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Education Sciences
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Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions
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Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 73114

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Will W. K. Ma

E-Mail Website
Guest Editor
Centre for Innovative Teaching and Learning, Tung Wah College, Hong Kong
Interests: artificial intelligence; blended learning; information systems; higher education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers on the theme of "Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions". This Special Issue aims to explore the intersection between artificial intelligence (AI) and blended learning, with a focus on innovative research and practical applications that enhance the effectiveness and efficiency of blended learning environments.

Blended learning, combining traditional face-to-face instruction with online or digital components, has become increasingly prevalent in educational settings. AI, with its capabilities in data analysis, machine learning and natural language processing, holds great promise for transforming the landscape of blended learning. It offers opportunities to personalize instruction, provide intelligent tutoring systems, automate assessment processes and create immersive learning experiences.

We invite researchers and practitioners from diverse disciplines to contribute original research papers, case studies and theoretical perspectives that shed light on the potential of AI in enhancing blended learning. The topics of interest include, but are not limited to:

  1. Adaptive learning platforms and personalized instruction in blended learning;
  2. Intelligent tutoring systems and their application in blended learning environments;
  3. Natural language processing for language learning in blended settings;
  4. Data analytics and learning analytics in blended learning research;
  5. Automated grading and feedback mechanisms in blended learning assessments;
  6. Virtual reality (VR) and augmented reality (AR) in blended learning;
  7. Intelligent content recommendation systems for blended learning;
  8. Predictive modeling and personalized learning paths in blended learning.

We encourage contributors to explore theoretical frameworks, present empirical studies, and share practical insights on the integration of AI in blended learning. Submissions should demonstrate a clear research methodology, present novel findings, and offer implications for both research and practice.

Accepted papers will be published in a Special Issue of Education Sciences, providing a platform for scholars to disseminate their work to a global audience. This Special Issue will contribute to the advancement of knowledge in the field of blended learning and AI, fostering collaboration and innovation.

Please refer to our journal's website for detailed submission guidelines. We look forward to receiving your contributions and collectively advancing the frontiers of AI in blended learning research and applications

Prof. Dr. Will W. K. Ma
Guest Editor

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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly 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 1800 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 (AI)
  • blended learning
  • information systems
  • data analytics
  • adaptive learning
  • personalized learning
  • assessments and feedback
  • natural language processing

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

Published Papers (8 papers)

