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
