Conference Agenda
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10 SES 07 C: Teacher Agency in the Age of AI: Negotiating Pedagogy, Knowledge, and Professional Practice
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10. Teacher Education Research
Paper Can AI Mediate Teacher Agency? Preservice Teachers’ Engagement With Cogniti AI in Initial Teacher Education Pontificia Universidad Católica de Chile, Chile Presenting Author:The rapid integration of GenAI into teacher education programs has created a critical imperative to explore its emerging impact on how PSTs develop professional skills and make pedagogical judgements. This paper reports on a pedagogical innovation that uses Cogniti AI as a reflective partner in a classroom research course designed for preservice English teachers. The study examined the tensions generated through PSTs navigating two distinct modes of engagement: a "confirmatory" mode oriented toward validation and reassurance, and an "exploratory" mode oriented toward generating alternatives and broadening perspectives. By positioning AI as a mediating tool, this study analysed how PSTs moved from passive recipients to active collaborators who exercised transformative agency—the capacity to reconceptualise existing frames of action to adopt new strategies to transform their classroom practice. Research Questions
Research Objectives
Theoretical Framework Given this research focus, the study is conceptually framed by Cultural-Historical Activity Theory (CHAT), which understands learning as a culturally and historically situated process mediated by social and material tools. From a CHAT perspective, human agency is understood not as an innate trait but as an intentional, deliberate set of actions taken to transform social reality. Further, central to this research is the related conception of transformative agency, which aligns with the drive of expansive learning central to CHAT theorising (Engeström, 1987). This orientation proposes that in confronting contradictions between the current and desired state, actors can move beyond existing knowledge and practices to create new expansive actions. Consistent with this conception, GenAI is understood in this study as a mediating artefact—that is, not merely a tool that facilitates a task—but instead something that fundamentally disrupts the mental processes and professional development of PSTs. In addition, we used positioning theory to understand the discursive shifts that occur during this mediation. As PSTs interact with AI tools, they necessarily need to negotiate their professional authority, in essence, "positioning" themselves as either dependent users seeking validation or as empowered researchers (i.e., using the GenAI as a sounding board to solve complex pedagogical problems). Through the CHAT lens, we sought to understand PST interactions with GenAI as a site of professional transformation where agency is fostered through the resolution of conflicts between current practices and new affordances emerging through GenAI.
Methodology, Methods, Research Instruments or Sources Used This research adopts a qualitative, interpretive case study design to capture the nuances of PSTs’ interactions with AI tools. The participants were preservice English teachers enrolled in a university-based teacher education program who engaged in classroom-based research tasks supported by Cogniti AI. To ensure a deep and triangulated understanding of their experiences, data were collected from multiple sources over a semester-long intervention period. The primary data source was AI Conversation Logs, which provided comprehensive insights into the interactions between PSTs and the AI. These logs provided an objective basis for understanding how students iterated their prompts and the depth of analytical questioning adopted. This was complemented by two other data sources—Student Reports and Reflective Journals—where PSTs gave meaning to their subjective experiences, capturing their emerging engagement with the affordances of Cogniti AI. Finally, a series of focus group interviews explored the collective tensions and shifts in pedagogical roles that occurred when AI was introduced into the design of the classroom-based research process. Data analysis followed a multi-layered approach, using thematic analysis to identify patterns of engagement. This centred on identifying evidence of changes in agency and expansive learning actions—such as questioning, analysing, and modelling—to distinguish between stances of dependence and empowerment. Furthermore, positioning analysis was used to analyse how PSTs and teacher educators discursively constructed their authority and roles whilst using Cogniti AI in decision-making processes. Conclusions, Expected Outcomes or Findings The findings demonstrate a progressive reconfiguration of PSTs’ professional agency in their engagement with Cogniti AI, unfolding across four interrelated stages: embryonic, emerging, evolving, and expanded. In the embryonic stage, PSTs engaged with the tool in largely confirmatory ways, focusing