Explainable AI in Education

Topic Information

Dear Colleagues,

This Topic will focus on the development and implementation of explainable artificial intelligence (XAI) techniques within the education sector. This Topic will highlight how XAI can enhance transparency, accountability, and trust in educational technologies. We invite contributions that explore the technical aspects of creating and applying XAI systems for personalized learning, curriculum development, and student assessment. We are particularly interested in research that addresses the technical challenges of integrating XAI in diverse educational settings and studies that illustrate how explainability in AI contributes to more effective educational outcomes. Submissions may include case studies, empirical research, theoretical analyses, or innovative methodologies. This Topic aims to equip educators, developers, and researchers with a deeper understanding of XAI’s role in advancing education, emphasizing the technical foundations and implications of these systems in academic environments. Topics include, but are not limited to, the following:

• XAI for Real-Time Student Feedback
• The Effectiveness of XAI in Personalized Learning Environments
• Technological and Methodological Advances in XAI for Educational Assessments
• XAI for Curriculum Optimization
• XAI in Teacher Decision-Making
• Bias Detection and Mitigation in Educational XAI Applications
• Cross-Cultural Implementations of XAI in Education
• Integration Challenges of XAI in Legacy Educational Systems
• Theoretical Models for Understanding XAI in Education
• Transparent AI Systems for Education

Technical Program Committee Member:
Charanya Ramakrishnan, Macquarie University

Dr. Guanfeng Liu
Dr. Karina Luzia
Dr. Luke Bozzetto
Dr. Tommy Yuan
Prof. Dr. Pengpeng Zhao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Education Sciences education	2.6	5.5	2011	26.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	20.9 Days	CHF 1800	Submit

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Published Papers (8 papers)

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28 pages, 3558 KB  

Open AccessSystematic Review

AI-Based Academic Advising Across the Student Lifecycle: A Systematic Literature Review

by Ilyas Alloug, Mohamed Daoudi and Ilham Oumaira

Information 2026, 17(4), 335; https://doi.org/10.3390/info17040335 - 1 Apr 2026

Viewed by 936

Abstract

Academic advising is fundamental to student success, yet the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming the delivery of academic support. While predictive models and Recommendation Systems (RS) are becoming more accessible, the existing literature remains fragmented [...] Read more.

Academic advising is fundamental to student success, yet the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming the delivery of academic support. While predictive models and Recommendation Systems (RS) are becoming more accessible, the existing literature remains fragmented across diverse technical architectures and institutional objectives, preventing a clear understanding of the field’s evolution. In view of this, we present a Systematic Literature Review (SLR) of AI-driven academic advising, adhering to the PRISMA 2020 framework. We analyzed 27 peer-reviewed studies published between 2018 and 2025 to synthesize methodological trends and functional applications. Our findings reveal that while most systems prioritize pathway recommendations via classical ML or hybrid architectures, Early-Warning Systems (EWS) remain anchored in predictive classification. Furthermore, a nascent shift toward Generative AI (GenAI) indicates a move toward more interactive advising, though transparency and evaluation standards remain inconsistent. This review identifies a critical tension between algorithmic performance and institutional interpretability. We conclude by proposing a research agenda that emphasizes the need for cross-context validation and the development of socio-technical frameworks that integrate AI into existing higher education management structures. Full article

(This article belongs to the Topic Explainable AI in Education)

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22 pages, 3647 KB  

Open AccessArticle

Addressing Class Imbalance in Predicting Student Performance Using SMOTE and GAN Techniques

by Fatema Mohammad Alnassar, Tim Blackwell, Elaheh Homayounvala and Matthew Yee-king

Appl. Sci. 2026, 16(7), 3274; https://doi.org/10.3390/app16073274 - 28 Mar 2026

Viewed by 713

Abstract

Virtual Learning Environments (VLEs) have become increasingly popular in education, particularly with the rise of remote learning during the COVID-19 pandemic. Assessing student performance in VLEs is challenging, and the accurate prediction of final results is of great interest to educational institutions. Machine [...] Read more.

