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Education Sciences
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The Impact of Artificial Intelligence on Teaching and Learning
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The Impact of Artificial Intelligence on Teaching and Learning

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 13216

Special Issue Editors

Dr. Kristen Gregory

E-Mail Website
Guest Editor
Elementary, Middle, and Secondary Education Department, College of Education, East Carolina University, Greenville, NC 27858, USA
Interests: teacher education; professional development; artificial intelligence in education
Dr. Xi Lin

E-Mail Website
Guest Editor
Department of Interdisciplinary Professions, College of Education, East Carolina University, Greenville, SC, USA
Interests: adult learning; distance and online education; artificial Intelligence in education (AIED); game-based learning
Dr. Ken Luterbach

E-Mail Website
Guest Editor
Mathematics, Science, and Instructional Technology Education, College of Education, East Carolina University, Greenville, NC, USA
Interests: artificial intelligence in education; learning technologies; performance technology; computational thinking; instructional design and development

Special Issue Information

Dear Colleagues,

This Special Issue of Education Sciences will examine key topics associated with the impact of artificial intelligence (AI) on teaching and learning in K-12 and postsecondary education. AI is rapidly expanding in both capacity and usage in the field of education. However, there is variance in what educators and students know about best practices with implementing AI into teaching and learning. In this formative stage, there is a need for experimental results and case studies on the effectiveness of AI technologies to guide ethical practice.

This Special Issue aims to highlight, but is not limited to, the following topics:

  • Impact of AI on Student Learning Outcomes and Engagement;
  • Impact of AI on Teaching Practices and Pedagogical Strategies;
  • Impact of AI on Personalization and Differentiation;
  • Impact of AI on Assessment and Feedback Practices;
  • Impact of AI on Teacher Professional Development and Readiness;
  • Impact of AI on Educational Equity and Access;
  • Impact of AI on Student Experience;
  • Impact of AI on Student Identity and Agency;
  • Impact of AI on Ethical and Responsible Use in Education.

Researchers, educators, and practitioners are invited to contribute manuscripts to this Special Issue. The authors will ground their research in current and seminal literature and utilize quantitative and/or qualitative research methods to investigate their selected topic. Clear findings that exhibit the impact of AI on teaching and learning will be presented, including recommendations for practical usage of AI in K-12 and/or postsecondary educational settings.

Dr. Kristen Gregory
Dr. Xi Lin
Dr. Ken Luterbach
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 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
  • K-12 education
  • postsecondary education
  • AI in education

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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