on technical prompt refinement, accuracy checks, and validation of pre-existing ideas. This mode of engagement played an important pedagogical role, providing psychological safety and reducing uncertainty in early research work, yet agency remained primarily procedural. As PSTs progressed, their engagement shifted through emerging and evolving stages toward more exploratory and reflective uses of Cogniti AI. Students increasingly positioned it as a reflective partner, asking it to confirm coherence between research questions, instruments, and evidence, and to surface tensions or alternative analytic possibilities. In these stages, Cogniti AI functioned as a mediator of metacognition, prompting PSTs to interrogate the adequacy of their methodological choices and to exercise professional judgement by selectively adopting, adapting, or rejecting AI feedback. At the expanded stage, agency was expressed most clearly in PSTs’ capacity to decide when and how GenAI should be used—or deliberately not used—based on pedagogical, ethical, and developmental considerations. Across all stages, a persistent tension was the risk of a dependency trap, whereby the fluency and ease of GenAI output could undermine independent professional judgement. Learning to navigate this tension, rather than maximising GenAI use, emerged as central to the development of professional agency. The theoretical contribution of this research is to conceptualise GenAI as a relational mediator in the development of teacher agency. Pedagogically, it offers guidance on scaffolding GenAI use to foster critical autonomy rather than dependence. Practically, it provides design principles for research tasks in which GenAI supports professional judgement and disciplined reflection, enhancing—rather than replacing—human teaching and inquiry. References Alé, J., Ávalos, B., & Araya, R. (2025). Chilean teachers’ knowledge of and experience with artificial intelligence as a pedagogical tool. Education Sciences, 15(10), 1268. https://doi.org/10.3390/educsci15101268 Edwards, A. (2015). Working relationally in and across practices: A cultural-historical approach to collaboration. Cambridge University Press. Engeström, Y. (1987). Learning by expanding: An activity theoretical approach to development research. Helsinki: Orienta-Konsultit. Harré, R., & van Langenhove, L. (1999). Positioning theory: Moral contexts of intentional action. Blackwell Publishers. Martin, J. (2020). Teacher agency and the sociotechnical: Reconfiguring professional learning through relational approaches. Teaching and Teacher Education, 92, 103046. https://doi.org/10.1016/j.tate.2020.103046 Lodge, J. M., Yang, S., Furze, L., & Dawson, P. (2023). It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence? Learning: Research and Practice, 9(2), 117–124. https://doi.org/10.1080/23735082.2023.2261106 Reyes-Rojas, J., Díaz, B., Ruz-Reveco, C., Castro, A., & Reyes-González, D. (2026). Conceptualizing pre-service teachers’ readiness for AI integration into teaching practices: An intelligent-TPACK approach. Computers and Education Open, 10, 100320. https://doi.org/https://doi.org/10.1016/j.caeo.2025.100320 10. Teacher Education Research
Paper AI-Powered Pedagogical Priorities: How AI is Shaping Instructional Design in Indonesian Teacher Education. 1: University of Southampton; 2: Yogyakarta State University Presenting Author:The rapid integration of generative artificial intelligence (GenAI) in higher education is reshaping how faculty design, implement, and assess learning. This shift is especially consequential for teacher education institutions, which must prepare graduates to teach effectively and lead school change (Dickerson et al., 2021). Accordingly, instructional design is increasingly framed as a decision-oriented, reflective practice that models professional reasoning for prospective teachers (Loughran, 2019). Lecturers, therefore, need to interrogate design choices so that AI adoption improves learning rather than merely substituting existing tools (Osorio Vanegas et al., 2025). Reflective modelling, however, is constrained by time poverty linked to work intensification. Scholarship shows that administrative burdens and formalities erode attention to pedagogy and reduce teaching effectiveness (Woelert, 2023). In Indonesia, mandatory performance reporting and evidence uploads to a national digital platform expand documentation workloads, while such processes can raise motivation and document management, but they do not directly improve lecturers’ teaching performance (Kamaluddin et al., 2024). Institutional analyses of Indonesian higher education data reporting governance likewise foreground complexity and structural accountability, conditions that readily redirect academic energy from pedagogical priorities to compliance (Baraas et al., 2023). At the same time, national policy allows GenAI integration when it is ethical, transparent, and maintains academic integrity, so AI is often promoted to drive efficiency and personalise curriculum, assessment, and materials (Directorate of Learning