This PhD course explores the tensions between teacher professionalisation and artificial intelligence (AI) in schools. It is organised as a Northern European intensive school under the auspices of the Circle U. Teacher Education Network (TENet), with academic communities from the University of Oslo’s Department of Teacher Education and School Research, King’s College London, and Humboldt Universität zu Berlin.

Most existing theories of teacher professionalisation were developed before the generative AI era. Classical frameworks – from Schön’s reflective practitioner to competence-oriented models such as DigCompEdu and TPACK – were not designed for situations in which an AI system can produce plausible teaching materials, provide feedback to pupils, or simulate subject-matter expertise. The course provides PhD candidates with an opportunity to examine these theoretical gaps and position their own research within this emerging field.

Thematic structure

The course is organised into three interrelated blocks that build upon one another.

The central premise is that most theories of teacher professionalisation are insufficient to address the challenges posed by generative AI. This block addresses:

  • The theoretical gap between professionalisation theory and epistemic uncertainty;
  • Professional identity and preparedness for reconstruction, drawing on Kelchtermans and Day & Gu;
  • Classical frameworks, including Schön, TPACK, and DigCompEdu, as well as critical AI literacy;
  • Sociological theories of professions, including Abbott and Freidson;
  • The meaning of expertise when AI systems reproduce the outward features of professional knowledge.

This block is connected to school practice and anchors abstract didactic theory in the AI-saturated classroom. Key questions include:

  • How does generative AI affect the foundations of assessment in different school subjects?
  • What do authorship, source use, and epistemic credibility mean when AI produces text?
  • How can subject teachers respond to AI in their educational and didactic decision-making?

The block includes an optional subject-didactic specialisation in which participants may examine the use of AI within their own subject areas.

This block grounds the theoretical perspectives in lived experiences from schools. Topics include:

  • The school as an organisation and the adoption of AI;
  • Teachers as gatekeepers between institutional mandates and professional judgement;
  • Practice and professionalisation, including what practice-based teacher education can and cannot achieve;
  • National and European governance and regulatory frameworks, including the EU AI Act and Norwegian digitalisation policy.

The overarching theme connecting all three blocks is the following question:

What constitutes sustainable professionalization when the technological landscape is constantly changing?

Overall Research Question

What kind of professional identity, subject knowledge, and didactic judgement do teachers need in order to remain professionally effective over time in an era shaped by generative AI?

Learning outcomes

Upon successful completion of the course, the candidate will have achieved the following learning outcomes:

The candidate will have:

  • Advanced knowledge of current research on teacher professionalisation, professional identity, and artificial intelligence in education;
  • Insight into classical and critical frameworks for teacher professionalism, including Schön, TPACK, DigCompEdu, Abbott, and Freidson, and their limitations in the AI era;
  • Insight into comparative, national, and European approaches to AI governance in education, including the EU AI Act and Nordic digitalisation policies;
  • Knowledge of central debates concerning subject-specific didactics, assessment, and authorship in the context of generative AI;
  • Knowledge of schools as organisations and of AI adoption processes at the institutional level.

The candidate will be able to:

  • Critically analyse the influence of AI on teachers’ professional identities and professional knowledge;
  • Analyse subject-didactic practice in relation to AI tools in specific school subjects;
  • Assess and apply relevant theories of professionalisation and professional identity to their own research questions;
  • Position their own research within ongoing academic debates on AI and teacher professionalism;
  • Identify and discuss the ethical dimensions of AI in professional educational practice

The candidate will be able to:

  • Contribute with academic precision to scholarly debates on teacher professionalisation in the AI era;
  • Collaborate across disciplinary and institutional boundaries on research concerning AI and teachers’ work;
  • Use insights from international comparisons to enrich national and local understandings;
  • Reflect upon and communicate sustainable conceptions of professionalism in response to technological pressure for change.

Teaching

The course is structured into three phases:

Approximately one week before the intensive programme, participants complete asynchronous and synchronous online activities. They read the core literature, approximately 40 pages, and participate in one joint online seminar lasting two hours.

The purpose of this phase is to establish a shared frame of reference and to introduce the participants’ research projects.

The intensive programme takes place over five days, from 1 to 5 March 2027, at the University of Oslo’s Department of Teacher Education and School Research in Oslo.

