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A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2026) | Viewed by 25346

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Editors

Prof. Dr. Xuesong Zhai

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Guest Editor

1. College of Education, Zhejiang University, Hangzhou 310058, China
2. School of Education, City University of Macau, Macau, China
Interests: AI and metaverse in education; educational technology; smart learning environments
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoming Zhai

E-Mail Website
Guest Editor

1. AI4STEM Education Center, University of Georgia, Athens, GA 30602, USA
2. National GENIUS Center, University of Georgia, Athens, GA 30602, USA
3. Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA 30602, USA
Interests: AI/machine learning-based innovative assessment practices in science; mobile learning in science; science teacher education and career motivation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As technology continues to evolve at an unprecedented rate, education has emerged as a critical arena for innovation and research. The advent of new technologies, particularly generative AI and biotechniques, has not only enhanced teaching and learning environments but has also highlighted the need for a deeper understanding and application of these tools in educational settings. A majority of the existing research tends to focus on theoretical aspects or is limited to small-scale, specific group experiments. This Special Issue aims to expand the discourse by exploring the practical application and integration of smart learning tools and environments in educational systems.

Smart learning, a concept that integrates intelligent technologies with educational processes, offers a promising avenue for enhancing teaching effectiveness and student outcomes. However, the challenge remains to effectively combine these technologies with instructional design and adapt them to the diverse characteristics of teachers and students.

Therefore, we invite submissions to this Special Issue on the “Applications of Smart Learning in Education”, which seeks to collect innovative research and case studies that demonstrate the effective use of smart educational tools and environments. We are particularly interested in contributions that address (but are not limited to) the following topics:

Applications of generative AI in developing personalized learning materials and environments;
integration of biotechnological innovations to enhance learning, such as biometric sensors for measuring student engagement and cognitive response;
exploration of biofeedback mechanisms in personalized education;
development and application of AI, VR, and AR in educational contexts;
tools for personalized learning experiences and differentiated instruction;
case studies on the seamless integration of technology in curriculum design;
research on pedagogical strategies that incorporate smart learning tools;
studies on how smart technologies affect teacher roles and teaching styles;
analysis of student engagement and performance in tech-enhanced learning environments;
examination of the challenges and successes in adopting smart learning at scale;
insights into the long-term impacts of smart learning tools across various educational levels;
empirical research measuring the effectiveness of smart learning environments;
comparative studies highlighting traditional vs. smart learning outcomes.

Through this Special Issue, we aim to highlight the transformative potential and practical applications of smart learning technologies in education. We encourage practitioners, researchers, and policymakers from diverse backgrounds to contribute their findings and insights. Together, we can forge pathways that enhance educational practices and prepare the future generations for a rapidly evolving technological landscape.

Dr. Xuesong Zhai
Dr. Xiaoming Zhai
Guest Editors

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Keywords

smart learning
pedagogical design
application of learning technology

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Published Papers (6 papers)

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Research

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18 pages, 1404 KB

Open AccessArticle

Artificial Intelligence-Supported Solfège Instruction in Higher Music Education: Effects on Student Performance and Learning Attitudes

by Bilge Atay Karlıdağ, Tülün Malkoç and Seval Eminoğlu Küçüktepe

Appl. Sci. 2026, 16(11), 5383; https://doi.org/10.3390/app16115383 - 28 May 2026

Viewed by 603

Abstract

Background: Artificial intelligence (AI)-supported learning environments are increasingly used in music education; however, evidence regarding their effectiveness in solfège instruction remains limited. This action research study evaluated the effects of AI-supported solfège instruction on undergraduate students’ attitudes and performance. Materials and Methods: This action research study included 36 undergraduate students enrolled in a conservatory program. A 10-week AI-supported solfège training was implemented using the EarMaster intelligent tutoring system. Data were collected through a solfège attitude scale and a performance test administered before and after the intervention, along with a delayed retention test. Results: Following the intervention, significant improvements were observed in both attitude scores (104.6 vs. 117.3, p < 0.001) and performance scores (32.8 vs. 51.52, p < 0.001). However, retention test scores showed a significant decline after a no-practice period (51.5 vs. 48.8, p < 0.001). Qualitative findings indicated increased motivation, engagement, and individualized learning opportunities. Conclusions: AI-supported solfège instruction improved students’ performance and learning attitudes in higher music education. The findings suggest that adaptive feedback, individualized practice, and continuous engagement may contribute positively to auditory skill development and student motivation. However, sustained practice remains necessary for long-term retention. Artificial intelligence-supported systems should therefore be integrated as complementary tools alongside teacher-guided instruction to support more flexible and personalized learning environments. Full article

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

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25 pages, 2504 KB

Open AccessArticle

Teaching Strategies and Methods in a Complex Education Process: Use Case of Multi-Level Computer-Assisted Exercises on Constructive Simulation Systems

by Miro Čolić and Mirko Sužnjević

Appl. Sci. 2026, 16(8), 3692; https://doi.org/10.3390/app16083692 - 9 Apr 2026

Viewed by 417

Abstract

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy. Full article

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

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23 pages, 1592 KB

Open AccessArticle

Smart Learning with Generative AI Tools in Higher Education: An Integrated SOR–SDT Model of Student Creative Confidence and Engagement