and Student Affairs, 2024). Existing Indonesian research on AI largely addresses technological affordances, student outcomes, and ethical or policy concerns. Far less attention is paid to AI as a transformative force in pedagogical priorities and in lecturers’ professional agency issues central to teacher education, where modelling reflective, values-driven practice is essential. Consequently, we lack a robust understanding of how AI reshapes the conceptualisation of effective pedagogy in instructional design, and how such influence shapes prospective teachers’ perceptions and uptake of professional practices. This study addresses that gap by conceptualising AI integration as a mediating factor that can reconfigure pedagogical priorities. We employ the Community of Inquiry (CoI) framework with emphasis on teaching presence as foundational to teaching effectiveness. It comprises design and organisation, facilitation, and direct instruction, which together express teaching intentionality and the quality of design decisions (Wang et al., 2021). In teacher‑training institutions, teaching presence not only orchestrates learning through design, facilitation, and social‑cognitive activity, but also models pedagogical reasoning and professionalism that prospective teachers can internalise (Adam et al., 2025). To explain how AI shapes teaching presence, we draw on Activity Theory. We conceptualise instructional design as a historically and socially situated activity system in which educators (subjects) pursue pedagogical goals (objects) through mediating artefacts, AI among them, within rules, community norms, and divisions of labour. Contradictions among these components are treated as drivers of practice change (Engeström, 2001). Within this lens, AI operates as a mediating artefact that embodies implicit pedagogical assumptions and can introduce new tensions into the ecology of instructional design. Guided by CoI through the lens of Activity Theory, the study investigates three questions: (1) How does AI reconfigure pedagogical priorities associated with teaching presence in lecturers’ instructional design at Indonesian teacher‑training institutions? (2) What tensions emerge when AI affordances interact with pedagogical values, institutional rules, and divisions of labour in the instructional‑design activity system? (3) How does AI-mediated teaching presence influence the modelling of pedagogical practices and the development of pre-service teachers’ professional identities? Centring lecturers’ perspectives, we foreground pedagogical agency as a key construct for understanding how educators negotiate, accommodate, or resist AI-mediated design norms in teacher education. Methodology, Methods, Research Instruments or Sources Used An interpretive qualitative approach was employed, utilising a single-site case study design at a teacher-training university in Indonesia. The university was treated as a bounded system to investigate how lecturers interpret, negotiate, or reject AI-mediated design norms in instructional design. The theoretical framework integrates the Community of Inquiry (CoI) model, specifically the dimensions of teaching presence (design and organisation, facilitation, direct instruction), with Engeström's Activity Theory (AT), thereby connecting micro-level teaching practices to system-level elements such as Tools, Rules, and the Division of Labour. Participants were purposively recruited, with five lecturers selected based on the following criteria: (1) active involvement in teaching prospective teachers, (2) experience utilising artificial intelligence for lesson planning, syllabus development, assessment design, or instructional materials, and (3) representation of social sciences, STEM, and humanities disciplines with exposure to educational technology. Data collection involved semi-structured interviews lasting approximately 60 minutes that focused on design workflows and supporting artefacts, such as lesson plan excerpts, rubrics, prompts, and draft materials. Ethical procedures included obtaining informed consent and ensuring participant anonymity. We applied Framework Analysis by Ritchie & Spencer (2002) in five stages, aligned with the research questions and the CoI–AT frame. (1) Familiarisation: immersion in transcripts and artefacts, with reflexive memos marking teaching presence and Activity Theory segments and their initial links to RQ. (2) Thematic framework: an a priori structure from CoI (pedagogical priorities) and AT (Subject, Object, Tools, Rules, Division of Labour), augmented by emergent subthemes (efficiency, pedagogical control, trust in AI, local context) to maintain theory–data fit. (3) Indexing: codebook-based coding with operational definitions and inclusion/exclusion rules. 