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Education Sciences
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Teaching and Learning with Generative AI
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Teaching and Learning with Generative AI

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

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 47777

Special Issue Editors

Dr. Brian K. Smith

E-Mail Website
Guest Editor
Lynch School of Education and Human Development, Boston College, Chestnut Hill, MA 02467, USA
Interests: design of computational learning environments; out-of-school learning; human–computer interaction; computer science education
Dr. Matthew Duvall

E-Mail Website
Guest Editor
Learning, Teaching, & Literacy, Graduate School of Education, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: design of technology-enhanced learning experiences; computational thinking; teacher professional development; game-based learning
Dr. Marcela Borge

E-Mail Website
Guest Editor
Learning, Design, and Technology, College of Education, The Pennsylvania State University, University Park, PA 16801, USA
Interests: play-based learning; socio-metacognition; human-centered design; computer-supported collaborative learning

Special Issue Information

Dear Colleagues,

Advances in the 2020s led to an explosion of generative artificial intelligence (genAI) tools. These systems are best known for their capacity to generate text, images, videos and other media that resemble similar outputs created by humans. Examples include text-to-image generators like Dall-E or MidJourney, and systems that produce language such as Bing or ChatGPT. 

Although there is a long history of AI research in education, these generative tools have affordances that could lead to novel approaches to supporting education. This Special Issue of Educational Sciences welcomes submissions from contributors, focused on “Teaching and Learning with Generative AI (GenAI). It is important that we explore and evaluate how the integration of these technologies can enhance learning and development. However, to develop learning innovations that could be adopted in real-world settings, we must pair the design of new learning technologies and practices with learning theory and ongoing research in authentic learning contexts.  

In this Special Issue, we are interested in submissions relating to the effective use of GenAI in formal and informal learning settings, including in teaching (across the lifespan), as well as learning in and out of school. We welcome interdisciplinary/innovative approaches, including quantitative/qualitative/mixed-methods research and conceptual articles.

  1. Novel pedagogical perspectives and theories on the use of GenAI in education;
  2. Using GenAI tools to support the development of AI literacy;
  3. Design and development of GenAI-based educational interventions;
  4. Supporting collaborative learning processes, i.e., collective sense-making, creativity, regulation/socio-metacognition;
  5. Societal and ethical considerations of the use of GenAI in education;
  6. GenAI support for non-cognitive/social-emotional skill development (e.g., persistence, resilience, empathy);
  7. Increasing equity and inclusion with GenAI-supported tools and curricula;
  8. Tensions and connections between business/technical/educational conceptualizations of GenAI;
  9. Future directions for research on GenAI and education. 

Dr. Brian K. Smith
Dr. Matthew Duvall
Dr. Marcela Borge
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 artificial intelligence
  • educational technology
  • ethical considerations

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Published Papers (7 papers)