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Research

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20 pages, 641 KB  
Article
An Integrated Individual, Social, and Technology Model for the Sustainable Adoption of Generative AI in Blended Learning
by Will W. K. Ma
Educ. Sci. 2026, 16(1), 128; https://doi.org/10.3390/educsci16010128 - 14 Jan 2026
Viewed by 539
Abstract
Generative AI is a promising adjunct to blended learning, offering an innovative means to enhance academic performance. Its rapid diffusion has been accompanied by criticism and uncertainty, particularly regarding ethics and the potential displacement of human labor. A review of the existing research [...] Read more.
Generative AI is a promising adjunct to blended learning, offering an innovative means to enhance academic performance. Its rapid diffusion has been accompanied by criticism and uncertainty, particularly regarding ethics and the potential displacement of human labor. A review of the existing research reveals persistent gaps in understanding AI use among students. This study therefore aimed to develop an integrated model to explain generative AI adoption across two distinctive time points. Employing a survey-based design, cross-sectional data were collected at two time points from college students at a local tertiary institution in Hong Kong. PLS-SEM Model testing showed that performance expectancy was the strongest and most persistent determinant of both intention to use and actual use across both data collections. Risk propensity had no effect at the outset, but at a longer usage time point, it was significantly related to intention and use through performance expectancy. Social influence exerted a direct and significant effect initially and later demonstrated both direct and indirect significant effects on intention and use via performance expectancy. The findings identify key determinants and enhance our understanding of the complex decision-making process involved in the use of generative AI. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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15 pages, 259 KB  
Article
Evaluating the Relationship Between Pre-Service Teachers’ Artificial Intelligence Readiness and Professional Self-Efficacy
by Kuralay Baimukhambetova, Kalibek Ybyraimzhanov, Kulakhmet Moldabek, Ulsana Borashkyzy Akhatayeva, Aliya Zhetkizgenova and Elmira Uaidullakyzy
Educ. Sci. 2026, 16(1), 43; https://doi.org/10.3390/educsci16010043 - 30 Dec 2025
Cited by 2 | Viewed by 1361
Abstract
The rapid development of educational technologies requires a deeper understanding of pre-service teachers’ readiness for artificial Intelligence and the extent to which their professional self-efficacy beliefs influence this process. Although the integration of emerging technologies has gained increasing attention, the relationship between technological [...] Read more.
The rapid development of educational technologies requires a deeper understanding of pre-service teachers’ readiness for artificial Intelligence and the extent to which their professional self-efficacy beliefs influence this process. Although the integration of emerging technologies has gained increasing attention, the relationship between technological competence and professional confidence among pre-service teachers remains underexplored. This study aims to investigate the interplay between pre-service teachers’ readiness for artificial intelligence and their professional self-efficacy. An exploration sequential mixed method design was employed, beginning with a quantitative phase involving 293 pre-service teachers, followed by a qualitative phase to capture deeper insights. Findings revealed that pre-service teachers demonstrated an elevated level of readiness for artificial intelligence and positive self-efficacy beliefs, yet no meaningful relationship emerged between the two variables. The results suggest that professional self-efficacy and technological readiness are influenced by broader contextual and pedagogical factors rather than functioning in a straightforward manner. In the qualitative phase, participants highlighted both opportunities and challenges related to the use of artificial intelligence in primary education. While many emphasized its potential to support personalized learning, reduce workload, and enhance student adaptability, concerns were raised about ethical implications, risks to social-emotional development, cultural values, digital literacy gaps, and infrastructural limitations. The study underscores the necessity for teacher education programs to extend beyond technical training by incorporating pedagogical, ethical, and cultural dimensions to prepare pre-service teachers for meaningful integration of artificial intelligence into educational practice. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
23 pages, 2710 KB  
Article
Non-Semantic Multimodal Fusion for Predicting Segment Access Frequency in Lecture Archives
by Ruozhu Sheng, Jinghong Li and Shinobu Hasegawa
Educ. Sci. 2025, 15(8), 978; https://doi.org/10.3390/educsci15080978 - 30 Jul 2025
Viewed by 1273
Abstract
This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, [...] Read more.
This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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19 pages, 1635 KB  
Article
Integrating AI-Driven Wearable Metaverse Technologies into Ubiquitous Blended Learning: A Framework Based on Embodied Interaction and Multi-Agent Collaboration
by Jiaqi Xu, Xuesong Zhai, Nian-Shing Chen, Usman Ghani, Andreja Istenic and Junyi Xin
Educ. Sci. 2025, 15(7), 900; https://doi.org/10.3390/educsci15070900 - 15 Jul 2025
Cited by 1 | Viewed by 3476
Abstract
Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory [...] Read more.
Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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22 pages, 5384 KB  
Article
Evaluating an Artificial Intelligence (AI) Model Designed for Education to Identify Its Accuracy: Establishing the Need for Continuous AI Model Updates
by Navdeep Verma, Seyum Getenet, Christopher Dann and Thanveer Shaik
Educ. Sci. 2025, 15(4), 403; https://doi.org/10.3390/educsci15040403 - 23 Mar 2025
Cited by 2 | Viewed by 4955
Abstract
The growing popularity of online learning brings with it inherent challenges that must be addressed, particularly in enhancing teaching effectiveness. Artificial intelligence (AI) offers potential solutions by identifying learning gaps and providing targeted improvements. However, to ensure their reliability and effectiveness in educational [...] Read more.
The growing popularity of online learning brings with it inherent challenges that must be addressed, particularly in enhancing teaching effectiveness. Artificial intelligence (AI) offers potential solutions by identifying learning gaps and providing targeted improvements. However, to ensure their reliability and effectiveness in educational contexts, AI models must be rigorously evaluated. This study aimed to evaluate the performance and reliability of an AI model designed to identify the characteristics and indicators of engaging teaching videos. The research employed a design-based approach, incorporating statistical analysis to evaluate the AI model’s accuracy by comparing its assessments with expert evaluations of teaching videos. Multiple metrics were employed, including Cohen’s Kappa, Bland–Altman analysis, the Intraclass Correlation Coefficient (ICC), and Pearson/Spearman correlation coefficients, to compare the AI model’s results with those of the experts. The findings indicated low agreement between the AI model’s assessments and those of the experts. Cohen’s Kappa values were low, suggesting minimal categorical agreement. Bland–Altman analysis showed moderate variability with substantial differences in results, and both Pearson and Spearman correlations revealed weak relationships, with values close to zero. The ICC indicated moderate reliability in quantitative measurements. Overall, these results suggest that the AI model requires continuous updates to improve its accuracy and effectiveness. Future work should focus on expanding the dataset and utilise continual learning methods to enhance the model’s ability to learn from new data and improve its performance over time. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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17 pages, 281 KB  
Article
Subject-Specialized Chatbot in Higher Education as a Tutor for Autonomous Exam Preparation: Analysis of the Impact on Academic Performance and Students’ Perception of Its Usefulness
by Fulgencio Sánchez-Vera
Educ. Sci. 2025, 15(1), 26; https://doi.org/10.3390/educsci15010026 - 30 Dec 2024
Cited by 6 | Viewed by 5764
Abstract
This study evaluates the impact of an AI chatbot as a support tool for second-year students in the Bachelor’s Degree in Early Childhood Education program during final exam preparation. Over 1-month, 42 students used the chatbot, generating 704 interactions across 186 conversations. The [...] Read more.
This study evaluates the impact of an AI chatbot as a support tool for second-year students in the Bachelor’s Degree in Early Childhood Education program during final exam preparation. Over 1-month, 42 students used the chatbot, generating 704 interactions across 186 conversations. The study aimed to assess the chatbot’s effectiveness in resolving specific questions, enhancing concept comprehension, and preparing for exams. Methods included surveys, in-depth interviews, and analysis of chatbot interactions. Results showed that the chatbot was highly effective in clarifying doubts (91.4%) and aiding concept understanding (95.7%), although its perceived usefulness was lower in content review (42.9%) and exam simulations (45.4%). Students with moderate chatbot use achieved better academic outcomes, while excessive use did not lead to further improvements. The study also identified challenges in students’ ability to formulate effective questions, limiting the chatbot’s potential in some areas. Overall, the chatbot was valued for fostering study autonomy, though improvements are needed in features supporting motivation and study organization. These findings highlight the potential of chatbots as complementary learning tools but underscore the need for better user training in “prompt engineering” to maximize their effectiveness. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)