20–30% double coded, discrepancies resolved, and the codebook revised, all decisions logged in an audit trail. (4) Charting: case and theme matrices summarising (a) reconfigured pedagogical priorities (teaching presence), (b) tensions at AI and Rules/Division of Labour, and (c) modelling of lecturer practices and pre-service teachers’ professional identities. (5) Mapping & interpretation: cross-case comparison to derive a typology of AI-based teaching presence, trace adaptation mechanisms (rule changes, assessment redesign, work redistribution), and formulate propositions on pedagogical agency. Rigour achieved through member checking of summary matrices, negative case analysis, and conceptual-level theoretical saturation. Conclusions, Expected Outcomes or Findings Across this teacher training university, lecturers consistently subordinate AI to pedagogical rationality, sustaining teaching presence through concrete actions. AI is therefore confined to preparatory work, including drafting materials, course outlines, recaps, and initial evaluations, while core pedagogical decisions remain with lecturers. Adoption is heterogeneous. Three lecturers fully embraced AI with personal implementation strategies, whereas two accepted it conditionally, contingent on clear institutional guidelines and training in responsible use. Together, illustrating a normative stance that AI must remain subordinate to human pedagogical judgement, supported by digital literacy, institutional safeguards, and prudence to preserve lecturer agency (Kasneci et al., 2023). To stabilise tensions between tools–rules and tools–division of labour, boundary-setting is implemented through learning outcomes-based guardrails, source verification, and a mandatory synthesis stage to safeguard reasoning and critical thinking. This aligns with Activity Theory’s contradictions and affirms human control in educational AI integration. Operationally, AI assumes augmentative, distributive, and delegative roles within “shadow work” such as summarising, rubric drafting, formative item banks, and technical corrections, ensuring that professional reasoning remains central to instructional design. This approach reinforces the lecturer’s role as the director responsible for metacognition and final decisions, maintaining human oversight and preventing AI from displacing pedagogical judgement (Atchley et al., 2024). Efficiency gains are reinvested into high-value pedagogy, including personalised mentoring, contextual resource enrichment, instructional reflection, richer classroom interactions, and advanced, transparent assessment methods (such as consistent analytic rubrics, adaptive formative quizzes, tiered feedback banks, and process-tracked portfolios). Lecturers model selective-reflective AI use to foster prospective teachers’ reflective competencies, highlighting reflection’s centrality in teacher preparation and a human–AI collaboration model that prioritises pedagogical agency, metacognition, and professional reasoning (Pinnegar & Lay, 2023). It is recommended to enact institution-wide AI policies with human oversight, cut administrative workload, and embed critical synthesis tasks while modelling effective AI use across teacher education curricula. References Adam, M. S., Abd Hamid, J., Khatibi, A., & Azam, S. F. (2025). Investigating the effects of direct instruction and facilitated discourse on social and cognitive presence in blended learning. Online Learning, 29(1), 89–108. Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: Opportunities and concerns. Cognitive Research: Principles and Implications, 9(1), 20. Baraas, T., Sudirman, Asrin, Setiadi, D., & Fahruddin. (2023). Governance of higher education database reporting (PDDikti): Case study at XYZ University. Jurnal Ilmiah Mandala Education, 9(4), 3168–3178. Creagh, S., Thompson, G., Mockler, N., Stacey, M., & Hogan, A. (2025). Workload, work intensification and time poverty for teachers and school leaders: A systematic research synthesis. Educational Review, 77(2), 661–680. Dickerson, C., White, E., Levy, R., & Mackintosh, J. (2021). Teacher leaders as teacher educators: Recognising the ‘educator’ dimension of some teacher leaders’ practice. Journal of Education for Teaching, 47(3), 395–410. Direktorat Pembelajaran dan Kemahasiswaan. (2024). Panduan penggunaan generative artificial intelligence (GenAI) pada pembelajaran di perguruan tinggi. Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi. https://dikti.go.id/api/file/humas-production/2024/10/Panduan-Penggunaan-Generative-Artificial-Intelligence-pada-Pembelajaran-di-Perguruan-Tinggi.pdf Engeström, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work, 14(1), 133–156. Kamaluddin, L. A., & Asniwati, A. (2025). Optimalisasi kinerja dosen melalui penguasaan teknologi dan dukungan organisasi: Implikasi terhadap kepuasan kerja dalam pelaksanaan Tridharma perguruan tinggi. Jurnal Manajemen STIE Muhammadiyah Palopo, 11(2). Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. Loughran, J. (2019). Pedagogical reasoning: The foundation of the professional knowledge of teaching. Teachers and Teaching, 25(5), 523–535. Osorio Vanegas, H. D., Segovia Cifuentes, Y. D. M., & Sobrino Morrás, A. (2025). Educational technology in teacher training: A systematic review of competencies, skills, models, and methods. Education Sciences, 15(8), 1036. Pinnegar, S., & Lay, C. D. (2023). The role of reflection in teacher and teacher educator development. Frontiers in Education, 8, 1225168. Wang, Y., Zhao, L., Shen, S., & Chen, W. (2021). Constructing a teaching presence measurement framework based on the Community of Inquiry theory. Frontiers in Psychology, 12, 694386. Woelert, P. (2023). Administrative burden in higher education institutions: A conceptualisation and a research agenda. Journal of Higher Education Policy and Management, 45(4), 409–422. 10. Teacher Education Research
