Doctoral Consortium II
Listening to Digital Fatigue: Neurodivergent Students’ Narratives of Embodiment, Belonging, and Online Learning
Giselle Tadman
Lancaster University
Abstract:
This study explores neurodivergent university students’ experiences – and makes sense – of digital fatigue in post-pandemic online learning environments. Using a qualitative, experience-centred narrative analysis design, it applies Voice-Centred Relational Method (VCRM) alongside Theory of Practice Architectures (TPA) to examine how cultural-discursive, material-economic, and social-political structures shape students’ embodied and relational experiences of fatigue. Findings show digital fatigue as cyclical, structurally produced, and closely tied to sensory load, institutional pacing, interface design, and relational isolation, while also highlighting the protective role of assistive technologies, flexible pacing, humour, and supportive tutor relationships. This study offers originality through narrative voices, embodiment, and structural analysis to illuminate digital fatigue as a systemic phenomenon instead of individual limitations. Insights generated have implications for higher education designers, digital learning developers, disability support teams, and educators seeking to create more accessible, relational, and neurodivergent-inclusive online learning environments.
Improving learners’ attention in synchronous online ESOL classes through the integration of visual aids: An action research and eye tracking study
Truong Do, Ursula Stickler, Lijing Shi, Irina Rets
The Open University, UK
Abstract:
Maintaining learner attention in synchronous online ESOL (English for Speakers of Other Languages) classes remains a persistent pedagogical challenge in CALL. Although visual aids are frequently incorporated into language teaching, most empirical research examining their impact has been conducted in face-to-face contexts. Existing findings are mixed, reporting both facilitative and distracting effects, and relatively few studies explore how visual aids shape sustained attention in live online ESOL classrooms. My research project investigates how the use of visual aids may facilitate adult ESOL learners’ attention during synchronous online lessons, drawing on an action research framework.
Situated within UK adult online ESOL provision, the study adopts an interpretivist paradigm and unfolds across three iterative action research cycles. In each cycle, teacher participants design, implement, and refine their own lessons incorporating visual aids, such as images and videos. Learners’ eye movements are captured during selected lesson segments using webcam-based eye tracking, an emerging and accessible methodological tool in CALL research. Quantitative eye-tracking data is triangulated with classroom observations and post-lesson learner interviews, which inform follow-up teacher discussions and action planning for subsequent cycles. My initial findings from the three cycles will also be presented.
AI-Mediated Student Support in Technology-Enhanced Learning: A Design-Based Research Study of Inclusive Engagement in Higher Education
Mahbub Rahman
Lancaster University
Abstract:
This doctoral research examines how an Ai-enabled student support application can be evaluated and iteratively redesigned to enhance engagement and academic participation for students with additional needs in higher education. While AI is increasingly embedded within institutional infrastructures, there remains limited empirical work examining how AI-mediated support operates within inclusive practices, particularly in Global South contexts.
The study adopts a design-based research (DBR) approach to investigate a newly launched AI-enabled support application implemented within a transnational university in Egypt. The research is structured across two iterative cycles of analysis, redesign, and evaluation. Cultural Historical Activity theory (CHAT) is used to examine how student engagement and access to support are mediated through the AI system within the institutional activity system, while Universal Design for Learning (UDL) informs the development of inclusive design modifications.
The first phase focuses on exploring how students with additional needs experience and engage with the AI-enabled application, alongside institutional factors shaping its use. The second phase will examine how iterative redesign may influence AI-mediated engagement, accessibility, and participation within existing support structures.
By situating the AI-mediated support system within a real institutional context, this study aims to contribute to ongoing discussions in technology enhanced learning (TEL) regarding inclusive digital provision and the role of AI in mediating support processes.
When Competence Fails Under Pressure: Designing AI-Supported Simulation for Emotional Intelligence and Decision-Making in Project Management
Kristen Karmazinuk
Lancaster University
Abstract:
Professional education often assumes that competence transfers directly to performance under pressure. However, failure in applied contexts frequently arises not from knowledge deficits, but from miscalibrated judgement under cognitive and emotional strain. While emotional intelligence (EI) is widely recognized as critical to professional practice, it remains inconsistently operationalized and is rarely embedded in technology-enhanced learning (TEL) environments to support decision-making under pressure.
This doctoral research, currently in its early conceptual and design stages, investigates how to design TEL systems to improve decision-making under pressure by supporting emotional regulation and decision calibration. In this research, decision-making under pressure is conceptualized as the alignment among judgement, confidence, and action under performance pressure, and reframes EI not as a static trait but as a trainable and measurable capability enacted in context.
Adopting a Design Science Research (DSR) approach, the research proposes developing an AI-supported simulation environment in which learners engage in time-constrained, socially interactive project scenarios. The proposed system integrates adaptive scaffolding into conversational, scenario-based simulations, where AI-driven stakeholders and team members respond dynamically to learners’ decisions. These interactions are intended to surface moments of miscalibration and support real-time reflection through prompts, confidence checks, and feedback.
A mixed-methods evaluation is planned to examine how such scaffolds may influence decision-making quality, confidence alignment, and reflective processes under pressure. At this stage, the research focuses on refining the conceptual model, defining system requirements, and informing the design of an initial prototype for subsequent empirical testing.
This research contributes to the design of TEL environments by embedding emotional and cognitive processes within AI-supported simulations for professional learning. It further addresses a critical gap between competence and performance under pressure, demonstrating how AI-supported environments can operationalize EI within decision-making contexts.
Feedback is sought on: (1) operationalizing calibration under stress; (2) capturing real-time emotional regulation in simulation-based environments; and (3) designing adaptive AI scaffolds that support reflection without reducing learner agency.
GenAI-Driven Assessment in Business Schools: Rethinking Trust, Pedagogy, and Outcomes
Peter Birdsall
The Open University, UK & Wittenborg University of Applied Sciences
Abstract:
The swift adoption of Generative Artificial Intelligence (GenAI) in higher education is transforming assessment methods, institutional practices, and the skills graduates develop. Although current research highlights the potential benefits and challenges of GenAI in education, there is limited empirical evidence on its broader influence on assessment systems, the changing psychological contract between students and institutions, and its effects on graduate employability, especially in business schools.
This study examines how GenAI impacts assessment and learning in higher education (business schools) using a framework that covers micro (students and faculty), meso (institutions), and macro (labour and policies) levels. It highlights the shift from traditional assessment to algorithmic evaluation and warns that, without careful redesign, GenAI could reinforce standardisation rather than promote human-centred learning.
The study uses a multi-phase mixed-methods approach rooted in critical realism and pragmatism. It includes qualitative interviews and focus groups with students, faculty, and employers to explore perceptions of fairness, trust, and GenAI in assessments. These insights inform longitudinal surveys tracking changes in GenAI literacy, assessment views, and employability confidence. A quasi-experimental intervention involving a GenAI and prompt-engineering module examines how structured GenAI interaction affects academic and employment outcomes. Also, document and policy analysis add institutional context.
This study advances both theory and practice in three key ways. First, it creates an integrated framework connecting GenAI adoption, assessment redesign, psychological contract dynamics, and employability outcomes. Second, it provides empirical insights into trust, transparency, and fairness in assessments mediated by GenAI. Third, it offers guidance for policy and strategic initiatives for business schools aiming to balance assessment innovation with ethical governance and labour market requirements.
The findings are intended to aid in creating human-centred, transparent, and employability-focused assessment systems in the growing context of GenAI-mediated higher education.
Emotional Profiles of Generative AI Adoption: A Mixed Methods Study of Distance Learning Educators in Higher Education
Kendal Wright
The Open University, UK
Abstract:
Emotional Profiles of Generative AI Adoption: A Mixed Methods Study of Distance Learning Educators in Higher Education
This EdD research project explores the emotional dimensions of generative AI (GenAI) adoption among distance learning educators in UK higher education. The study is guided by two aims: to explore the emotional dimensions of GenAI adoption, and to develop an adapted Technology Acceptance Model that incorporates emotional dimensions. While existing technology acceptance models consider cognitive aspects, emerging evidence suggests that emotions significantly shape a user’s engagement with rapidly evolving AI tools.
The project, at its current stage is considering a pragmatist paradigm. Enabling both methodological flexibility and a practical output focus. There will be two phases; phase one involves an online survey combining closed and open questions. The data will be analysed through hierarchical cluster analysis. Phase two will use purposive sampling to conduct semi structured interviews that explore how educators grouped within different emotional profiles work with GenAI within the distance learning higher education context. The two phase, sequential design ensures that the qualitative inquiry is grounded by empirically identified patterns.
Having the opportunity to present this research in its current stage at the CALRG Doctoral Consortium will provide a valuable opportunity to discuss a number of areas including the methodological and ethical challenges of researching emotions in technology adoption, and the grouping of educators, especially as a researcher embedded within the chosen research environment. I am seeking feedback on the development of the adapted technology acceptance model, the robustness of the cluster analysis approach for identifying emotional profiles, and strategies for maintaining reflexivity given my positionality.
This research aims to generate theoretically informed yet practically relevant insights that support educators navigating GenAI in distance learning contexts, aligning closely with CALRG’s commitment to advancing digital education research.