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Research

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20 pages, 396 KiB  
Article
Mentorship in the Age of Generative AI: ChatGPT to Support Self-Regulated Learning of Pre-Service Teachers Before and During Placements
by Ngoc Nhu Nguyen (Ruby) and Walter Barbieri
Educ. Sci. 2025, 15(6), 642; https://doi.org/10.3390/educsci15060642 - 23 May 2025
Abstract
This study investigates the integration of mentorship, self-regulated learning (SRL), and generative artificial intelligence (gen-AI) to support pre-service teachers (PSTs) before and during work-integrated learning (WIL) placements. Utilising the Mentoring and SRL Pyramid Model (MSPM), it examines how mentors’ dual roles as coaches [...] Read more.
This study investigates the integration of mentorship, self-regulated learning (SRL), and generative artificial intelligence (gen-AI) to support pre-service teachers (PSTs) before and during work-integrated learning (WIL) placements. Utilising the Mentoring and SRL Pyramid Model (MSPM), it examines how mentors’ dual roles as coaches and assessors influence PSTs’ SRL and explores to what extent gen-AI can assist PSTs in meeting the demands of WIL placements. Quantitative and qualitative data from 151 PSTs, including survey, interview, placement scores, and mentor feedback were analysed using statistical correlation analysis and thematic analysis to reveal varied mentorship approaches. Gen-AI tools are highlighted as valuable in enhancing PSTs’ SRL, providing tactical and emotional guidance where traditional mentorship is limited. However, challenges remain in gen-AI’s ability to navigate complex interpersonal dynamics. The study advocates for balanced mentorship training that integrates technical and emotional support, and equitable access to gen-AI tools. These insights are critical for educational institutions aiming to optimise PST experiences and outcomes in WIL through strategic integration of gen-AI and mentorship. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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15 pages, 997 KiB  
Article
Generative AI in Higher Education: Teachers’ and Students’ Perspectives on Support, Replacement, and Digital Literacy
by Samia Haroud and Nadia Saqri
Educ. Sci. 2025, 15(4), 396; https://doi.org/10.3390/educsci15040396 - 21 Mar 2025
Viewed by 2462
Abstract
Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its [...] Read more.
Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its implications for teaching and learning. A mixed-methods approach was adopted, combining quantitative data from 130 teachers and 156 students with qualitative insights. Quantitative findings reveal significant differences: students demonstrate greater openness to adopting AI, appreciating its capacity to provide instant feedback, enhance creativity, and improve academic performance. In contrast, teachers express reservations, particularly regarding AI’s potential to undermine critical soft skills such as collaboration, problem-solving, and critical thinking. Qualitative analyses confirm these trends, highlighting that, while AI is perceived as a valuable complementary tool, it cannot replace the essential human role of educators in providing personalized guidance and addressing students’ emotional and cognitive needs. Both groups agree on the necessity of enhanced digital literacy to ensure ethical and effective AI integration. These findings underscore the opportunities of generative AI, such as personalized learning and efficiency, while addressing limitations like ethical concerns and over-reliance, offering actionable insights for policymakers, educators, and technologists aiming to integrate AI responsibly in education. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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21 pages, 944 KiB  
Article
ChatGPT for Science Lesson Planning: An Exploratory Study Based on Pedagogical Content Knowledge
by Giorgos Peikos and Dimitris Stavrou
Educ. Sci. 2025, 15(3), 338; https://doi.org/10.3390/educsci15030338 - 10 Mar 2025
Viewed by 2160
Abstract
Contemporary education is evolving in a landscape shaped by technological advancements, with generative artificial intelligence (AI) gaining significant attention from educators and researchers. ChatGPT, in particular, has been recognized for its potential to revolutionize teachers’ tasks, such as lesson planning. However, its effectiveness [...] Read more.
Contemporary education is evolving in a landscape shaped by technological advancements, with generative artificial intelligence (AI) gaining significant attention from educators and researchers. ChatGPT, in particular, has been recognized for its potential to revolutionize teachers’ tasks, such as lesson planning. However, its effectiveness in designing science lesson plans aligned with the research-based recommendations of the Science Education literature remains in its infancy. This exploratory study seeks to address this gap by examining ChatGPT-assisted lesson planning for primary schools through the lens of a sound theoretical framework in Science Education: pedagogical content knowledge (PCK). Guided by the question, “What are the characteristics of lesson plans created by ChatGPT in terms of PCK?”, we designed four interactions with ChatGPT-4o using carefully constructed prompts informed by specific PCK aspects and prompt engineering strategies. Using qualitative content analysis, we analyzed data from these interactions. Findings indicate that incorporating PCK elements into prompts, using layer prompting strategies, and providing reference texts to ChatGPT might enhance the quality of AI-generated lesson plans. However, challenges were identified. This study concludes with guidelines for the teacher–ChatGPT co-design of lesson plans based on PCK. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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31 pages, 348 KiB  
Article
Generative AI Tools in Salvadoran Higher Education: Balancing Equity, Ethics, and Knowledge Management in the Global South
by Tizziana Valdivieso and Oscar González
Educ. Sci. 2025, 15(2), 214; https://doi.org/10.3390/educsci15020214 - 10 Feb 2025
Cited by 1 | Viewed by 1617
Abstract
The integration of generative artificial intelligence (GAI) tools in higher education offers new opportunities for personalized learning, critical thinking, and digital literacy. However, socio-economic disparities and ethical concerns present significant challenges to equitable and responsible GAI use, particularly in under-resourced educational settings. This [...] Read more.
The integration of generative artificial intelligence (GAI) tools in higher education offers new opportunities for personalized learning, critical thinking, and digital literacy. However, socio-economic disparities and ethical concerns present significant challenges to equitable and responsible GAI use, particularly in under-resourced educational settings. This mixed-methods study explored how undergraduate students at Universidad Centroamericana José Simeón Cañas (UCA) in El Salvador engage with GAI tools, focusing on patterns of access, usage, and the socio-economic and ethical factors shaping these interactions. A quantitative survey of 365 students and qualitative focus groups with 25 participants were conducted to examine device preferences, tool usage, and concerns related to academic integrity, data privacy, and responsible AI use. Results revealed significant socio-economic disparities in access to GAI tools, with students from lower-income backgrounds primarily using smartphones and free AI tools, while higher-income students reported greater access to laptops and premium features. Ethical concerns were more prominent among students with limited institutional support, highlighting the need for structured guidance on the responsible use of GAI tools. These findings underscore the importance of institutional policies that promote equitable access to educational technologies and provide ethical frameworks for their use. By integrating socio-constructivist and connectivist learning theories, this study emphasizes that equitable access and guided support are critical for maximizing the educational potential of GAI tools. The study contributes to ongoing discussions about how higher education institutions, particularly in the Global South, can responsibly and effectively integrate AI technologies to support diverse student populations. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)