by Yang Huang, Tao Yu, Yihui Chen, Yihuan Tian and Jinho Yim

Appl. Sci. 2026, 16(1), 63; https://doi.org/10.3390/app16010063 - 20 Dec 2025

Cited by 2 | Viewed by 2460

Abstract

We investigate how generative AI tools function in smart learning by estimating a structural path model that combines the Stimulus–Organism–Response (SOR) framework with Self-Determination Theory (SDT). Using survey data from N = 540 university students and covariance-based SEM, we examine whether perceptions of these tools—usefulness (PU), ease of use (PEU), creative benefit (PCB), and personalization (PP)—align with SDT’s motivational states of perceived autonomy (PA) and perceived competence (PC) and, in turn, relate to creative confidence (CC) and creative engagement (CE). All four perceptions show positive links to PA and PC, with PP exhibiting the largest association with PA. PA precedes PC, indicating a sequential motivational route. At the behavioral level, PC relates more strongly to CC, whereas PA shows a comparatively larger association with CE. In aggregate, the results support integrating SOR with SDT to explain students’ psychological responses to generative AI tools and inform course designs that cultivate autonomy and competence to sustain creative confidence and engagement in smart-learning contexts. Full article

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

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24 pages, 1258 KB

Open AccessArticle

Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing

by Ahmet Hakan İnce and Serkan Özbay

Appl. Sci. 2025, 15(13), 6999; https://doi.org/10.3390/app15136999 - 21 Jun 2025

Cited by 3 | Viewed by 4226

Abstract

This study investigates the impact of response time on ability estimation within an Item Response Theory (IRT) framework, introducing time-sensitive formulations to enhance student assessment accuracy. Seven models were evaluated, including standard 1PL-IRT and six response-time-adjusted variants: TP-IRT, STP-IRT, TWD-IRT, NRT-IRT, DTA-IRT, and ART-IRT. Three optimization techniques—Maximum Likelihood Estimation (MLE), full parameter optimization, and K-fold Cross-Validation (CV)—were employed to assess model performance. Empirical validation was conducted using data from 150 students solving 30 mathematics items on the “TestYourself” platform, integrating response accuracy and timing metrics. Student abilities (θ), item difficulties (b), and time–effect parameters (λ) were estimated using the L-BFGS-B algorithm to ensure numerical stability. The results indicate that subtractive models, particularly DTA-IRT, achieved the lowest AIC/BIC values, highest AUC, and improved parameter stability, confirming their effectiveness in penalizing excessive response times without disproportionately affecting moderate-speed students. In contrast, multiplicative models (TWD-IRT, ART-IRT) exhibited higher variability, weaker generalizability, and increased instability, raising concerns about their applicability in adaptive testing. K-fold CV further validated the robustness of subtractive models, emphasizing their suitability for real-world assessments. These findings highlight the importance of incorporating response time as an additive factor to improve ability estimation while maintaining fairness and interpretability. Future research should explore multidimensional IRT extensions, behavioral response–time analysis, and adaptive testing environments that dynamically adjust item difficulty based on response behavior. Full article

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

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23 pages, 6308 KB

Open AccessArticle

How Generative AI Enables an Online Project-Based Learning Platform: An Applied Study of Learning Behavior Analysis in Undergraduate Students

by Yi Dai, Jia-Ying Xiao, Yizhe Huang, Xuesong Zhai, Fan-Chun Wai and Ming Zhang

Appl. Sci. 2025, 15(5), 2369; https://doi.org/10.3390/app15052369 - 22 Feb 2025

Cited by 20 | Viewed by 10351

Abstract

Using Generative Artificial Intelligence (GAI) in education has opened new avenues for innovation, yet its role as an interactive tool with learners remains underexplored. Research in this domain faces challenges from pedagogical complexities and the variability of AI tools. To address these gaps, this study developed an online project-based learning (PBL) platform incorporating a GAI plug-in and conducted a year-long experiment to analyze its impact. Three sets of experimental analyses were performed to examine learners’ methods, cognitive processes, and learning effectiveness. The findings reveal that GAI significantly influenced students’ learning approaches, cognitive engagement, and learning effectiveness. Additionally, the study demonstrates that PBL offers an effective framework for investigating the educational implications of GAI, providing new insights for future research in this evolving field. Full article

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

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Review

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24 pages, 1861 KB

Open AccessReview

The Extended Education 4.0: Lifelong Learning in Times of Artificial Intelligence

by Jefferson Arias, José Isaias Salas, Andrés Chiappe and Fabiola Sáez Delgado

Appl. Sci. 2025, 15(17), 9352; https://doi.org/10.3390/app15179352 - 26 Aug 2025

Cited by 5 | Viewed by 4494

Abstract

Lifelong learning has become a central axis in the debate on education and innovation, especially in contexts where technological transformations and the integration of artificial intelligence are reshaping the ways individuals acquire, update, and apply knowledge. Despite the growing relevance of this field, research on lifelong learning remains dispersed across different perspectives, highlighting conceptual diversity and methodological fragmentation. This article presents a systematic review aimed at identifying how lifelong learning has been studied in relation to artificial intelligence, focusing on definitions, benefits, and limitations discussed in the literature. The review followed a rigorous methodological process, including a probabilistic sampling strategy, systematic screening and eligibility assessment, and the application of both qualitative and quantitative analyses supported by triangulation to ensure reliability. The findings indicate that research on lifelong learning in relation to artificial intelligence remains fragmented. While many studies emphasize conceptual definitions and highlight potential benefits, relatively few examine limitations, challenges, or empirical evidence of impact. By systematically synthesizing and analyzing the available literature, this review contributes to a more integrated understanding of how AI is shaping lifelong learning, offering both theoretical insights and practical implications for educational practice and policy. Full article

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

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