Doctoral Consortium I
Understanding Teacher Educators’ Reflective Practices: Early Findings From a Needs Analysis Informing AI–Supported Reflection
Mireille Pinas
The Open University, UK
Abstract:
Reflection is recognised as central to teachers’ professional development, supporting critical thinking, self-evaluation and informed pedagogical decision-making (Larrivee, 2008). Yet despite its importance, relatively little is known about the reflective skills and practices of university-based teacher educators (Logan et al., 2025). In practice, higher education faculty find it challenging to engage in meaningful reflection due to limited protected time and insufficient institutional support (Bray & Fotheringham, 2022).
A further challenge lies in the reflective practice literature. While established models offer valuable conceptual foundations, they are not designed for the distinctive, multifaceted nature of teacher educators’ work. Teacher educators navigate responsibilities including teaching, mentoring, curriculum design and researching their practice. This complexity increases the need for reflection to make informed decisions across overlapping roles. Yet existing frameworks do not fully account for this complexity or the need to navigate multiple roles simultaneously. Furthermore, most models focus on either stages of reflection or depth of reflective thinking, offering limited practical or role-sensitive scaffolding. This limitation may be particularly pronounced for teacher educators, given their varied roles.
In response to these issues, my doctoral study seeks to develop a reflective framework grounded in reflective theory and responsive to the multifaceted nature of teacher educators’ work. The study explores how generative AI (GenAI) can serve as a “critical friend,” guiding teacher educators by posing reflective questions and offering personalised, dialogic, and flexible support. Emerging work in GenAI suggests that these tools may prompt deeper thinking (Al-Fattal, 2025), yet their use for supporting teacher educators’ reflection remains underexplored.
This presentation reports early findings from the first phase of the study, a needs analysis based on semi-structured interviews with university-based teacher educators in the UK. It offers initial insights into their reflective practices across multiple roles and their perceptions of artificial intelligence for reflection.
Exploring Transformative Agency in AI-Mediated Foreign Language Learning: A Study of chatbot-mediated Language Learning using Activity Theory
Patricia Lanners-Kaminski
Lancaster University
Abstract:
While research on artificial intelligence in language education has grown rapidly, much of this work has focused on learning outcomes or technological affordances. Less attention has been paid to how learners actively engage with AI tools and how such engagement may reshape their learning practices over time. This doctoral study addresses that gap by examining active engagement with AI through the lens of Transformative Agency in AI-mediated foreign language learning, in chatbot-mediated environments. Transformative Agency is understood here as learners’ capacity to question, reinterpret, and deliberately reshape established ways of learning, making it a useful concept for analyzing active engagement with AI beyond mere tool use.
To understand how Transformative Agency develops, the study looks beyond isolated instances of chatbot use to the broader activity systems in which learning takes place. Drawing on Cultural-Historical Activity Theory (CHAT), it conceptualizes chatbot-mediated language learning as a dynamic activity system in which AI chatbots function as mediating artefacts embedded in broader social and institutional contexts. Particular attention is given to contradictions within and between elements of these activity systems and to how such tensions may drive change. Within this framework, Transformative Agency is used to examine how learners question, reinterpret, and potentially transform their learning practices in response to these tensions.
The study adopts a qualitative design based on semi-structured interviews with language learners who use AI chatbots as part of their language-learning activity systems. The project seeks to generate a contextualized understanding of how learners experience AI-mediated language learning and how they enact Transformative Agency in relation to chatbot use, including possible shifts in goals, strategies, roles, and responsibilities.
The study aims to contribute to research on AI in education by providing a systemic and developmental account of how Transformative Agency emerges in learners’ engagement with AI, rather than foregrounding technological functionality.
Re-Thinking Assessment in the Age of Artificial Intelligence
Denise G. Taylor
Lancaster University
Abstract:
This study used the interventionist methodology, CHAT-informed Change Laboratory with an expansive learning cycle to re-imagine what we are measuring and why we assess in the Age of AI. Throughout collaborative workshops with educators from diverse educational contexts, this study explored a central pedagogical challenge posed by the ubiquity of generative AI: if students can produce work that is indistinguishable from their own, what, and how, should we assess?
Beginning with a shared recognition that AI is a disruptor of traditional output-based assessment, workshop participants examined the distinction between AI as an assistant and AI as a substitute for student thinking. Early discussions raised tensions among institutional policy, equity concerns, and the validity of existing assessment frameworks, with participants questioning whether existing assessment criteria could still serve as reliable indicators of genuine learning.
From these dialogues, the group co-constructed a six-criterion rubric designed to shift the locus of assessment from product to process. The criteria: Reasoning Trail, Evidence Quality, AI Declaration and Prompt Transparency, Personal Situatedness, Idea First, Language Later, and Live Accountability, each require students to make their thinking visible at successive stages of their work, generating process artefacts that AI cannot easily replicate or simulate.
