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Education Sciences
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Generative-AI-Enhanced Learning Environments and Applications
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Generative-AI-Enhanced Learning Environments and Applications

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 3226

Special Issue Editors

Dr. Jianhua Yang

E-Mail Website
Guest Editor
International Digital Laboratory, Warwick Manufacturing Group, Warwick University, Coventry CV47AL, UK
Interests: pedagogical research; artificial neural networks; evolutionary computing
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Lu

E-Mail Website
Guest Editor
School of Computer Science, Leeds Trinity University, Leeds LS18 5HD, UK
Interests: deep learning; big data analytics; smart systems; human–robot collaboration; GenAI in higher education
Special Issues, Collections and Topics in MDPI journals
Dr. Michael O'Dea

E-Mail Website
Guest Editor
Department of Computer Science, University of York, York YO10 5DD, UK
Interests: generative AI in education; computer science education; machine learning
Dr. Wei Wang

E-Mail Website
Guest Editor
School of Engineering Science, University of Skövde, 54128 Skövde, Sweden
Interests: intelligent manufacturing; CAD/CAPP/CAM; human–robot collaboration; digital twins; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In the ever-evolving arena of education, the advent of generative artificial intelligence (GenAI) presents a frontier brimming with potential that is yet to be fully realized, especially within the context of higher education. Despite centuries of evolution in teaching methodologies, the core essence of learning has remained largely unchanged since the era of Confucius. Today, as we stand on the brink of a new educational paradigm, the integration of GenAI into learning environments beckons as a promising yet underexplored area upon which to embark. The burgeoning field of GenAI offers innovative tools and methodologies that could revolutionize educational practices, yet the path for its effective incorporation into higher education remains nebulous. There is a pressing need for comprehensive case studies, empirical research, and practical applications to illuminate the capabilities and limitations of GenAI in educational settings. 

This Special Issue, titled "Generative-AI-Enhanced Learning Environments and Applications", aims to bridge this gap by providing a platform for scholarly discourse and exploration into the integration and application of GenAI within higher educational settings. It seeks to elucidate the impact, potential, and challenges of adopting GenAI technologies, with a focus on developing robust, evidence-based frameworks for their implementation. Through empirical studies, case analyses, and theoretical contributions, this Special Issue endeavours to advance our understanding of how GenAI can be synergized with existing educational paradigms to foster more engaging, effective, and personalized learning experiences. 

The themes of this Special Issue are organized around three core areas of inquiry, reflecting the multifaceted implications of GenAI in education. These are as follows: 

  1. The Integration of GenAI into New or Existing Teaching Frameworks: This theme includes the seamless incorporation of GenAI into established educational methodologies such as problem-based learning, experiential learning, and others. Under this theme, this Special Issue seeks contributions that demonstrate how GenAI can enhance traditional teaching approaches, innovate assessment methods, and contribute to the development of new pedagogies and ethics in education.
  2. Using GenAI from the Learner's Perspective: Focusing on the student experience, this Special Issue invites studies and reports on how learners interact with GenAI tools, including personal tutoring systems, ChatGPT as a personal assistant, and prompt engineering in educational contexts. Contributions may address the impact of these technologies on learning outcomes, student engagement, and the personalization of the learning journey.
  3. Effective Human–AI Partnership and Interaction: This theme encompasses the dynamics of collaboration between humans and AI in the higher education ecosystem. It includes the development of human–AI partnerships, the systematic use of GenAI as a facilitative tool in teaching and learning, and the exploration of ethical considerations and best practices for human–AI interaction. 

Through these themes, this Special Issue aims to generate a rich dialogue on the transformative potential of GenAI in enhancing learning environments and applications, paving the way for informed and innovative educational practices in so doing. 

We look forward to receiving your contributions.

Dr. Jianhua Yang
Dr. Xin Lu
Dr. Michael O'Dea
Dr. Wei Wang
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
  • educational technology
  • human-AI partnership
  • experiential learning
  • personalized tutoring
  • pedagogical ethics
  • prompt engineering

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Published Papers (2 papers)

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24 pages, 3419 KiB  
Article
Evaluating the Potential of Generative Artificial Intelligence to Innovate Feedback Processes
by Gilberto Huesca, Mariana E. Elizondo-García, Ricardo Aguayo-González, Claudia H. Aguayo-Hernández, Tanya González-Buenrostro and Yuridia A. Verdugo-Jasso
Educ. Sci. 2025, 15(4), 505; https://doi.org/10.3390/educsci15040505 - 18 Apr 2025
Viewed by 311
Abstract
Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially [...] Read more.
Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially defining an adaptable framework to integrate GenAI into feedback mechanisms defined in theoretical frameworks. We applied a between-subjects analysis in an experimental research design with 263 undergraduate students across multiple disciplines based on an approach consisting of a pretest–post-test process and control and focus groups to evaluate students’ perceptions of artificial intelligence-enhanced feedback versus traditional professor-led feedback. The results show that students who used GenAI declared statistically significantly higher satisfaction levels and a greater sense of ownership in the feedback process. Additionally, GenAI scaffolded continuous improvement and active student participation through a structured and accessible feedback environment, determining that 97% of students are willing to reuse the tool. These findings show that GenAI is a valuable tool to complement professors in the creation of an integrated feedback model. This study draws directions on future research on the combination of artificial intelligence and innovative strategies to produce a long-term impact on education. Full article
(This article belongs to the Special Issue Generative-AI-Enhanced Learning Environments and Applications)
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20 pages, 383 KiB  
Article
Reimagining Flipped Learning via Bloom’s Taxonomy and Student–Teacher–GenAI Interactions
by Paul Kwan, Rajan Kadel, Tayab D. Memon and Saad S. Hashmi
Educ. Sci. 2025, 15(4), 465; https://doi.org/10.3390/educsci15040465 - 8 Apr 2025
Viewed by 470
Abstract
This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the [...] Read more.
This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity. Full article
(This article belongs to the Special Issue Generative-AI-Enhanced Learning Environments and Applications)
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