Paper Negotiating Knowledge, Assessment, and Teacher Agency in GenAI-Supported Science Experiments Zonguldak Bulent Ecevit University, Turkey (Türkiye) Presenting Author:The incorporation of generative AI (GenAI) into physics education embodies a duality of extraordinary potential and considerable educational risk. Recent research indicates that models such as GPT-4o may surpass the performance of typical undergraduates on standardized physics concept inventories; however, this superiority fosters a misleading appearance of mastery (Kortemeyer et al., 2025). A thorough review of the literature uncovers significant concerns related to multimodal processing, the validity of assessments in laboratory environments, and the dependence on students' prior knowledge to alleviate AI hallucinations (Kortemeyer et al., 2025; Nikolic et al., 2025). First, AI doesn't work the same way in all areas and modes. LLMs are excellent at conceptual tasks that involve text, and they often do better than humans at things like thermodynamics and relativity. However, they are not very good at interpreting visual data like graphs and diagrams. Also, there is a big "language gap" because AI models don't work the same way in all languages. They work better in English and Western European languages but worse in others. This could make global educational inequalities worse (Kortemeyer et al., 2025). Second, the rise of GenAI puts the accuracy of traditional lab tests at risk. The standard lab report, which tests cognitive skills like writing and data synthesis a lot, is very easy for AI to copy. Conversely, psychomotor objectives (equipment handling) and affective objectives (teamwork, ethics) are significantly more impervious to AI simulation; however, educators presently exhibit diminished confidence in evaluating these areas. To preserve assessment validity, it is imperative to transition from unsupervised written reports to a variety of assessment modalities, such as practical examinations, interviews, and direct observations, which are not easily replicable by AI (Nikolic et al., 2025). Lastly, the fact that AI can be a "lab partner" shows a paradox of expertise. Case studies with high school students show that in order to use AI to solve problems well, students need to already know enough about the subject to be able to spot "hallucinations" or wrong physics explanations given by the model. Students who are new to a subject and don't have this basic knowledge are more likely to believe analogies that sound good but are scientifically wrong. This can make misunderstandings worse instead of fixing them. So, to use GenAI in classes, we need more than just access to the technology (Kilde-Westberg et al., 2025). In this context, the emphasis on science teacher training is critical, particularly in relation to GenAI. Programs should not only use GenAI for productivity but also encourage critical evaluation and the development of irreplaceable skills. This design-oriented qualitative study was conducted in a new undergraduate course, Experiments in Primary Science Education. The course treated GenAI as a critical tool for hands-on experiments rather than a credible source of information. The hands-on experiments focus on recognizing AI faults, reassessing valid evaluations, and rethinking instructional identity regarding AI. GenAI is not considered reliable. The study shows how GenAI changes notions of reliable knowledge and teaching obligations by redefining knowledge as an epistemic practice impacted by judgment and uncertainty. Overall, it contributes to discussions on how educational research can adapt to AI-enhanced classroom difficulties and potential. Within this framework, the study is guided by the following research questions: 1. How do pre-service teachers evaluate the scientific reliability of GenAI-generated explanations in hands-on science experiments? 2. How does engagement with GenAI influence pre-service teachers’ conceptions of assessment validity in experimental science education? 