Editorial

DOI: 10.5281/zenodo.20714562

Abstract

The aim of this introduction and special issue is to explore Artificial Intelligence (hereinafter AI) in higher education and examine its implications for teaching and learning in an AI-powered world. This special issue contains five articles that discuss themes such as the opportunities and challenges, biases and divides, and teachers’ perceptions of AI: their hopes, concerns, and ethics. The issue concludes with a forward-looking ‘metacognitive creative destruction’ theme recognizing how teaching and learning evolves in an AI-powered world. Through this special issue we attempt to further enrich current research on AI in higher education by balancing its transformative potential with a consideration of its ethical, reflective, and pedagogical challenges.

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Anninou, I., Stavraki, G.& Khan, J. (2025). Editorial – Artificial intelligence in higher education: Teaching and learning in an AI-powered world. Journal of Contemporary Education Theory & Research, 9(2), 1–9. https://doi.org/10.5281/zenodo.20714562

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Artificial Intelligence in contemporary higher education: Opportunities and challenges

Published online: JUNE 2026

Georgios Patsiaouras, School of Business, University of Leicester, UK
Sylvian Jesudoss, School of Business, University of Leicester, UK
John Balabanis, School of Business, University of Leicester, UK

DOI: 10.5281/zenodo.20714766

Abstract

This paper seeks to elaborate on the opportunities and challenges stemming from the increased use of AI in Higher Education. First, we critically analyse some negative consequences from AI misuse in HE with emphasis on the threats of algorithmic bias, and the rise of educational and digital inequalities. Second, we highlight the opportunities stemming from AI use in HE around the automation of tasks, personalization and accessibility. Finally, we conclude by providing a critical discussion about the future use of AI in universities and Higher Education and we indicate areas for future research and critical considerations of AI applications across different parts of the globe.

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Patsiaouras, G., Jesudoss, S.& Balabanis, J. (2025). Artificial Intelligence in contemporary higher education: Opportunities and challenges. Journal of Contemporary Education Theory & Research, 9(2), 10–15. https://doi.org/10.5281/zenodo.20714766

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Artificial intelligence in language education: Teachers’ fears, hopes and solutions

Published online: JUNE 2026

Satu-Maarit Korte, University of Helsinki, Finland
Kim Youngsang, Sungkyunkwan University, South Korea
Keijo Sipilä, University of Lapland, Finland
Lixun Wang, The Education University of Hong Kong, Hong Kong

DOI: 10.5281/zenodo.20714911

Abstract

The potential of Artificial Intelligence (AI) to improve educational outcomes, especially in language learning, has been widely discussed. Yet, the lived realities, expectations, and concerns educators have about AI’s role in schools remain unexplored. This commentary looks at a comparative study involving 636 second language teachers from Brazil, Finland, Hong Kong, and South Korea. It examines how educators perceive and use AI. The results show generally positive attitudes but highlight differences in self-efficacy, usage frequency, and pedagogical value. These differences are shaped by regional policies and digital resources. Understanding these insights is important for guiding teacher training, policy design, and the development of ethical, inclusive tools that respond to local needs.

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Korte, S.-M., Kim, Y., Sipilä, K. T.& Wang, L. (2025). Artificial intelligence in language education: Teachers’ fears, hopes and solutions. Journal of Contemporary Education Theory & Research, 9(2), 16–20. https://doi.org/10.5281/zenodo.20714911

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Generative artificial intelligence and ethics: University faculty, staff, and students’ views

Published online: JUNE 2026

Chelsey M. Bahlmann Bollinger, James Madison University, USA
Alexa M. Quinn, James Madison University, USA
Kara M. Kavanagh, James Madison University, USA
Rabia Lieber, James Madison University, USA
Michele Estes, James Madison University, USA
Joi Merritt, James Madison University, USA

DOI: 10.5281/zenodo.20715136

Abstract

This exploratory study examines the ethical viewpoints, and perspectives of faculty, staff, and students concerning Gen-erative Artificial Intelligence (GAI) technology tools at a mid-Atlantic university as of August 2023. This qualitative study is guided by Diffusion of Innovation Theory, and the Eight Key Questions Ethical Reasoning Framework, and aims to document the early reactions of the academic community to GAI’s introduction into educational contexts. A university wide survey was distributed to document the perceptions, concerns, and expectations of the academic community with respect to GAI as it emerged. The results indicated a range of responses to GAI from optimism that GAI could be used to augment teaching and provide additional support to students, to concern over issues such as academic honesty, bias, privacy of data, and devaluation of authentic learning experiences. The study indicates the complexity of incorporating GAI into higher education. The study establishes a foundation for future studies on the sociotechnical implications of GAI, and includes a University GAI Ethics Guidance Checklist to assist in the ethical implementation of GAI.

