AI in higher education: Teaching and Learning in an AI-powered world

Artificial Intelligence (hereinafter AI) is a broad term referring to the use of ‘systems to interpret external data and to learn and use those learnings to achieve a specific goal or task via adaption’ (Sollosy & McInerney, 2022, p. 2). This approach describes AI-embedded technologies, which implement actions that used to require human intervention. As such, AI- technologies provide strategic benefits (e.g., automation, personalisation, minimising costs, facilitating decision-making) and allow for novel encounters between individuals and technology to take place in various areas.

Indeed, AI permeates individuals’ personal, professional, and educational lives. For instance, in the personal life realm AI helps individuals to combat loneliness and perform daily tasks (Marriott & Pitardi, 2024). In the professional realm AI outgrows new roles, replaces existing ones (e.g., entry-level jobs, Valcea et al., 2024) and requires new skills for entering an AI-powered world. Similarly, in the education realm, AI creates paradoxes (as discussed by Valcea et al., 2024) calling for (re)envisioning learning and teaching practices, such as practices in the assessment and curriculum landscape to meaningfully engage and enable upskilling of students.

AI transforms society and industries, as well as the core link between these two spheres, namely education, and calls for ‘…preparing students and learners for this transformation of work and society…’ (Bearman et al., 2023, p. 370). In education and more specifically, in Higher Education (hereinafter HE) AI is mostly known through the use of generative AI (e.g., ChatGPT). Extant research mainly focuses on the challenges generative AI creates (e.g., helps students to create content to tackle assessments) and argues for the necessity of new frameworks, policies, and guidelines around a sensible use of generative AI (Bearman et al., 2023; Farrelly & Baker, 2023; Richter et al., 2024; Valcea et al., 2024).

Thus, AI envisions a different landscape for teaching and learning in HE, by not only providing different possibilities to assist learning and teaching (e.g., personalised learning and tutoring system) but also by sometimes downplaying the deeply contextual, experiential and personal nature of learning (Bearman et al., 2023; Lindebaum & Fleming, 2024). As such, opportunities exist to unpack how such a transformation in HE reimagines teaching and learning practices and the relationship between students, business, HE and technology.

To this end, we welcome submissions focusing on advancing our understanding of the role of AI in enriching and transforming distinctive HE teaching and learning areas along with those that relate to the support/ administration of teaching and learning. The purpose of this special issue is to focus on the encounters between AI technology (e.g., Generative AI, chatbots, voice-assistants), HE and their implications for different stakeholders (e.g., students, HE and businesses).

Conceptual and empirical contributions to this special issue are invited to explore learning and teaching practices and relationships under which AI technology ‘fosters an environment of continuous learning, application, reflection, and adaptation’ (Richter et al., 2024, p.11). In doing so, we aim to offer insights onto meaningful, ethical and engaging working encounters in an AI-mediated learning and teaching environment.

Submissions addressing, but not restricted to, the following topics are encouraged:

• AI, placements and employability: AI changes the workplace landscape and creates new opportunities and challenges. Following relevant discourses in the literature as discussed above about preparing students for an AI-powered world, topics of interest in AI include placement and employability. This area incorporates the exploration of teaching and learning practices that can prepare students not only to work with AI but also to work with AI in a sensible way (Richter et al., 2024; Valcea et al., 2024). In this area the role of mindfulness in HE in terms of tackling negative feelings created by a rapidly changing working environment as well as aligning (placement) curricula with AI advancements can be further unpacked to understand how we can prepare students for the workforce.
• Students’ and educators’ experience with AI: This area invites for a better understanding of how students learn to work in an AI-mediated world and of what pedagogies are needed to meaningfully engage students in such a world. This topic area calls for going closer to student experiences and encounters with AI and how such experiences and encounters (re)shape student and teacher practices and cultivate learning communities (Barros et al., 2023; Bearman & Ajjawi, 2023; Gonsalves, 2024). Submissions in this area can also extent to consider the role of AI in supporting the community of educators; for example, how can AI-assisted marking processes support and/or disrupt the student educator relationship?
• AI as pedagogy: In line with the above areas, this area focuses on AI’s influence on a) students’ and teachers’ methods of learning and teaching, and b) approaches to learning styles, feedback and assessment strategies (Díaz & Nussbaum, 2024). Such an approach manifests a view of AI as pedagogy. Pedagogy of learning and teaching refers to how a lecturer delivers and assesses the taught curriculum. An AI pedagogy assumes an integration of AI’s strengths with human skills (e.g., critical thinking, ethical reasoning, and creativity, Passalacqua et al., 2024). We encourage papers to appreciate and debate these new frontiers of AI pedagogy as a useful process to leverage judgment, empathy, and collaborations, shaping effective future business leaders (Dai et al., 2024).
• AI, teaching and tutoring: This area refers to adaptive and personalised learning and tutoring environments occurring via the use of study guides, T-bots and tutoring support technologies. We call for further research into this area to tease out how AI engages students and personalises learning as well as what challenges it overcomes and what challenges such an AI use creates (Barros et al., 2023). Moreover, how students engage and interact with study guides, T-bots and tutoring support technologies warrant further examination since such measures contribute to diminishing the expertise paradox as approached by Valcea et al., 2024.
• AI and ethics in education: This area relates with what is described as sensible use of AI and is considered as central topic in AI and HE as it allows to overcome the paradoxes that AI creates in HE (Valcea et al., 2024). The area emphasizes ethical and safe use of AI in various disciplines and as a next step in various professions (Richter et al., 2024). Ethical considerations, like ethical skill development, clear guidelines for legitimate and non-legitimate use (e.g., plagiarism detection and discrimination) of AI warrant further exploration to cultivate students’ social awareness and understand students’ meaning making processes of AI and ethics (Acar, 2024; Akgun & Greenhow, 2022).
• AI and student support services: This topic area invites for further exploring the use of AI-technologies, such as automated digital assistants by professional service teams. The rising numbers of students enrolled in HE as well as the need to engage and retain students via student support services (along with other methods) pose a need for the use of AI technologies in student support services (Pérez et al., 2020). How can AI in student support services be a source that facilitates (personalised) access to student services as well as personalisation of student learning pathway?