Other

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14 pages, 528 KB  
Systematic Review
Integrating Artificial Intelligence into the Cybersecurity Curriculum in Higher Education: A Systematic Literature Review
by Jing Tian
Educ. Sci. 2025, 15(11), 1540; https://doi.org/10.3390/educsci15111540 - 15 Nov 2025
Cited by 4 | Viewed by 2266
Abstract
Background: To understand the state of the art of how artificial intelligence (AI) and cybersecurity are taught together, this paper conducts a systematic literature review on integrating AI into the cybersecurity curriculum in higher education. Methods: The peer-reviewed works were screened from major [...] Read more.
Background: To understand the state of the art of how artificial intelligence (AI) and cybersecurity are taught together, this paper conducts a systematic literature review on integrating AI into the cybersecurity curriculum in higher education. Methods: The peer-reviewed works were screened from major databases published between 2020 and 2025. Integrating AI and cybersecurity typically requires new learning designs. To address this gap in higher education, this review is organized by three categories of research questions: (1) who we teach (audiences and delivery modes), (2) what we teach (related AI topics and cybersecurity topics and how they are integrated), and (3) how we teach (instructional activities and tools used in teaching). Results: The course delivery is mostly face-to-face. The course curricula focus mostly on perception AI. Teaching methods are active and practical, with hands-on labs, interactive tasks, and game-based activities, supported by hardware, programming notebooks, and interactive visualizations. Conclusion: This paper provides the state of the art of integrating AI into the cybersecurity curriculum in higher education, actionable recommendations, and implications for further research. Therefore, it is relevant and transferable for instructors in the field of artificial intelligence education and cybersecurity education. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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18 pages, 7518 KB  
Systematic Review
ChatGPT in Teaching and Learning: A Systematic Review
by Duha Ali, Yasin Fatemi, Elahe Boskabadi, Mohsen Nikfar, Jude Ugwuoke and Haneen Ali
Educ. Sci. 2024, 14(6), 643; https://doi.org/10.3390/educsci14060643 - 14 Jun 2024
Cited by 81 | Viewed by 51516
Abstract
The increasing use of artificial intelligence (AI) in education has raised questions about the implications of ChatGPT for teaching and learning. A systematic literature review was conducted to answer these questions, analyzing 112 scholarly articles to identify the potential benefits and challenges related [...] Read more.
The increasing use of artificial intelligence (AI) in education has raised questions about the implications of ChatGPT for teaching and learning. A systematic literature review was conducted to answer these questions, analyzing 112 scholarly articles to identify the potential benefits and challenges related to ChatGPT use in educational settings. The selection process was thorough to ensure a comprehensive analysis of the current academic discourse on AI tools in education. Our research sheds light on the significant impact of ChatGPT on improving student engagement and accessibility and the critical issues that need to be considered, including concerns about the quality and bias of generated responses, the risk of plagiarism, and the authenticity of educational content. The study aims to summarize the utilizations of ChatGPT in teaching and learning by addressing the identified benefits and challenges through targeted strategies. The authors outlined some recommendations that will ensure that the integration of ChatGPT into educational frameworks enhances learning outcomes while safeguarding academic standards. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blended Learning: Challenges, Opportunities, and Future Directions)
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