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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: closed (20 December 2025) | Viewed by 18275

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Prof. Dr. Huseyin Uzunboylu

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Guest Editor

Department of Special Education, University of Kyrenia, Kyrenia, Cyprus
Interests: educational technology; media education; learning theories; curriculum; special education; didactics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart learning is an approach that uses technology and data analytics to optimize the learning processes of learners and groups. This method adapts to the needs of learners by providing personalized learning experiences. The following methods and technologies are implemented across diverse interactive environments to enhance engagement and learning outcomes:

Personalized learning;
Use of analytical tools;
Blended learning;
Artificial Intelligence (AI)-supported learning;
Virtual reality (VR);
Augmented reality (AR);
Blended learning;
Online learning;
Mobile Learning.

This Special Issue includes smart learning applications in all application areas of education and educational sciences, as well as all smart and interactive research topics.

Prof. Dr. Huseyin Uzunboylu
Guest Editor

Manuscript Submission Information

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Keywords

smart learning
personalized learning
virtual reality (VR)
augmented reality (AR)
online learning
mobile Learning

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Published Papers (7 papers)

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Research

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27 pages, 1542 KB

Open AccessArticle

The Application of AI Chatbot System Based on CLIL Concept in the Teaching of Artificial Intelligence Courses

by Ziqi Liu and Qian Wang

Appl. Sci. 2026, 16(3), 1633; https://doi.org/10.3390/app16031633 - 5 Feb 2026

Viewed by 410

Abstract

The interdisciplinary nature of artificial intelligence courses forces non-computer science majors to contend with the simultaneous challenges of terminology comprehension and language cognition. To increase the efficiency of terminology teaching, this project develops and deploys an OpenAI-based AI chatbot teaching system that incorporates the concept of content and language integrated learning (CLIL). The system creates a dual-track “terminology layer-cognition layer” framework that includes term recognition, multi-level explanation (contextual examples and conceptual associations), task-driven dialogues, and conversation memory bank (CMB) modules. It then guides students through natural language interactions to master the core AI terms in context. The system’s effectiveness was confirmed in a controlled experiment with 98 participants (including computer and non-computer majors) separated into two groups: experimental (chatbot teaching) and control (conventional PPT teaching). In terms of terminology mastery, the experimental group’s posttest score (86.0 ± 5.33) was considerably higher than that of the control group (66.98 ± 5.6). Non-computer science major students showed a more significant improvement effect (83.29 ± 4.5 vs. 63.62 ± 4.68 for the control group). Non-computing students evaluated the clarity of systematic terminology explanation (4.33 ± 0.76) and the effectiveness of contextual assistance (4.21 ± 0.88) as the most important aspects of their learning experience. These experimental results show that the fusion AI chatbot teaching system developed in this study can improve teaching efficiency while effectively reducing cognitive load, and that the task-guided and immediate feedback mechanism can significantly increase students’ learning engagement. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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24 pages, 2163 KB

Open AccessArticle

KFF-Transformer: A Human–AI Collaborative Framework for Fine-Grained Argument Element Identification

by Xuxun Cai, Jincai Yang, Meng Zheng and Jianping Zhu

Appl. Sci. 2026, 16(3), 1451; https://doi.org/10.3390/app16031451 - 31 Jan 2026

Viewed by 428

Abstract

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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19 pages, 2610 KB

Open AccessArticle

Open HTML5 Widgets for Smart Learning: Enriching Educational 360° Virtual Tours and a Comparative Evaluation vs. H5P

by Félix Fariña-Rodriguez, Jose Luis Saorín, Dámari Melian Díaz, Jose Luis Saorín-Ferrer and Cecile Meier

Appl. Sci. 2026, 16(1), 338; https://doi.org/10.3390/app16010338 - 29 Dec 2025

Viewed by 486

Abstract

In educational smart learning contexts, 360° virtual tours deliver authentic, cross-device experiences, but uptake is limited by subscription-based authoring tools and free options that restrict in-tour rich media embedding. To address this, we present a library of eight open-source HTML5 widgets (image gallery, PDF viewer, quiz, 3D model viewer, webpage viewer, audio player, YouTube viewer, and image comparison) that can be embedded directly in the viewer as HTML pop-ups (e.g., CloudPano) or run standalone, with dual packaging (single self-contained HTML or server-hosted assets referenced by URL). Evaluation is limited to technical efficiency (resource size, load performance, and cross-device/browser compatibility), with pedagogical outcomes and learner performance beyond the scope. The architecture minimizes dependencies and enables reuse in virtual classrooms via iframes. We provide a unified web interface and a repository to promote adoption, auditability, and community contributions. The results show that standalone widgets are between 20 and 100 times smaller than H5P equivalents produced with Lumi Education and exhibit shorter measured load times (0.1–0.5 ms). Seamless integration is demonstrated for CloudPano and Moodle. By lowering costs, simplifying deployment, and broadening in-tour media capabilities, the proposed widgets offer a pragmatic pathway to enrich educational 360° tours. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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17 pages, 264 KB

Open AccessArticle

The Effect of a Mathematics Learning Disability Program Offered Face to Face with Interactive Online Learning from Smart Learning Environments on Teachers’ Knowledge and Self-Efficacy Levels

by Necmi Sağıroğlu, Hüseyin Uzunboylu, Gönül Akçamete and Mukaddes Sakallı Demirok