Virtual Learning Environments (VLEs) have become increasingly popular in education, particularly with the rise of remote learning during the COVID-19 pandemic. Assessing student performance in VLEs is challenging, and the accurate prediction of final results is of great interest to educational institutions. Machine learning classification models have been shown to be effective in predicting student performance, but the accuracy of these models depends on the dataset’s size, diversity, quality, and feature type. Class imbalance is a common issue in educational datasets, but there is a lack of research on addressing this problem in predicting student performance. In this paper, we present an experimental design that addresses class imbalance in predicting student performance by using the Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Network (GAN) technique. We compared the classification performance of seven machine learning models (i.e., Multi-Layer Perceptron (MLP), Decision Trees (DT), Random Forests (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CATBoost), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC)) using different dataset combinations, and our results show that SMOTE techniques can improve model performance, and GAN models can generate useful simulated data for classification tasks. Among the SMOTE resampling methods, SMOTE NN produced the strongest performance for the RF model, achieving a Region of Convergence (ROC) Area Under the Curve (AUC) of 0.96 and a Type II error rate of 8%. For the generative data experiments, the XGBoost model demonstrated the best performance when trained on the GAN-generated dataset balanced using SMOTE NN, attaining a ROC AUC of 0.97 and a reduced Type II error rate of 3%. These results indicate that the combined use of class balancing techniques and generative synthetic data augmentation can enhance student outcome prediction performance. Full article

(This article belongs to the Topic Explainable AI in Education)

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15 pages, 1495 KB  

Open AccessPerspective

Artificial Intelligence in Higher Education: A Global Statistical Synthesis for Policy and Quality Assurance Reform

by Rima J. Isaifan

Educ. Sci. 2026, 16(3), 483; https://doi.org/10.3390/educsci16030483 - 20 Mar 2026

Viewed by 1891

Abstract

Artificial intelligence has transitioned from a peripheral innovation to a core infrastructure shaping higher education within a remarkably short period. While conceptual debates on AI ethics, pedagogy, and academic integrity are expanding, empirically grounded syntheses that consolidate global evidence remain limited. This study [...] Read more.

Artificial intelligence has transitioned from a peripheral innovation to a core infrastructure shaping higher education within a remarkably short period. While conceptual debates on AI ethics, pedagogy, and academic integrity are expanding, empirically grounded syntheses that consolidate global evidence remain limited. This study addresses this gap by providing an integrated cross-domain synthesis and statistically grounded overview of AI adoption, use, and governance across higher education systems. Using a secondary statistical synthesis methodology, the study aggregates large-scale quantitative data published between 2021 and 2025 from reputable international sources, including student and faculty surveys, institutional reports, research indices, and regulatory datasets. Results demonstrate near-universal student adoption of AI tools, rapid but uneven professional engagement among faculty and staff, a sharp rise in AI-related academic misconduct, accelerating impacts on research production and scientific workflows, and persistent gaps in institutional preparedness, policy development, and equity. The findings reveal a widening disconnect between bottom-up AI adoption and top-down governance mechanisms, particularly in assessment design, academic integrity frameworks, faculty capacity building, and quality assurance systems. Moreover, this paper argues that AI can no longer be treated as an optional educational technology and must instead be governed as a foundational component of higher education infrastructure. The study concludes by outlining evidence-based policy implications for institutions, regulators, and quality assurance agencies, emphasizing the need for coordinated, adaptive, and equity-oriented governance frameworks grounded in empirical realities rather than speculative narratives. Full article

(This article belongs to the Topic Explainable AI in Education)

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29 pages, 1545 KB  

Open AccessArticle

Hierarchical Aggregation of Local Explanations for Student Adaptability

by Leonard Chukwualuka Nnadi and Yutaka Watanobe

Appl. Sci. 2026, 16(1), 333; https://doi.org/10.3390/app16010333 - 29 Dec 2025

Cited by 2 | Viewed by 660

Abstract

In this study, we present Hierarchical Local Interpretable Model-agnostic Explanations (H-LIME), an innovative extension of the LIME technique that provides interpretable machine learning insights across multiple levels of data hierarchy. While traditional local explanation methods focus on instance-level attributions, they often overlook systemic [...] Read more.

In this study, we present Hierarchical Local Interpretable Model-agnostic Explanations (H-LIME), an innovative extension of the LIME technique that provides interpretable machine learning insights across multiple levels of data hierarchy. While traditional local explanation methods focus on instance-level attributions, they often overlook systemic patterns embedded within educational structures. To address this limitation, H-LIME aggregates local explanations across hierarchical layers, Institution Type, Location, and Educational Level, thereby linking individual predictions to broader, policy-relevant trends. We evaluate H-LIME on a student adaptability dataset using a Random Forest model chosen for its superior explanation stability (approximately 4.5 times more stable than Decision Trees). The framework uncovers consistent global predictors of adaptability, such as education level and class duration, while revealing subgroup-specific factors, including network type and financial condition, whose influence varies across hierarchical contexts. This work demonstrates the effectiveness of H-LIME at uncovering multi-level patterns in educational data and its potential for supporting targeted interventions, strategic planning, and evidence-based decision-making. Beyond education, the hierarchical approach offers a scalable solution for enhancing interpretability in domains where structured data relationships are essential. Full article

(This article belongs to the Topic Explainable AI in Education)

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27 pages, 357 KB  

Open AccessArticle

Ethical and Responsible AI in Education: Situated Ethics for Democratic Learning

by Sandra Hummel

Educ. Sci. 2025, 15(12), 1594; https://doi.org/10.3390/educsci15121594 - 26 Nov 2025

Cited by 4 | Viewed by 2436

Abstract

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in [...] Read more.