Subsequent workshops focused on trialling the rubric in participants’ own classrooms. Feedback revealed both promise and friction. The criteria most consistently valued were those requiring personal situatedness and visible ideation, while live accountability tasks proved a practical alternative to formal viva-style examinations. Persistent challenges included teacher readiness for process-based assessment, institutional policy constraints, and a minority view among participants that AI has already rendered any criterion-based assessment fundamentally unreliable.
Participants concluded by arguing that authentic assessment in a generative AI landscape requires a structural shift: from assessing what students produce, to evidencing how and why they think.
Learning Through Mediated Problem-Solving in Engineering Final Year Projects
Deepa Gundala Vijayaraghavan
Lancaster University
Abstract:
Final year projects (FYPs) are capstone experiences that prepare engineering students for professional practice, which is a measure of the transformation of knowledge, they acquire through their academic program. Whilst many studies have examined the administration and outcomes of FYPs, there remains limited analytical understanding of how learning unfolds progressively within these complex, tool-rich environments. This study investigates how students engage with multiple mediational resources during FYPs and how such engagements shape their learning trajectories and development of industry readiness attributes.
Drawing on an Activity Theory perspective, the study analyses 200 micro episodes of student activity across different phases of FYP work. The analysis focuses on how disturbances[contradictions] arising within the activity system—such as tool unfamiliarity, knowledge gaps, resource constraints, and team-based tensions—trigger mediated actions. Students mobilised a range of mediational artefacts, including simulation tools, online resources, peer collaboration, and AI-based tools, to navigate these challenges.
The findings indicated that learning was characterised by trajectories moving from disturbance to stabilisation through mediated action, viz: From Double Stimulation to Ascending from abstract to concrete [DS →ATC], Moving from abstract to concrete [ATC]. Students were able to reorganise their activity, leading to the development of practical competencies such as troubleshooting, adaptive problem solving and collaborative coordination. Some episodes resulted in a “critical learning impasse” [ IMPASSE], where constraints could not be effectively negotiated through available mediational means, limiting both activity progression and skill development. These can be understood as critical touchpoints where intervention may enhance learning outcomes in FYPs.
The study indicated that learning in FYPs is not linear or solely driven by prior knowledge but emerges through engagement with the evolving object of activity mediated by diverse tools and resources. It is also shaped by how students construe the object of the activity. By illustrating how learning is enabled or constrained, the findings can be used as directives for designing learning environments and re-design curriculum that better support students in navigating complex, real-world engineering tasks and bridge the gap between curriculum and industry requirements.
Rethinking Programming Education through Physical Computing: A Case Study approach on Level 1 Computing and Communications at the Open University
David McDade
The Open University, UK
Abstract:
Small, low-cost computing devices associated with the maker technology movement have become popular over the last decade. Credit-card sized devices and small embedded controllers such as Raspberry Pi and Arduino are now starting to play a vital role in education, industry and how we engage with technology at home.
Collectively, under the term Physical Computing, these devices can offer an alternative and engaging computing experience that enable opportunities for embodied learning, innovative use of virtual technologies and contemporary strategies for teaching computing. However, what role can Physical Computing play with level 1 students at the Open University?
Can we use Physical Computing as a medium to showcase novel and inventive ways of programming and the use of technology at the Open University? Can we use Physical Computing to develop improved and enhanced teaching experiences for level 1 students learning programming as part of their studies?
This presentation outlines the work carried out so far on a Professional Doctorate study by David McDade, titled: Rethinking Programming Education through Physical Computing: A Case Study approach on Level 1 Computing and Communications at the Open University.
The study asks the following questions:
- In what ways do systemic factors and teacher expertise enable or constrain the effective use of physical computing?
- How can engagement with physical computing supports students’ conceptual understanding of programming and foundational computer science concepts?
David McDade is in year 3 of his doctoral studies and works as a Staff Tutor and Senior Lecturer in the Faculty of Science, Technology, Engineering and Mathematics (STEM) in the School of Computing & Communications (C&C).
Exploring Volumetric Video Production for Learning: Preliminary Insights from Industry Practitioners
Italo Rangel
The Open University, UK
Abstract:
Volumetric Video (VV) is a form of immersive media that uses multiple cameras to capture dynamic subjects and scenes from multiple viewpoints, creating 3D representations that can be integrated into virtual or mixed reality learning experiences. To date, much of the published work on VV appears to focus on technical development, while its educational use remains an emerging area, with a limited number of studies exploring applications in education and training.
Existing studies suggest its potential within immersive learning environments to support embodiment, realism in high-stakes scenarios, storytelling, interactivity, and distance learning (Liu et al., 2024; Bi et al., 2023; Hackett et al., 2022; Bourke et al., 2024; Young et al., 2023; McIlvenny, 2020; Mangina et al., 2024). However, most applications remain largely experimental, and there is still limited understanding of how learning experiences using this medium are designed in practice.
In the current volumetric video landscape, industry leads the design and production of VV across contexts, including education and training, which presents a significant opportunity to explore current practice.
This presentation reports preliminary findings from the first exploratory study within an ongoing PhD project, based on semi-structured interviews with industry practitioners. These findings offer an initial foundation for design conversations with educators and contribute to the ongoing development of a learning experience design model for volumetric video.