Appl. Sci. 2025, 15(10), 5326; https://doi.org/10.3390/app15105326 - 10 May 2025

Cited by 2 | Viewed by 1513

Abstract

This study examines the effectiveness of in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy in the context of learning disabilities (LD) in mathematics. Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research has compared their relative effectiveness in this specific field. Furthermore, existing studies have not adequately addressed whether improvements in teachers’ knowledge and self-efficacy are sustained over time. To address this gap, the present study employed a quasi-experimental design with two experimental groups. The sample consists of 80 classroom teachers, with 40 participants in the interactive online learning education group and 40 in the face-to-face education group. The training program consists of 16 h of instruction over four weeks. Data were collected using a demographic questionnaire and the Mathematics Learning Difficulty Area Teacher Self-Efficacy Scale, and statistical analyses were conducted. The findings indicate that, prior to the intervention, teachers in the interactive online learning education group exhibited significantly higher levels of knowledge and self-efficacy. However, the post-intervention results revealed no statistically significant differences between the two groups. Cohen’s d analysis indicated a moderate effect size for interactive online learning education before the intervention, which diminished to a small effect size afterward. This study validates the efficiency of interactive online learning from smart learning environments for in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy about learning disabilities in mathematics. These results suggest that both training modalities effectively improve teachers’ knowledge and self-efficacy, but neither demonstrate a clear long-term advantage. This study underscores the need for further research to determine optimal strategies for sustaining professional development in this domain. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

29 pages, 7061 KB

Open AccessArticle

Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners

by Fawad Naseer and Sarwar Khawaja

Appl. Sci. 2025, 15(8), 4473; https://doi.org/10.3390/app15084473 - 18 Apr 2025

Cited by 14 | Viewed by 4698

Abstract

Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adaptive feedback while the control group followed traditional instructor-led feedback methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, and survey responses. Results indicate that students receiving AI-driven adaptive feedback demonstrated a 28% improvement in conceptual mastery, compared to 14% in the control group. Additionally, student engagement increased by 35%, with a 22% reduction in cognitive overload. Analysis of interaction logs revealed that frequent engagement with AI-generated feedback led to a 40% increase in retention rates. Despite these benefits, variations in impact were observed based on prior knowledge levels and interaction consistency. The findings highlight the potential of AI-driven smart learning environments to enhance educational equity. Future research should explore long-term effects, scalability, and ethical considerations in adaptive AI-based learning systems. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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22 pages, 4593 KB

Open AccessArticle

Quality Management System in Shaping Students’ Pro-Quality Attitude in the Era of Industry 4.0

by Bartosz Spychalski

Appl. Sci. 2025, 15(8), 4227; https://doi.org/10.3390/app15084227 - 11 Apr 2025

Viewed by 2084

Abstract

The significance of the quality issue in Industry 4.0 increases due to the dynamically changing economy. Not only selected workers who form the quality department must be aware of this fact, but each member of the staff must be as well. A considerable portion of responsibility concerning the proper preparation of workers in the field of quality relies on the education system that should help graduates develop a pro-quality attitude. In order to fulfill this aim, schools must use a number of tools, including, among others, the ISO 9001 quality management system—one of the elements introduced by the author’s model of factors influencing the development of students’ pro-quality attitude. The subject of this article is to determine the impact of the quality management system implemented in schools on the development of students’ pro-quality attitude—an issue that allows us, on the one hand, to ensure a higher level of education and, on the other hand, to use it as an element of smart learning. For the needs of the performed research, the author has collected secondary data from the literature analysis, as well as primary data from surveys performed on 1294 people. The research results deserve the attention of people who manage schools to improve the use of the quality management system implemented in schools in order to develop students’ attitudes towards quality and to improve the system itself more effectively. They are also important for enterprises, which can positively influence young people’s (future participants of the labor market based on Industry 4.0) education through cooperation with schools. The research performed by the author proved the hypothesis according to which the implementation of the quality management system is not sufficient to ensure effective development of students’ pro-quality attitudes. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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22 pages, 713 KB

Open AccessSystematic Review

Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses

by Yan Zhang, Heng Luo, Shiqing Peng and Xue Han

Appl. Sci. 2025, 15(11), 6293; https://doi.org/10.3390/app15116293 - 3 Jun 2025

Cited by 1 | Viewed by 4735

Abstract

This study compares the effectiveness of virtual reality (VR) and videoconferencing (VC) platforms for online learning. Comparative meta-analyses of 29 articles from 2003 to 2023 (15 for VR, 14 for VC) revealed that both technologies positively affect learning outcomes, with VR demonstrating a larger effect size (ES = 0.913) compared with VC (ES = 0.284). VR proved more beneficial in regular-sized classes, especially for natural science subjects, and excelled in experiential or collaborative learning environments. VC showed a greater impact in smaller classes, with significant variations depending on the program brand and camera options; it also favored natural science. These findings provide valuable guidance for educators and learners in choosing the most suitable technology for different online learning scenarios and shed light on future research in this field. Full article

(This article belongs to the Special Issue Application of Smart Learning in Education)

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