Review

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22 pages, 1532 KiB  
Review
Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies
by Odin Monrad Schei, Anja Møgelvang and Kristine Ludvigsen
Educ. Sci. 2024, 14(8), 922; https://doi.org/10.3390/educsci14080922 - 22 Aug 2024
Cited by 14 | Viewed by 32035
Abstract
With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review [...] Read more.
With the recent arrival of publicly available AI chatbots like ChatGPT, Copilot, Gemini, and Claude follows a need for knowledge about how students in higher education perceive and use these tools, and what this might mean for their learning processes. This scoping review analyzes 24 empirical articles published between 1 January 2022 and 5 September 2023 on students’ perceptions and use of AI chatbots in higher education. The articles were reviewed using a five-stage scoping review methodology. The findings underscore a global research interest in how students engage with AI chatbots, which is especially pronounced in Asia. The studies span diverse disciplines, with a predominance in science, technology, engineering, and mathematics disciplines. The empirical findings reveal that students perceive AI chatbots as highly useful and motivating as personal task assistants and for getting immediate feedback and help with writing, coding, and academic tasks. However, students are concerned about the accuracy and reliability of the responses from the chatbots, as well as potential negative impacts on their learning processes, critical thinking, discipline, and creativity. The purpose-driven use of AI chatbots among students and their potentially positive influence on motivation and learning processes offer insights for educators and policymakers. Our research concludes that while positive attitudes, perceptions, and critical use prevail, addressing students’ concerns is crucial for responsible AI integration in higher education. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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9 pages, 471 KiB  
Review
Generative Artificial Intelligence in Tertiary Education: Assessment Redesign Principles and Considerations
by Che Yee Lye and Lyndon Lim
Educ. Sci. 2024, 14(6), 569; https://doi.org/10.3390/educsci14060569 - 26 May 2024
Cited by 7 | Viewed by 4904
Abstract
The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative [...] Read more.
The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative AI models trained on information retrieved from the Internet could produce biased and discriminatory outputs, and hallucination issues in large language models upon which generative AI acts provide made-up and untruthful outputs. This article considers the affordances and challenges of generative AI specific to assessments within tertiary education. It illustrates considerations for assessment redesign with the existence of generative AI and proposes the Against, Avoid and Adopt (AAA) principle to rethink and redesign assessments. It argues that more generative AI tools will emerge exponentially, and hence, engaging in an arms race against generative AI and policing the use of these technologies may not address the fundamental issues in assessments. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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Other

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20 pages, 282 KiB  
Essay
Reconceptualizing the Role of the University Language Teacher in Light of Generative AI
by Mark Tutton and Doron Cohen
Educ. Sci. 2025, 15(1), 56; https://doi.org/10.3390/educsci15010056 - 8 Jan 2025
Viewed by 1173
Abstract
This paper reconceptualizes the role of the teacher in the university foreign language classroom in an age of generative AI chatbots and automatic translation tools. We call for a reconceptualization of this role based on two factors: the unique social interactivity of the [...] Read more.
This paper reconceptualizes the role of the teacher in the university foreign language classroom in an age of generative AI chatbots and automatic translation tools. We call for a reconceptualization of this role based on two factors: the unique social interactivity of the university language classroom and the need for effective instruction on how to use Intelligent Computer-Assisted Language Learning (ICALL) tools outside of the classroom. We argue that the teacher must master and integrate these two different modes of teaching and learning. Interpersonal exchanges in class respond to the need for real-time human interaction and relatedness in language learning and so cannot, and should not, be wholly replaced by chatbots. Rather, these sorts of exchanges must form a cornerstone of on-campus foreign language pedagogy. In contrast, teachers must also be able to leverage the benefits of learner-facing AI tools, especially for use outside of the classroom, given the learning gains associated with them. We provide detailed examples of how this dual approach can be realized and propose a five-step approach for incorporating AI into university language pedagogy. Full article
(This article belongs to the Special Issue Teaching and Learning with Generative AI)
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