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AI
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Exploring the Use of Artificial Intelligence in Education
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Exploring the Use of Artificial Intelligence in Education

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5050

Special Issue Editor

Dr. Hyeon Jo

E-Mail Website
Guest Editor
Headquarters, HJ Institute of Technology and Management, 71 Jungdong-ro 39, Bucheon-si 14721, Gyeonggi-do, Republic of Korea
Interests: AI; generative AI; AI in education; students’ use of AI; ethical concerns on Ai

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) in education is revolutionizing the way learning and teaching are approached. This Special Issue on “Exploring the Use of Artificial Intelligence in Education” aims to delve into the various applications and impacts of AI within the educational landscape. AI technologies, such as machine learning, natural language processing, and intelligent tutoring systems, are being leveraged to personalize learning experiences, automate administrative tasks, and enhance educational outcomes. This Special Issue seeks to explore both theoretical and practical aspects, including the development of AI-driven educational tools, the ethical implications of AI in education, and the future of AI-enhanced learning environments. We invite researchers, educators, and technologists to submit their work on topics such as AI-driven adaptive learning systems, automated assessment and feedback, AI in educational data analytics, and the role of AI in promoting inclusivity and accessibility in education. By bringing together diverse perspectives and cutting-edge research, this Special Issue strives to provide a comprehensive overview of the current state and future directions of AI in education.

Dr. Hyeon Jo
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 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 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. AI 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 1600 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

  • human–AI interaction in education
  • educational technology
  • adaptive learning systems
  • intelligent tutoring systems
  • automated assessment
  • educational data analytics
  • personalized learning
  • AI ethics in education
  • inclusive education
  • generative AI in education

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

Published Papers (3 papers)

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15 pages, 3081 KiB  
Article
Antiparasitic Pharmacology Goes to the Movies: Leveraging Generative AI to Create Educational Short Films
by Benjamin Worthley, Meize Guo, Lucas Sheneman and Tyler Bland
AI 2025, 6(3), 60; https://doi.org/10.3390/ai6030060 - 17 Mar 2025
Viewed by 697
Abstract
Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and [...] Read more.
Medical education faces the dual challenge of addressing cognitive overload and sustaining student engagement, particularly in complex subjects such as pharmacology. This study introduces Cinematic Clinical Narratives (CCNs) as an innovative approach to teaching antiparasitic pharmacology, combining generative artificial intelligence (genAI), edutainment, and mnemonic-based learning. The intervention involved two short films, Alien: Parasites Within and Wormquest, designed to teach antiparasitic pharmacology to first-year medical students. A control group of students only received traditional text-based clinical cases, while the experimental group engaged with the CCNs in an active learning environment. Students who received the CCN material scored an average of 8% higher on exam questions related to the material covered by the CCN compared to students in the control group. Results also showed that the CCNs improved engagement and interest among students, as evidenced by significantly higher scores on the Situational Interest Survey for Multimedia (SIS-M) compared to traditional methods. Notably, students preferred CCNs for their storytelling, visuals, and interactive elements. This study underscores the potential of CCNs as a supplementary educational tool, and suggests the potential for broader applications across other medical disciplines outside of antiparasitic pharmacology. By leveraging genAI and edutainment, CCNs represent a scalable and innovative approach to enhancing the medical learning experience. Full article
(This article belongs to the Special Issue Exploring the Use of Artificial Intelligence in Education)
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20 pages, 683 KiB  
Article
The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback
by Lucas Jasper Jacobsen and Kira Elena Weber
AI 2025, 6(2), 35; https://doi.org/10.3390/ai6020035 - 12 Feb 2025
Viewed by 2554
Abstract
Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with [...] Read more.
Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with feedback from novices and experts. This study investigates (1) the types of prompts needed to ensure high-quality LLM feedback in teacher education and (2) how feedback from novices, experts, and LLMs compares in terms of quality. Methods: To address these questions, we developed a theory-driven manual to evaluate prompt quality and designed three prompts of varying quality. Feedback generated by ChatGPT-4 was assessed alongside feedback from novices and experts, who were provided with the highest-quality prompt. Results: Our findings reveal that only the best prompt consistently produced high-quality feedback. Additionally, LLM feedback outperformed novice feedback and, in the categories explanation, questions, and specificity, even surpassed expert feedback in quality while being generated more quickly. Conclusions: These results suggest that LLMs, when guided by well-crafted prompts, can serve as high-quality and efficient alternatives to expert feedback. The findings underscore the importance of prompt quality and emphasize the need for prompt design guidelines to maximize the potential of LLMs in teacher education. Full article
(This article belongs to the Special Issue Exploring the Use of Artificial Intelligence in Education)
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22 pages, 720 KiB  
Systematic Review
AI and Creativity in Entrepreneurship Education: A Systematic Review of LLM Applications
by Jeong-Hyun Park, Seon-Joo Kim and Sung-Tae Lee
AI 2025, 6(5), 100; https://doi.org/10.3390/ai6050100 - 14 May 2025
Viewed by 398
Abstract
The rapid advancement of artificial intelligence (AI) and digital transformation is reshaping labor markets, emphasizing creativity as a core competency in entrepreneurship education. Large Language Models (LLMs) provide personalized learning experiences through natural language processing (NLP), enhancing real-time feedback and problem-solving skills. However, [...] Read more.
The rapid advancement of artificial intelligence (AI) and digital transformation is reshaping labor markets, emphasizing creativity as a core competency in entrepreneurship education. Large Language Models (LLMs) provide personalized learning experiences through natural language processing (NLP), enhancing real-time feedback and problem-solving skills. However, research on how LLMs foster creativity in entrepreneurship education remains limited. This study analyzed the technical characteristics and educational impact of LLMs, focusing on their applications in entrepreneurship education and their role in fostering creativity-driven learning environments. Specifically, it explores the educational effects of LLMs, their integration into entrepreneurship education, and the ways in which they enhance learners’ creative thinking. A systematic literature review using the PRISMA methodology was conducted to analyze existing studies. Findings suggest that LLMs improve self-efficacy, cognitive engagement, and creative problem-solving, supporting entrepreneurship education in areas such as business model development, market analysis, and multicultural communication. Despite these benefits, concerns remain regarding over-reliance, ethical risks, and the need for critical thinking frameworks. This study proposes a hybrid model integrating LLMs with traditional pedagogies to maximize creativity. Future research should explore long-term effects, cross-cultural applications, and ethical challenges to ensure responsible implementation. Full article
(This article belongs to the Special Issue Exploring the Use of Artificial Intelligence in Education)
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