Topic Editors

Faculty of Education Sciences, University of Seville, 41013 Sevilla, Spain
Didactics and School Organisation, Faculty of Education and Sport Sciences-Melilla, Universidad de Granada, 52005 Melilla, Spain

AI Trends in Teacher and Student Training

Abstract submission deadline
11 January 2026
Manuscript submission deadline
11 March 2026
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3442

Topic Information

Dear Colleagues,

Artificial intelligence is transforming education at all levels, from early childhood education to university. This Topic seeks to explore the latest trends and developments in the application of AI in education for teachers and students, showcasing case studies, innovative practices and emerging technologies. A variety of topics related to AI in teacher and student education will be addressed, including, but not limited to, AI-powered teaching tools, student data analysis, research advances, and the ethical implications of AI integration. In this regard, it is important to uncover both positive and negative consequences on society. By examining how AI is reshaping academic environments, this theme seeks to contribute to a deeper understanding of how educational institutions can leverage AI to improve educational outcomes and operational efficiency.

Potential topics include, but are not limited to, the following:

  • AI-powered personalized learning platforms;
  • Use of AI in the analysis and prediction of student performance;
  • AI-driven research innovations in various academic areas;
  • Ethical concerns and challenges in implementing AI in education;
  • AI administrative tools to improve the management and efficiency of educational institutions;
  • The role of AI in fostering accessibility and inclusion in education;
  • The impact of AI on the future of teaching and learning methodologies across different educational levels;
  • Case studies on the successful integration of AI in educational environments.

Dr. José Fernández-Cerero
Dr. Marta Montenegro-Rueda
Topic Editors

Keywords

  • AI
  • teacher training
  • ICT
  • higher education
  • method
  • inclusive education
  • academic performance

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai
5.0 6.9 2020 20.7 Days CHF 1600 Submit
Computers
computers
4.2 7.5 2012 16.3 Days CHF 1800 Submit
Education Sciences
education
2.6 5.5 2011 29.2 Days CHF 1800 Submit
Societies
societies
1.6 3.0 2011 34.4 Days CHF 1400 Submit
Future Internet
futureinternet
3.6 8.3 2009 17 Days CHF 1600 Submit
Technologies
technologies
3.6 8.5 2013 21.8 Days CHF 1600 Submit

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Published Papers (3 papers)

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19 pages, 433 KiB  
Article
A TAM-Based Analysis of Hong Kong Undergraduate Students’ Attitudes Toward Generative AI in Higher Education and Employment
by Kam Cheong Li, Grace Ho Lan Chong, Billy Tak Ming Wong and Manfred Man Fat Wu
Educ. Sci. 2025, 15(7), 798; https://doi.org/10.3390/educsci15070798 - 20 Jun 2025
Viewed by 253
Abstract
This study explores undergraduate students’ attitudes towards generative AI tools in higher education and their perspectives on the future of jobs. It aims to understand the decision-making processes behind adopting these emerging technologies. A multidimensional model based on the technology acceptance model was [...] Read more.
This study explores undergraduate students’ attitudes towards generative AI tools in higher education and their perspectives on the future of jobs. It aims to understand the decision-making processes behind adopting these emerging technologies. A multidimensional model based on the technology acceptance model was developed to assess various factors, including perceived ease of use, perceived benefits, perceived concerns, knowledge of AI, and students’ perceptions of generative AI’s impact on the future of jobs. Data were collected through a survey distributed to 93 undergraduate students at a university in Hong Kong. The findings of multiple regression analyses revealed that these factors collectively explained 23% of the variance in frequency of use [(F(4, 78) = 5.89, p < 0.001), R2 = 0.23]. Perceived benefits played the most significant role in determining frequency of use of generative AI tools. While students expressed mixed attitudes toward the role of AI in the future of jobs, those who voiced concerns about AI in education were more likely to view generative AI as a potential threat to job availability. The results provide insights for educators and policymakers to promote the effective use of generative AI tools in academic settings to help mitigate risks associated with overreliance, biases, and the underdevelopment of essential soft skills, including critical thinking, creativity, and communication. By addressing these challenges, higher education institutions can better prepare students for a rapidly evolving, AI-driven workforce. Full article
(This article belongs to the Topic AI Trends in Teacher and Student Training)
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14 pages, 204 KiB  
Article
Perceptions of AI in Higher Education: Insights from Students at a Top-Tier Chinese University
by Yi Yan, Bin Wu, Jiaqi Pi and Xiaowen Zhang
Educ. Sci. 2025, 15(6), 735; https://doi.org/10.3390/educsci15060735 - 12 Jun 2025
Viewed by 820
Abstract
While AI integration in higher education has transformative potential, existing studies may not fully capture the unique socio-cultural and institutional contexts of top-tier universities in China. This study investigates students’ perceptions of AI utilization at a leading Chinese university, drawing on the Technology [...] Read more.
While AI integration in higher education has transformative potential, existing studies may not fully capture the unique socio-cultural and institutional contexts of top-tier universities in China. This study investigates students’ perceptions of AI utilization at a leading Chinese university, drawing on the Technology Acceptance Model (TAM). Quantitative data were collected via a 5-point Likert scale questionnaire (n = 253), complemented by open-ended qualitative responses. Results revealed that while they viewed AI as useful for enhancing efficiency and easy to use, concerns about content accuracy, over-reliance, and ethical issues persisted. Their high interest in AI contrasted with lower self-assessed proficiency, highlighting a gap between enthusiasm and competence. Institutional support significantly motivated adoption, whereas social influence played a lesser role. Students valued AI’s support in language learning, writing, research, and programming but noted its limitations in complex problem-solving. They also called for human-centric AI tools offering emotional support and personalized guidance. These findings may offer educators, policymakers, and AI developers valuable insights to address students’ concerns and optimize learning experiences in competitive academic environments. Full article
(This article belongs to the Topic AI Trends in Teacher and Student Training)
22 pages, 699 KiB  
Article
Integration of Artificial Intelligence in K-12: Analysis of a Three-Year Pilot Study
by Boško Lišnić, Goran Zaharija and Saša Mladenović
AI 2025, 6(3), 49; https://doi.org/10.3390/ai6030049 - 1 Mar 2025
Viewed by 1708
Abstract
A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications [...] Read more.
A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications (RIWA) module. Twelve schools in Split-Dalmatia County in the Republic of Croatia participated, resulting in 112 successfully completed student projects. The program consisted of two phases: (1) theoretical instruction with examples and exercises, and (2) project-based learning, where students developed final projects using JavaScript and the ml5.js library. The study employed project analysis and semi-structured student interviews to assess learning outcomes. Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula. Full article
(This article belongs to the Topic AI Trends in Teacher and Student Training)
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