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Bahlmann Bollinger, C. M., Quinn, A. M., Kanavagh, K. M., Lieber, R., Estes, M.& Merritt, J. (2025). Generative artificial intelligence and ethics: University faculty, staff, and students’ views. Journal of Contemporary Education Theory & Research, 9(2), 21–42. https://doi.org/10.5281/zenodo.20715136

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Artificial intelligence and teaching practice: Pre-service teachers’ perceptions in Greece

Published online: JUNE 2026

Konstantina Tsoli, National and Kapodistrian University of Athens, Greece
Chara Papoutsi, National and Kapodistrian University of Athens, Greece
Eirini Kontostavlou, National and Kapodistrian University of Athens, Greece
Ioanna Katsiampoura, National and Kapodistrian University of Athens, Greece
George Koutromanos, National and Kapodistrian University of Athens, Greece
Constantine Skordoulis, National and Kapodistrian University of Athens, Greece
Thomas Babalis, National and Kapodistrian University of Athens, Greece

DOI: 10.5281/zenodo.20715374

Abstract

The objectives of the study, which was conducted in 2025, were both to explore 344 pre-service teachers’ perceptions and attitudes on Artificial Intelligence (AI) and ways of implementing it in their Teaching Practice, based on the TPACK mod-el, and examine the differences regarding gender and age, through a questionnaire which was designed based on the tool developed by Chounta et al. (2022). According to the results, pre-service teachers at the Department of Pedagogy and Primary Education of the National and Kapodistrian University of Athens (NKUA) seem to be familiar with the concept and have used relevant applications. However, the use and the aim of using it during their Teaching Practice seems to be limited. The results highlight the necessity for preparing future teachers to address educational challenges through the utilization of AI tools.

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Tsoli, K., Papoutsi, C., Kontostavlou, E., Katsiampoura, I., Koutromanos, G., Skordoulis, C.& Babalis, T. (2025). Artificial intelligence and teaching practice: Pre-service teachers’ perceptions in Greece. Journal of Contemporary Education Theory & Research, 9(2), 43–58. https://doi.org/10.5281/zenodo.20715374

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Disruptive learning in the artificial intelligence era: A metacognitive crea-tive destruction model for educational innovation

Published online: JUNE 2026

Spyros Avdimiotis, International Hellenic University, Greece
Ioannis Konstantinidis, International Hellenic University, Greece

DOI: 10.5281/zenodo.20715467

Abstract

This paper introduces the Metacognitive Creative Destruction (MCD) Model to explain how educational disruptive inno-vation can emerge through cognitive deconstruction and reconstruction processes in Artificial Intelligence (AI) enhanced environments. While prior research explores metacognition, unlearning, and educational change separately, an integrat-ed model explaining how individuals intentionally dismantle outdated knowledge structures to foster adaptive and in-novative learning has not emerged, yet. Grounded on Schumpeter’s theory of “Creative Destruction”, the MCD model conceptualizes “Destruction” at the cognitive level, where AI acts as a metacognitive spark for reflective disruption and epistemic revision. The model, which was developed to monitor the educational transition towards disruptive innova-tion, includes thirteen (13) empirically tested variables grouped into core adaptive capacities, contextual enablers / inhib-itors, and learning outcomes. To validate the concept, quantitative primary research took place and data from 1,498 cur-rent and alumni students-educators of the “Administration and Management of Education Units” postgraduate program of the International Hellenic University in Greece were analyzed using structural equation modeling (SEM). Results confirmed the model’s robustness and predictive validity towards disruptive educational innovation. The findings sug-gest that fostering in a recursive learning cycle, metacognitive flexibility, unlearning propensity, epistemic humility, the incorporation of AI as cognitive and reflective partner and psychological readiness for revision may significantly en-hance knowledge reconstruction and disruptive innovation in education systems. In a sentence, the study aims to share with academia, both a validated measurement model and a theoretical lens for understanding transformational learning towards disruptive innovation, in the AI era.

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Avdimiotis, S.& Konstantinidis, I. (2025). Disruptive learning in the artificial intelligence era: A metacognitive crea-tive destruction model for educational innovation. Journal of Contemporary Education Theory & Research, 9(2), 59–81. https://doi.org/10.5281/zenodo.20715467

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