3. How do these experiences reshape pre-service teachers’ pedagogical positioning and sense of agency in AI-mediated learning environments? Methodology, Methods, Research Instruments or Sources Used The study adopts a design-based qualitative research approach, enabling iterative alignment between pedagogical design, data collection, and theoretical interpretation. The research was carried out during a six-week instructional module integrated into the “Experiments in Primary Science Education” course at a faculty of education. The participants were pre-service primary and elementary science teachers enrolled in the course. The educational design has three phases that are all connected. In the first phase, participants are expected to perform science experiments that are in line with primary-level curricula, such as floating and sinking. After doing certain experiments, students ask a GenAI system to explain what they saw. These AI-generated answers are deliberately chosen or directed to incorporate plausible yet scientifically erroneous reasoning. Participants are instructed to evaluate AI outputs against their empirical observations and to substantiate their assessments concerning scientific reliability and accuracy. During the second phase, participants are expected to work on redesigning assessments. For the same experimental tasks, they look at several ways to test, such as traditional written lab reports, oral exams, practical demonstrations, and real-time observational rubrics. Students critically evaluated which learning outcomes could be properly assessed in the context of GenAI and which necessitated human judgment, physical involvement, or ethical thinking. The third phase is all about figuring out how they are as a teacher and where they fit in. At the beginning and end of the module, participants are expected to write down their thoughts on the function of GenAI in science teaching and what teachers should do in AI-mediated learning settings. The sources of data are: Reports on comparative assessments in writing of explanations based on AI and experiments Reflective journals and structured written responses Pre- and post-module pedagogical positioning texts Interviews (individual and group) Qualitative content analysis and an inductive-deductive coding technique are planned to be used. Analytical categories concentrated on epistemic evaluation standards, the rationale for assessment validity, and manifestations of teacher agency and accountability. Triangulation of data sources and repeated peer debriefing will be generated to show the reliability of the analysis. Conclusions, Expected Outcomes or Findings This research aims to enhance physics and science education research by providing an empirically based and critically focused example for the incorporation of GenAI into experimental learning contexts. Instead of only looking at how well or quickly GenAI works, the study hopes to show how using AI changes teachers’ decisions about what to teach, how they evaluate students’ performances, and how they conduct themselves as teachers through the educational environment changes. First, the study is going to clarify how structured, experiment-focused interactions with GenAI facilitate the cultivation of epistemic awareness among pre-service teachers. It is expected that participants would exceed artificial recognition of fluent AI-generated explanations and progressively depend on empirical evidence, causal reasoning, and conceptual coherence in the assessment of scientific arguments. This result would highlight that science education should see GenAI as a tool for critical analysis rather than a definitive source of knowledge. Second, the study seeks to improve GenAI-era assessment validity discussions. The research engages pre-service teachers in a critical review of evaluation methodologies to emphasize a change toward learning outcomes that focus on human qualities like experiential judgment, psychomotor skills, ethical reasoning, and collaborative practices. These findings might help reconfigure AI-mediated assessment methods for efficacy. Third, the study aims to demonstrate shifts in pedagogical positioning, with pre-service teachers increasingly viewing their role as epistemic mediators and responsible decision-makers rather than mere distributors of content. This anticipated modification would impact teacher education programs aiming to prepare future teachers for classrooms filled with AI. The study aims to contribute to educational research by demonstrating how experimental science courses can function as critical platforms for examining the risks and opportunities linked to GenAI. The study underscores epistemic integrity, evaluative accountability, and teacher agency, offering a flexible structure for the incorporation of human-centered AI in scientific education. References Kortemeyer, G., Babayeva, M., Polverini, G., Widenhorn, R., & Gregorcic, B. (2025). Multilingual performance of a multimodal artificial intelligence system on multisubject physics concept inventories. Physical Review Physics Education Research, 21(2), 020101. https://doi.org/10.1103/98hg-rkrf Nikolic, S.,Suesse, T.F., Grundy, S., Haque, R., Lyden, S., Lal, S., Hassan, G.M., Daniel S. & Belkina, M. (2025). Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection, European Journal of Engineering Education, 50(4), 673-701, https://doi.org/10.1080/03043797.2025.2456944 Kilde-Westberg, S., Johansson, A., & Enger, J. (2025). Generative AI as a lab partner: A case study. Physical Review Physics Education Research, 21(2), 020119. https://doi.org/10.1103/ggy1-3kjk | ||