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The following are the slides and synopsis of my paper, The Ghost in the Feedback Loop: AI, Academic Praxis, and the Decomposition of Disciplinary Boundaries, presented at the International Society for the Scholarship of Teaching and Learning Annual Conference (ISSOTL 2025), in the University of Canterbury, Christchurch, New Zealand.

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As AI tools transform content creation, academic practices, and disciplinary boundaries are under pressure. Drawing on Actor-Network Theory (ANT), this paper explores AI tools as nonhuman actants shaping authorship, assessment, and pedagogical authority (Fenwick & Edwards, 2010, 2012). ANT challenges humanist binaries such as human/machine by inviting us to view education as an assemblage of human and nonhuman actors co-constructing the learning environment (Landri, 2023).

Within this framework, AI systems used in formative assessment, ranging from feedback automation to individual AI tutoring, reshape pedagogic feedback loops, influence student agency, and reconfigure the distribution of cognitive labor in classrooms (Hopfenbeck et al., 2024; Zhai & Nehm, 2023). As students increasingly co-produce knowledge with AI (Wang et al., 2024), this paper argues that the pedagogical focus must shift from control and containment to composition and negotiation. Using case studies from large international cohorts, the paper examines how AI alters feedback loops, shifts student agency, and challenges discipline-specific praxis. What new academic identity and ethics forms must emerge in this hybrid landscape?

Recent studies suggest that generative AI can reduce perceived cognitive effort while paradoxically elevating the problem-solving confidence of knowledge workers (Lee et al., 2025). When strategically embedded in formative assessment practices, AI can scaffold students’ movement up Bloom’s taxonomy from comprehension to application, analysis, and synthesis, especially among international and multilingual cohorts (Walter, 2024; Klimova & Chen, 2024).

In this context, this paper argues for a radical reframing of educational assessment design. Instead of resisting machinic participation, educators must critically reassemble pedagogical networks that include AI as epistemic collaborators (Liu & Bridgeman, 2023). By unpacking the socio-material dynamics of AI-infused learning environments, ANT offers a pathway for understanding and designing inclusive, dynamic, and ethically aware pedagogical futures. This includes rethinking agency as distributed across human and nonhuman nodes, assessment as an ongoing negotiation, and learning environments as fluid, adaptive ecologies shaped by constant assemblage and reassemblage rather than fixed instructional designs or isolated learner outcomes.

References
Fenwick, T., & Edwards, R. (2010). Actor-Network Theory in Education. Routledge. https://doi.org/10.4324/9780203849088

Fenwick, T., & Edwards, R. (Eds.). (2012). Researching Education Through Actor-Network Theory. Wiley-Blackwell. https://doi.org/10.1002/9781118275825

Hopfenbeck, T. N., Zhang, Z., & Authors (2024). Challenges and opportunities for classroom-based formative assessment and AI: A perspective article. International Journal of Educational Technology, 15(2), 1–28.

Klimova, B., & Chen, J. H. (2024). The impact of AI on enhancing students’ intercultural communication, competence at the university level: A review study. Language Teaching Research Quarterly, 43, 102-120. https://doi.org/10.32038/ltrq.2024.43.06

Landri, P. (2023). Ecological materialism: redescribing educational leadership through Actor-Network Theory. Journal of Educational Administration and History, 56, 84 – 101. https://doi.org/10.1080/00220620.2023.2258343.

Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581234

Liu, D. & Bridgeman, A. (2023, July 12). What to do about assessments if we can’t out-design or out-run AI? University of Sydney. https://educational-innovation.sydney.edu.au/teaching@sydney/what-to-do-about-assessments-if-we-cant-out-design-or-out-run-ai/

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Syst. Appl., 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Zhai, X., & Nehm, R. H. (2023). AI and formative assessment: The train has left the station. Journal of Research in Science Teaching, 60(6), 1390–1398. https://doi.org/10.1002/tea.21885

Having been in and out of academy for the last, scary number, 16 years, both as a student and lecturer, I have a long list of convictions on what constitutes good learning practice. These have formed, without exception, as a result of frontal clashes with the common-sense notions of good learning practice in higher education. Sitting in class and listening to a lecturer, working in groups, taking notes and /or memorizing lecture notes, passing exams (my favorite) – the list is familiar to everyone with a degree or the aspirations to get one. Bottom line is that the pernicious notions that learning happens in organized time-blocks, and that the best learning practices manifest themselves through instant-recall have contributed tremendously towards the boxed-content assembly line we call higher education. What we produce is students capable of remembering the answer to a question, who excel at obediently taking exams.

There is an alternative to learning though, to my knowledge first charted by Vygotsky, who used the metaphor of scaffolding to describe it. I try and shape my subjects in accordance with this, constructivist, approach whereby the learning process happens dynamically, in an open location (that is, the students decides where), and is assessed through the regular, dynamic production of content, with the separate assessments integrated into a higher-order whole. For example, students produce weekly content consisting of research, reflection, and mini fact-finding missions; they read and comment on each-other’s content; they may use the annotated sources from their fact-finding missions as building blocks towards a group project, or a longer research and reflection piece; they may use that piece to look back and reflect on the issues they identified, etc. The intention is to create a modular, scaffolding-like platform of content production and assessment which can be tailored towards particular topics, problems, and end-tasks.

I have been looking for a new metaphor to describe this approach, and today found it in the work of Robert Bjork from the UCLA Learning and Forgetting Lab, who coins the term ‘interleaving’ to describe the effect of engaging with a topic or a problem on several different levels of intensity simultaneously. The problem is how to design subject materials relying on constant feedback and reflection, so as to maximize the recall function of memory and the relaitonality of knowledge.

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The Ghost in the Feedback Loop: AI, Academic Praxis, and the Decomposition of Disciplinary Boundaries

Learning through interleaving