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Research

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20 pages, 937 KB  
Article
Challenge and Hindrance Stressors, Artificial Intelligence Use and Interpersonal Interaction in University Students’ Perceptions of Decent Education
by Yangyang Deng, Ka Po Wong, Jin Yau Tsou and Yuanzhi Zhang
Educ. Sci. 2026, 16(5), 705; https://doi.org/10.3390/educsci16050705 - 30 Apr 2026
Viewed by 349
Abstract
Academic stress is prevalent among university students and affects their evaluation of educational environment quality, fairness, and supportiveness. Based on the challenge–hindrance stressor framework and transactional stress-coping model, this study explores how challenge and hindrance stressors (HSs) shape perceived decent education (DE), focusing [...] Read more.
Academic stress is prevalent among university students and affects their evaluation of educational environment quality, fairness, and supportiveness. Based on the challenge–hindrance stressor framework and transactional stress-coping model, this study explores how challenge and hindrance stressors (HSs) shape perceived decent education (DE), focusing on the mediating role of artificial intelligence use (AIUSE) and moderating effect of interpersonal interaction (II). Using partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 520 university students, the results show that both stressors positively predict AIUSE, which in turn improves perceived DE and mediates the stressor-DE relationship. II negatively moderates the AIUSE–DE link: the positive effect weakens as II increases. Moderated mediation analysis indicates that the indirect effects via AIUSE are only significant at low II levels. These findings highlight AI-enabled learning as an adaptive coping strategy and the necessity of integrating technological and interpersonal resources to enhance student well-being in higher education. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence on Teaching and Learning)
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29 pages, 1074 KB  
Article
Leveraging Generative AI for IELTS Preparation: Student Perspectives on Language Learning
by Michael James Day and Tracy Zhang
Educ. Sci. 2026, 16(5), 673; https://doi.org/10.3390/educsci16050673 - 23 Apr 2026
Viewed by 313
Abstract
This study investigates Chinese students’ perspectives on leveraging Generative Artificial Intelligence (GenAI) to enhance reading and writing abilities in preparation for the language learning and examination. 76 students enrolled in an online virtual learning environment (VLE) and participated in forum discussions prompted by [...] Read more.
This study investigates Chinese students’ perspectives on leveraging Generative Artificial Intelligence (GenAI) to enhance reading and writing abilities in preparation for the language learning and examination. 76 students enrolled in an online virtual learning environment (VLE) and participated in forum discussions prompted by questions relating to AI use and different study practices. Analysis identified 33 detailed forum posts written by and between students that specifically engaged in discussions concerning the use of AI to support English as an Additional Language (EAL) fluency, academic reading/writing skills, and IELTS-related skills development. This article presents an analysis of these contributions using thematic analysis. An inductive approach enabled the identification of key themes relating to students’ perceptions. Findings indicated that students appreciated AI’s capacity for personalised language learning, reading and writing practice while expressing reservations about overreliance on digital tools. The concept of Artificially Intelligent Mediated Counterbalance (AIMC) is proposed to capture students’ reported strategies for integrating AI tools with traditional study methods to maintain authentic language development. The article concludes by discussing the implications of AIMC for educators and policymakers seeking to support the responsible integration of AI into language education. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence on Teaching and Learning)
20 pages, 324 KB  
Article
From Familiarity to Criticality: Cultivating EFL Teachers’ AI Literacy Through an AI-Integrated Genre-Based Pedagogy
by Yuan Zhang and Mohd Ali Samsudin
Educ. Sci. 2026, 16(1), 150; https://doi.org/10.3390/educsci16010150 - 19 Jan 2026
Cited by 1 | Viewed by 1289
Abstract
Although AI integration in education is accelerating, professional development for Chinese EFL teachers remains predominantly techno-centric and lacks disciplinary specificity, resulting in persistently lower levels of AI literacy. To address this, this study used an eight-week AI-Integrated Genre-Based Pedagogy (AI-GBP) module grounded in [...] Read more.
Although AI integration in education is accelerating, professional development for Chinese EFL teachers remains predominantly techno-centric and lacks disciplinary specificity, resulting in persistently lower levels of AI literacy. To address this, this study used an eight-week AI-Integrated Genre-Based Pedagogy (AI-GBP) module grounded in Situated Learning Theory and TPACK. A quasi-experimental, explanatory, sequential, and mixed-methods design was employed to compare the AI-GBP module with a generic AI-literacy program among 64 Chinese college EFL teachers (n = 32 per group). Quantitative data from pre- and post-intervention surveys were analyzed using ANCOVA and MANCOVA to determine differences, while qualitative data from semi-structured interviews underwent thematic analysis. Quantitative findings indicated statistically significant gains in overall AI literacy as well as in its subcomponents, particularly regarding practical application and critical appraisal, among teachers in the AI-GBP group compared with the control group. Qualitative findings revealed certain developmental shift from tool-oriented AI use toward more critical and pedagogically relevant engagement, including teachers’ increased ability to identify model bias, contextual mismatch, and ‘agreeableness’ AI possesses. These findings suggest that embedding AI learning within more pedagogically and contextually appropriate situations (e.g., genre-based pedagogy) can support EFL teachers’ sustainable and critical integration and understanding of AI in their professional practice. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence on Teaching and Learning)

Other

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33 pages, 2053 KB  
Systematic Review
Generative AI in Art Education: A Systematic Review of Research Trends, Tool Applications, and Outcomes (2019–2025)
by Yihan Jiang, Yujiao Fan and Zifeng Liu
Educ. Sci. 2026, 16(1), 47; https://doi.org/10.3390/educsci16010047 - 30 Dec 2025
Cited by 2 | Viewed by 7374
Abstract
Generative artificial intelligence (GenAI) tools are transforming art education by enabling instant creation of textual, visual, audio, and multimodal outputs. This systematic review synthesizes research on GenAI applications in art education from January 2019 to August 2025. Following PRISMA 2020 guidelines, 19 peer-reviewed [...] Read more.
Generative artificial intelligence (GenAI) tools are transforming art education by enabling instant creation of textual, visual, audio, and multimodal outputs. This systematic review synthesizes research on GenAI applications in art education from January 2019 to August 2025. Following PRISMA 2020 guidelines, 19 peer-reviewed empirical studies across six databases (Web of Science, ScienceDirect, Springer, Taylor & Francis, Scopus, and ERIC) met inclusion criteria, which required clear pedagogical implementation with students or educators as active participants. Research accelerated from two studies in 2023 to 14 in 2025, with most studies examining higher education and East Asia contexts through mixed methods approaches and grounded in constructivist and cognitive learning theories. Text-to-image generation models (DALL-E, Midjourney, Stable Diffusion) and conversational AI (ChatGPT) were most frequently implemented across creative production, pedagogical scaffolding, and instructional design applications. Findings from this emerging body of research suggest that GenAI has the potential to improve learning achievement, creative thinking, engagement, and cultural understanding when integrated through structured pedagogical frameworks with intentional instructor design. However, these positive outcomes represent early-stage implementation trends in well-resourced contexts rather than broadly generalizable conclusions. Successful integration requires explicit instructional frameworks, clear ethical guidelines for human-AI collaboration, and evolved assessment methods. Full article
(This article belongs to the Special Issue The Impact of Artificial Intelligence on Teaching and Learning)
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