The daily programme consists of lectures, seminars, working groups, and guest lectures by researchers from King’s College London and Humboldt University of Berlin. Day 3 includes a school visit to a local lower or upper secondary school. Participants present their research projects in plenary seminars and provide peer feedback on one another’s work.

Each participant submits an individual written assignment of 3,000–5,000 words within four weeks after Phase 2. Participants must also submit a one-page response providing peer feedback on a fellow student’s manuscript draft within the same deadline.

Examination and Assessment

  • Pass / Fail

Requirements for Passing

All three of the following requirements must be fulfilled:

  1. Attendance:  A minimum of 80% attendance during the intensive programme, in addition to participation in the preparatory seminar.
  2. Active seminar participation: Two active seminar contributions during the intensive programme:
    • presentation of the participant’s own research project; and
    • peer assessment of another participant’s project.

Individual written assignment: An academic essay or scholarly paper of up to 5,000 words, submitted within four weeks after the end of the course.

Reading List

The reading list is divided into two sections:

  • Core Reading: compulsory, approximately 160 pages;
  • Extended Reading: recommended for further study, approximately 240 pages.

Page numbers are provided where available; all other works are assigned in full.

Classical Frameworks and the Theoretical Gap

  • Biesta, G. (2015). What is education for? On good education, teacher judgement, and educational professionalism. European Journal of Education, 50(1), 75–87. https://doi.org/10.1111/ejed.12109
  • Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
  • Redecker, C., & Punie, Y. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. EUR 28775 EN. Publications Office of the European Union. https://doi.org/10.2760/159770 

Professional Identity

  • Kelchtermans, G. (2009). Who I am in how I teach is the message: Self-understanding, vulnerability and reflection. Teachers and Teaching, 15(2), 257–272. https://doi.org/10.1080/13540600902875332
  • Day, C., & Gu, Q. (2010). The new lives of teachers. Routledge. (Kapitler 1, 2 og 7; ca. 60 sider)

AI Literacy and Critical Understanding of Technology

Generative AI & Assessment

  • Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  • Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic literature review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370

Writing, Authorship, and Multiliteracies

  • Warschauer, M., Tseng, W., Yim, S., Webster, T., Jacob, S., Du, Q., & Tate, T. (2023). The affordances and contradictions of AI-generated text for writers in higher and secondary education. Journal of Writing Research, 15(1), 1–36. https://doi.org/10.17239/jowr-2023.15.01.01

Regulations & governance (EU AI Act)

  • European Parliament & Council of the EU. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the EU. Annex III (høyrisiko-klassifisering utdanning) + Artikkel 5 (forbudte praksiser). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689

School development & AI Use

  • Fullan, M. (2016). The new meaning of educational change (5th ed.). Teachers College Press. (Del I: kap. 1–2; ca. 50 sider)
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/our-work/aied/ (del II, kap. 4–5)

Norwegian / Nordic context

  • Abbott, A. (1988). The system of professions: An essay on the division of expert labor. University of Chicago Press. (Kap. 1–2: »Hva er et profesjonssystem?« og »Profesjonell arbeidstransaksjon«; ca. 60 sider)
  • Freidson, E. (2001). Professionalism: The third logic. University of Chicago Press. (Kap. 1 og 8; ca. 45 sider)
  • Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books. (Kap. 1–2; ca. 50 sider)
  • Kelchtermans, G., & Ballet, K. (2002). The micropolitics of teacher induction: A narrative-biographical study on teacher socialization. Teaching and Teacher Education, 18(1), 105–120. https://doi.org/10.1016/S0742-051X(01)00053-1
  • European Commission. (2022). Ethical guidelines on the use of artificial intelligence and data in teaching and learning for educators. Publications Office of the European Union. https://doi.org/10.2766/153756
  • Vrikki, M., Warwick, P., Vermunt, J. D., Mercer, N., & Van Halem, N. (2019). Teacher learning in the context of Lesson Study: A video-based analysis of teacher discussions. Teaching and Teacher Education, 78, 68–80. https://doi.org/10.1016/j.tate.2018.11.011

Contact

For questions related to funding, ECTS recognition, mobility, or other course-related matters, please contact the Circle U. office at your home university:

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Apply by 5 January 2027

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