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education. Full article

(This article belongs to the Topic Explainable AI in Education)

21 pages, 1133 KB  

Open AccessArticle

Research on China’s Innovative Cybersecurity Education System Oriented Toward Engineering Education Accreditation

by Yimei Yang, Jinping Liu and Yujun Yang

Information 2025, 16(8), 645; https://doi.org/10.3390/info16080645 - 29 Jul 2025

Cited by 1 | Viewed by 2476

Abstract

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical [...] Read more.

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education. Full article

(This article belongs to the Topic Explainable AI in Education)

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17 pages, 430 KB  

Open AccessArticle

Adoption and Impact of ChatGPT in Computer Science Education: A Case Study on a Database Administration Course

by Daniel López-Fernández and Ricardo Vergaz

AI 2024, 5(4), 2321-2337; https://doi.org/10.3390/ai5040114 - 11 Nov 2024

Cited by 7 | Viewed by 4129

Abstract

The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This contribution presents an exploratory and correlational [...] Read more.

The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This contribution presents an exploratory and correlational study conducted with 37 computer science students who used ChatGPT as a support tool to learn database administration. The article addresses three questions: The first one explores the degree of use of ChatGPT among computer science students to learn database administration, the second one explores the profile of students who get the most out of tools like ChatGPT to deal with database administration activities, and the third one explores how the utilization of ChatGPT can impact in academic performance. To empirically shed light on these questions the student’s grades and a comprehensive questionnaire were employed as research instruments. The obtained results indicate that traditional learning resources, such as teacher’s explanations and student’s reports, were widely used and correlated positively with student’s grades. The usage and perceived utility of ChatGPT were moderate, but positive correlations between students’ grades and ChatGPT usage were found. Indeed, a significantly higher use of this tool was identified among the group of outstanding students. This indicate that high-performing students are the ones who are using ChatGPT the most. So, a new digital trench could be rising between these students and those with a lower degree of fundamentals and worse prompting skills, who may not take advantage of all the ChatGPT possibilities. Full article

(This article belongs to the Topic Explainable AI in Education)

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31 pages, 6312 KB  

Open AccessArticle

An Explainable Student Performance Prediction Method Based on Dual-Level Progressive Classification Belief Rule Base

by Jiahao Mai, Fanxu Wei, Wei He, Haolan Huang and Hailong Zhu

Electronics 2024, 13(22), 4358; https://doi.org/10.3390/electronics13224358 - 6 Nov 2024

Cited by 7 | Viewed by 3626

Abstract

Explainable artificial intelligence (XAI) is crucial in education for making educational technologies more transparent and trustworthy. In the domain of student performance prediction, both the results and the processes need to be recognized by experts, making the requirement for explainability very high. The [...] Read more.

Explainable artificial intelligence (XAI) is crucial in education for making educational technologies more transparent and trustworthy. In the domain of student performance prediction, both the results and the processes need to be recognized by experts, making the requirement for explainability very high. The belief rule base (BRB) is a hybrid-driven method for modeling complex systems that integrates expert knowledge with transparent reasoning processes, thus providing good explainability. However, class imbalances in student grades often lead models to ignore minority samples, resulting in inaccurate assessments. Additionally, BRB models face the challenge of losing explainability during the optimization process. Therefore, an explainable student performance prediction method based on dual-level progressive classification BRB (DLBRB-i) has been proposed. Principal component regression (PCR) is used to select key features, and models are constructed based on selected metrics. The BRB’s first layer classifies data broadly, while the second layer refines these classifications for accuracy. By incorporating explainability constraints into the population-based covariance matrix adaptation evolution strategy (P-CMA-ES) optimization process, the explainability of the model is ensured effectively. Finally, empirical analysis using real datasets validates the diagnostic accuracy and explainability of the DLBRB-i model. Full article

(This article belongs to the Topic Explainable AI in Education)

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