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Education Sciences
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Generative AI in Education: Current Trends and Future Directions
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Generative AI in Education: Current Trends and Future Directions

A special issue of Education Sciences (ISSN 2227-7102).

Deadline for manuscript submissions: 20 February 2026 | Viewed by 4798

Special Issue Editors

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
Dr. Shuchen Guo

E-Mail Website
Guest Editor
1. Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, GA 30602, USA
2. School of Teacher Education, Nanjing Normal University, Nanjing 210023, China
Interests: AI-based innovative assessment practices in science; science teacher education; student attitudes toward science

Special Issue Information

Dear Colleagues,

The burgeoning field of generative artificial intelligence (GenAI) technologies has offered considerable potential to revolutionize the field of education. With various promising GenAI tools for empowering the teaching and learning process, such as enhancing timely feedback and personalized learning, the practice of successfully integrating GenAI in education still remains nebulous. Thus, research is desperately needed to shed light on the strengths and concerns of integrating GenAI in educational environments, both now and in the future.

This Special Issue, titled " Generative AI in Education: Current Trends and Future Directions", aims to explore the possibilities, impacts and challenges associated with implementing GenAI in education in order to deepen the understanding of how GenAI might be leveraged to create a more engaging, effective and equitable teaching and learning environment.

We therefore invite papers that address this topic of interest in a timely manner. This Special Issue is organized around the following themes:  

  1. The development of GenAI applications for educational purposes: this theme includes studies focusing on the development of applications integrating GenAI to empower teaching and learning, such as intelligent tutoring systems, automatic scoring systems, etc.
  2. Best practices of integrating GenAI in teaching and learning: this theme focuses on the student and teacher experience and performance when incorporating GenAI in real teaching and learning practices.
  3. The ethical and social issues of GenAI in educational settings: this theme aims to address ethical and social problems when leveraging GenAI in education.

We look forward to receiving your contributions.

Dr. Xiaoming Zhai
Dr. Shuchen Guo
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • generative AI (GenAI)
  • automatic scoring
  • personalized tutoring
  • prompt engineering

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

Published Papers (3 papers)

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32 pages, 1709 KiB  
Article
Supporting Reflective AI Use in Education: A Fuzzy-Explainable Model for Identifying Cognitive Risk Profiles
by Gabriel Marín Díaz
Educ. Sci. 2025, 15(7), 923; https://doi.org/10.3390/educsci15070923 - 18 Jul 2025
Abstract
Generative AI tools are becoming increasingly common in education. They make many tasks easier, but they also raise questions about how students interact with information and whether their ability to think critically might be affected. Although these tools are now part of many [...] Read more.
Generative AI tools are becoming increasingly common in education. They make many tasks easier, but they also raise questions about how students interact with information and whether their ability to think critically might be affected. Although these tools are now part of many learning processes, we still do not fully understand how they influence cognitive behavior or digital maturity. This study proposes a model to help identify different user profiles based on how they engage with AI in educational contexts. The approach combines fuzzy clustering, the Analytic Hierarchy Process (AHP), and explainable AI techniques (SHAP and LIME). It focuses on five dimensions: how AI is used, how users verify information, the cognitive effort involved, decision-making strategies, and reflective behavior. The model was tested on data from 1273 users, revealing three main types of profiles, from users who are highly dependent on automation to more autonomous and critical users. The classification was validated with XGBoost, achieving over 99% accuracy. The explainability analysis helped us understand what factors most influenced each profile. Overall, this framework offers practical insight for educators and institutions looking to promote more responsible and thoughtful use of AI in learning. Full article
(This article belongs to the Special Issue Generative AI in Education: Current Trends and Future Directions)
23 pages, 3511 KiB  
Article
From Intimidation to Innovation: Cross-Continental Multiple Case Studies on How to Harness AI to Elevate Engagement, Comprehension, and Retention
by Sue Haywood, Loredana Padurean, Renée Ralph and Jutta Tobias Mortlock
Educ. Sci. 2025, 15(7), 902; https://doi.org/10.3390/educsci15070902 - 15 Jul 2025
Viewed by 212
Abstract
As generative AI tools become increasingly embedded in education, their role in supporting student learning remains both promising and contested. These cross-continental multiple case studies explore how integrating AI into classroom-based creative projects can move students from intimidation to meaningful engagement, comprehension, and [...] Read more.
As generative AI tools become increasingly embedded in education, their role in supporting student learning remains both promising and contested. These cross-continental multiple case studies explore how integrating AI into classroom-based creative projects can move students from intimidation to meaningful engagement, comprehension, and retention of course content. Drawing on data from four international university classrooms—in the USA, UK, Canada, and Australia—this mixed-methods study examines students’ experiences as they collaboratively created comic books using generative AI. Each instructor embedded the assignment within their own pedagogical context, enabling cross-institutional comparison of AI’s educational potential. Findings highlight a shared trajectory: students initially approached AI with uncertainty or overconfidence, but developed nuanced understandings of its capabilities through experimentation, reflection, and collaboration. The process of creating narrative-driven visual outputs required students to synthesize theoretical material, communicate effectively in teams, and creatively solve problems—fostering both cognitive and interpersonal learning. Students reported deeper comprehension of academic content and greater confidence using AI tools critically and ethically. This study concludes that when framed as a collaborative partner rather than a replacement for human thinking, AI can support deeper learning experiences. It also suggests that creative, team-based projects can demystify AI and build essential future-facing skills. Full article
(This article belongs to the Special Issue Generative AI in Education: Current Trends and Future Directions)
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38 pages, 9889 KiB  
Article
AI and Eye Tracking Reveal Design Elements’ Impact on E-Magazine Reader Engagement
by Hedda Martina Šola, Fayyaz Hussain Qureshi and Sarwar Khawaja
Educ. Sci. 2025, 15(2), 203; https://doi.org/10.3390/educsci15020203 - 8 Feb 2025
Viewed by 1481
Abstract
This study investigates the impact of intelligible background speech on reading disruption utilising neuromarketing methodologies, specifically an eye-tracking webcam (Tobii Sticky) and AI eye-tracking software (Predict, v.1.0.). A cohort of 144 participants from Oxford Business College underwent emotional impact testing, while an AI [...] Read more.
This study investigates the impact of intelligible background speech on reading disruption utilising neuromarketing methodologies, specifically an eye-tracking webcam (Tobii Sticky) and AI eye-tracking software (Predict, v.1.0.). A cohort of 144 participants from Oxford Business College underwent emotional impact testing, while an AI eye-tracking algorithm analysed attention patterns across 180,000 eye-tracking recordings. Two articles from OxConnect Magazine were presented in varying background formats. Python-based analysis revealed that the HND article consistently outperformed OxFoodbank in maintaining reader engagement and attention. The HND’s structured content yielded higher total attention (white: 49.43%, black: 48.19%) and end attention (white: 27.58%, black: 28.43%). Emotion analysis indicated that HND elicited a more neutral (white mean difference: 0.1514, black: 0.1008) and consistent emotional response, with reduced puzzlement (white mean difference: −0.3296, black: −0.0918). Furthermore, this demonstrates the effectiveness of integrating AI eye-tracking algorithms with webcam eye trackers for comprehensive reading behaviour analysis. These findings provide valuable insights for colleges developing e-magazines, offering evidence-based strategies to enhance student engagement and information retention. By implementing well-structured, visually appealing content, educational institutions can optimise their digital publications to maintain reader attention even in the presence of background distractions, ultimately improving the effectiveness of their e-magazines as educational tools. Full article
(This article belongs to the Special Issue Generative AI in Education: Current Trends and Future Directions)
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