Submission Requirements:

The length of manuscripts submitted varies depending on the type of manuscript.

• Research Papers should be no longer than 6,000 words and not shorter than 3,500.
• Research Notes should be no longer than 2,500 words and not shorter than 1,000.
• Case Studies should be no longer than 3,500 words and not shorter than 2,000.
• Book Reviews should be no longer than 1,500 words and not shorter than 1,000.
• Practitioner/industry Viewpoints should be no longer than 1,500 words and not shorter than 500.

All submissions must strictly follow the guidelines for the Journal of Contemporary Education Theory & Research. These are available here

Please note the requirements for all submissions to include author’s and co-authors’ – if any – ORCID (compulsory for all submissions since volume 4, issue 1, 2020). Please read the publication ethics and publication malpractice policy here and the guidelines regarding plagiarism, authorship and referencing style prior to submitting your manuscript.

Manuscripts should be submitted online using the eReviewer Submission Portal for the Journal of Contemporary Education Theory & Research here

Informal queries regarding guest editors’ expectations or the suitability of specific research topics should be directed to the Special Issue Editors:

Editor Contact Details

• Georgia Stavraki stavrakig@ihu.gr
• Ioanna Anninou i.anninou@surrey.ac.uk
• Jashim Khan     j.a.khan@surrey.ac.uk

Key Deadlines:

• The online system will be open for submissions to this issue from 1^st May 2025.
• The closing date for submissions is 11 July 2025.

Technical queries about submissions can be referred to the Editorial Office: jcetr@education-master.gr

References

Acar, O. A. (2024). Commentary: Reimagining marketing education in the age of generative AI. International Journal of Research in Marketing, 41(3), 489-495.

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440.

Barros, A., Prasad, A., & Śliwa, M. (2023). Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604.

Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160-1173.

Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369-385.

Dai, Y., Lin, Z., Liu, A., & Wang, W. (2024). An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study. British Journal of Educational Technology, 55(1), 417-434.

Díaz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers & Education, 105071.

Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109.

Gonsalves, C. (2024). Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher education. Assessment & Evaluation in Higher Education, 1-15.

Lindebaum, D., & Fleming, P. (2024). ChatGPT undermines human reflexivity, scientific responsibility and responsible management research. British Journal of Management, 35(2), 566-575.

Marriott, H. R., & Pitardi, V. (2024). One is the loneliest number… Two can be as bad as one. The influence of AI Friendship Apps on users’ well‐being and addiction. Psychology & Marketing, 41(1), 86-101.

Passalacqua, M., Pellerin, R., Yahia, E., Magnani, F., Rosin, F., Joblot, L., & Léger, P. M. (2024). Practice with less AI makes perfect: partially automated AI during training leads to better worker motivation, engagement, and skill acquisition. International Journal of Human–Computer Interaction, 1-21.

Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549-1565.

Richter, S., Giroux, M., Piven, I., Sima, H., & Dodd, P. (2024). A Constructivist Approach to Integrating AI in Marketing Education: Bridging Theory and Practice. Journal of Marketing Education, 02734753241288876.

Sollosy, M., & McInerney, M. (2022). Artificial intelligence and business education: What should be taught. The International Journal of Management Education, 20(3), 100720.

Valcea, S., Hamdani, M. R., & Wang, S. (2024). Exploring the impact of ChatGPT on business school education: Prospects, boundaries, and paradoxes. Journal of Management Education, 48(5), 915-947.