025

Cited by 2 | Viewed by 3091

Abstract

Programming education traditionally requires extensive manual assessment of student assignments, which is both time-consuming and resource-intensive for instructors. Recent advances in large language models (LLMs) open opportunities for automating this process and providing timely feedback. This paper investigates the application of artificial intelligence (AI) tools for preliminary assessment of undergraduate programming assignments. A multi-phase experimental study was conducted across three computer science courses: Introduction to Programming, Programming 2, and Advanced Programming Concepts. A total of 315 Python assignments were collected from the Moodle learning management system, with 100 randomly selected submissions analyzed in detail. AI evaluation was performed using ChatGPT-4 (GPT-4-turbo), Claude 3, and Gemini 1.5 Pro models, employing structured prompts aligned with a predefined rubric that assessed functionality, code structure, documentation, and efficiency. Quantitative results demonstrate high correlation between AI-generated scores and instructor evaluations, with ChatGPT-4 achieving the highest consistency (Pearson coefficient 0.91) and the lowest average absolute deviation (0.68 points). Qualitative analysis highlights AI’s ability to provide structured, actionable feedback, though variability across models was observed. The study identifies benefits such as faster evaluation and enhanced feedback quality, alongside challenges including model limitations, potential biases, and the need for human oversight. Recommendations emphasize hybrid evaluation approaches combining AI automation with instructor supervision, ethical guidelines, and integration of AI tools into learning management systems. The findings indicate that AI-assisted grading can improve efficiency and pedagogical outcomes while maintaining academic integrity. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)

18 pages, 1151 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1760

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 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)

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17 pages, 2828 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1363

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 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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Review

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27 pages, 824 KB

Open AccessReview

The Architecture of AI-Mediated Learning: A Three-Layer Framework

by Arash Javadinejad and Maedeh Davari

Appl. Sci. 2026, 16(10), 4991; https://doi.org/10.3390/app16104991 (registering DOI) - 16 May 2026

Viewed by 112

Abstract

Artificial intelligence (or AI) is rapidly transforming digital learning environments, reshaping how educational processes are organized, how knowledge is produced, and how learning is evaluated. Despite a growing body of research on AI in education, existing studies often examine technological, pedagogical, and ethical dimensions in isolation, leaving a lack of integrative frameworks capable of explaining how AI restructures learning environments as a whole. This study addresses this gap by proposing a three-layer conceptual framework that models AI-mediated learning environments through the interaction of efficiency, pedagogy, and ideology. The framework conceptualizes AI integration as a system of interdependent processes: the efficiency layer captures the optimization of educational activities through automation and data-driven personalization; the pedagogical layer explains how AI reshapes learning processes, feedback cycles, and learner strategies; and the ideological layer examines the normative assumptions embedded within AI systems, including issues of epistemic authority, linguistic norms, and algorithmic bias. Drawing on a structured synthesis of recent empirical research across domains such as generative AI tools, automated feedback systems, intelligent tutoring systems, and AI-supported assessment, the study demonstrates how these dimensions interact to structure contemporary digital learning environments and generate both affordances and tensions. The main theoretical contribution lies in advancing a system-level analytical framework that moves beyond tool-specific approaches and enables a more integrated understanding of AI in education. In practical terms, the framework provides educators and policymakers with a lens to critically evaluate AI integration, supporting more informed decisions on assessment design, sustainable learning practices, and inclusive digital education. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence and Computer Science for E-Learning)

Other

Jump to: Research, Review

19 pages, 1523 KB

Open AccessSystematic 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

Cited by 1 | Viewed by 2117

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 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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