Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd...
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd Edition

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1932

Special Issue Editors

Dr. Yu Liang

E-Mail Website
Guest Editor
College of Computer Science, Beijing University of Technology, Beijing 100124, China
Interests: artificial intelligence; smart education; educational data mining; human–computer interaction; cognitive modeling and intelligent tutoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenjun Wu

E-Mail Website
Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: swarm intelligence and multi-agent systems; cognitive modeling and intelligent tutoring; intelligent cloud services; intelligent software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Ying Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Interests: intelligent scheduling; embedded real-time system; distributed parallel computing; program analysis; smart education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue seeks to explore the application of artificial intelligence (AI) technologies within the field of education. The Special Issue will focus on various educational domains and aims to highlight the unique task areas and challenges that arise in applying AI in education, as well as the potential positive impacts it can have. Authors are encouraged to present advances in AI techniques and methodologies specifically tailored to educational tasks and domains. This Special Issue aims to showcase innovative approaches that leverage AI to enhance personalized learning experiences, facilitate adaptive assessment, leverage natural language processing for educational purposes, utilize machine learning and data mining techniques to analyze educational data, and explore the cognitive modeling of learners. Additionally, it will emphasize the importance of considering ethical considerations and human–computer interactions in the context of AI in education.

In this Special Issue, we invite the submission of research papers that delve into different aspects of AI in education, including the development of novel student models, the design and implementation of intelligent learning environments, the use of automated assistants to support learning processes, and the role of AI in providing instructional support to both educators and learners. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

We look forward to receiving your contributions.

Dr. Yu Liang
Prof. Dr. Wenjun Wu
Dr. Ying Li
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 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
  • educational data mining
  • educational technology, pedagogical strategies and instructional support
  • intelligent learning environments
  • student modeling and cognitive modeling
  • learning analytics and personalized learning
  • automated assistants and intelligent tutoring
  • adaptive assessment
  • human–computer interaction

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 1957 KB  
Article
GWO-Optimized Ensemble Learning for Interpretable and Accurate Prediction of Student Academic Performance in Smart Learning Environments
by Mohammed Husayn, Oluwatayomi Rereloluwa Adegboye and Ahmad Alzubi
Appl. Sci. 2025, 15(22), 12163; https://doi.org/10.3390/app152212163 - 16 Nov 2025
Viewed by 354
Abstract
Accurate and interpretable prediction of student academic performance is a cornerstone of data-driven educational support systems, enabling timely interventions, personalized learning pathways, and equitable resource allocation. While ensemble machine learning models such as Random Forest, Extra Trees, and CatBoost have shown promise in [...] Read more.
Accurate and interpretable prediction of student academic performance is a cornerstone of data-driven educational support systems, enabling timely interventions, personalized learning pathways, and equitable resource allocation. While ensemble machine learning models such as Random Forest, Extra Trees, and CatBoost have shown promise in educational data mining, their predictive power and generalizability are often limited by suboptimal weighting schemes and sensitivity to hyperparameter configurations. To address this, we propose a Grey Wolf Optimizer (GWO)-guided ensemble framework that dynamically optimizes each base regressor’s contribution to minimize prediction error while preserving model transparency. Evaluated on a real-world student performance dataset, the proposed approach achieves a coefficient of determination (R2) of 0.93, significantly outperforming individual and conventional ensemble baselines. Furthermore, we integrate SHAP (SHapley Additive exPlanations) to provide educator-friendly interpretability, revealing that daily study hours, study effectiveness, lifestyle score, and screen time are the most influential predictors of exam outcomes. By bridging an optimized machine learning model with educational analytics, this work delivers a robust, transparent, and high-performing AI solution tailored for intelligent tutoring systems, early-warning platforms, and adaptive learning environments. The methodology exemplifies how nature-inspired optimization can enhance not only accuracy but also actionable insight for stakeholders in smart education ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 676 KB  
Article
Node Classification of Imbalanced Data Using Ensemble Graph Neural Networks
by Yuan Liang
Appl. Sci. 2025, 15(19), 10440; https://doi.org/10.3390/app151910440 - 26 Sep 2025
Viewed by 1112
Abstract
In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed. Although existing graph neural network (GNN) models have achieved notable progress in classification tasks, their performance tends [...] Read more.
In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed. Although existing graph neural network (GNN) models have achieved notable progress in classification tasks, their performance tends to rely on relatively balanced data distributions. To tackle this challenge, we propose an ensemble graph neural network framework designed for imbalanced node classification. Specifically, we employ spectral-based graph convolutional neural networks as base classifiers and train multiple models in parallel. We then adopt a bagging ensemble strategy to integrate the predictions of these classifiers and determine the final classification results through majority voting. Furthermore, we extend this approach to fake review detection tasks. Extensive experiments conducted on imbalanced node classification datasets (Cora and BlogCatalog), as well as fake review detection (YelpChi), demonstrate that our method consistently outperforms state-of-the-art baselines, achieving significant gains in accuracy, AUC, and Macro-F1. Notably, on the Cora dataset, our model improves accuracy and Macro-F1 by 3.4% and 2.3%, respectively, while on the BlogCatalog dataset, it achieves improvements of 2.5%, 1.8%, and 0.5% in accuracy, AUC, and Macro-F1, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Education: Advancements, Challenges, and Impacts, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop