Conference Agenda

My doctoral reseach uses narrative inquiry, positioning and valuation theories to explore the ways in which people ascribe value to their lifelong learning practices, when those practices are mediated by a multiplicity of everyday digital techologies. I aim to provide new insights into the value of learning practices which extend beyond but remain entangled with the commodified forms offered through educational qualifications.

Lifelong learning has multiple and contested definitions, shaped by decades of ideological framing by national and international policy bodies. It can be seen as an anticipatory regime (Adams et al., 2009; Amsler & Facer, 2017) in which technologies are understood to be rapidly advancing and learning is required either to defend against technological tyranny (Faure et al., 1972) or to prepare for an anticipated onslaught of oncoming skills needs (Black, 2022; Bound, 2023; OECD, 2021). But lifelong learning is also a lifewide, diverse and deeply personal practice, drawing on and arising from all contexts of life and their learning potential, an ongoing process of re-constructing the meanings by which we live our lives, shaping our understandings of self, world, and their interactions (Alheit, 2022; Jackson, 2011; Jarvis, 2007).

Whatever purpose is ascribed to lifelong learning practices, technology use and advancement are always implicated in their undertaking. I therefore take a postdigital approach, acknowledging and foregrounding the socio-technical role of digital, internet-connected technologies in mediating processes of learning and value creation (Jandrić et al., 2023; Knox, 2019). Broadly I define "lifelong learning" as learning undertaken outside of traditional formal education, by adults across diverse stages and domains of life.

In 2025, I faciltated narrative interviews with ten acquainted pairs of people engaged in a wide range of life phases, from retirement, to early parenting, to the first year out of secondary school. Each participant pair had shared a context of learning and had chosen one another as storytelling partners, enabling the dialogic negotiation of value between them. Participants' stories are rich, diverse and layered, demonstrating pragmatic, critical and creative engagement with a range of technology tools and platforms to pursue valued practices, from finding God to growing daphne, from reading archaic handwriting to mastering trapeze.

Now in the final year of the project, I am in the thick of analysis and write-up. My intent is to examine the evaluative features of each narrative, as well as the roles of the digital infrastructures within them in mediating these valuations. I hope to present my work-in-progress at this doctoral roundtable for advice on:

• Effective, compelling and ethical approaches to representing idiographic education research
• Tools for analysis of digital infrastructures
• Potential opportunities to disseminate this work for impact.

AI-Assisted vs Traditional Self-Assessment: A Multi-Modal Study of Student Processes, Accuracy, and Cognitive-Emotional Impact

Seval Kemal
Supervisors: Ernesto Panadero & Darina Scully

Abstract
Artificial intelligence is rapidly entering higher education assessment, yet its impact on students' self-regulated learning (SRL) processes remains underexplored. This PhD project investigates how AI-assisted self-assessment, Microsoft Copilot alongside a structured rubric, shapes metacognition, cognitive engagement, emotional responses, and accuracy in evaluating writing, compared to rubric-only methods. A randomized controlled trial (RCT) with DCU students contrasts control and intervention conditions on a reflective essay task. Multimodal data include think-aloud protocols (SEFEMO-coded), eye-tracking (Tobii Pro Fusion), electrodermal activity (Shimmer GSR), questionnaires, expert ratings, and self-generated feedback. Data collection ongoing with 33 participants secured.​

Background
SRL drives academic success through cognitive, metacognitive, and emotional processes (Panadero, 2017). Self-assessment, central to SRL, often misaligns with expert judgments, especially among novices (Andrade, 2019). While AI tools like Copilot support feedback, causal SRL effects lack multimodal study. SEFEMO application to AI self-assessment contexts is novel.

Objectives
RQ: How does AI-assisted self-assessment influence SRL processes, cognitive-emotional dynamics, and self-evaluation accuracy vs. traditional methods? Targets: SEFEMO actions; eye-tracking attention; EDA arousal; accuracy/feedback; AI trust.

Methods
Mixed-methods RCT: Students complete 750-word essay, randomized to control (rubric-only, Copilot disabled) or intervention (rubric + Copilot). Data collection: SEFEMO-coded think-alouds (Panadero et al., 2025); Tobii fixation/gaze; Shimmer EDA + cognitive load measures (Boucsein, 2012); blind expert ratings; feedback coding; baseline/AI perception questionnaires. Planned analyses: t-tests/Mann-Whitney, correlations, triangulation (de Mooij et al., 2025).​

Expected Results
AI expected to increase Compare/Redo frequencies and cyclical patterns (vs. linear Read/Recall in control); greater rubric attention; higher arousal (engagement vs. strain); accuracy gains for low-achievers but shallower feedback; mixed AI trust. Clarifies AI as scaffold vs. replacement.​

Significance
Theoretical: First SEFEMO application to AI; multimodal SRL extension. Practical: Evidence for Copilot in formative assessment. Methodological/policy: RCT template for AI-EdTech; informs ethics, autonomy, critical AI literacies.​

Feedback Questions

1. With this multimodal approach (SEFEMO + eye-tracking + EDA), which analyses should I prioritize first?

2. How can we protect student autonomy in AI self-assessment practices?

3. Even offering 15€ vouchers, I am struggling with participant recruitment, especially students uninterested in SRL. What practical strategies work best?​

References

Andrade, H. L. (2019). A critical review of research on student self-assessment. Frontiers in Education, 4, 87. https://doi.org/10.3389/feduc.2019.00087

Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-1126-0

de Mooij, S., Lams, J., Lim, L., Järvelä, S., Bannert, M., & Azevedo, R. (2025). A systematic review of self-regulated learning through multimodal data and AI. Educational Psychology Review, 37, Article 54. https://doi.org/10.1007/s10648-025-10028-0

​Panadero, E. (2017). A review of self-regulated learning. Frontiers in Psychology, 8, Article 422. https://doi.org/10.3389/fpsyg.2017.00422

Panadero, E., Pinedo, L., & Fernández Ruiz, J. (2025). Unleashing think-aloud data. Learning and Instruction, 95, Article 102031. https://doi.org/10.1016/j.learninstruc.2024.102031

The success of Leaving Certificate Computer Science (LCCS) since its launch in 2018 has depended heavily on out-of-field teachers (OOFT) to teach LCCS. The OOFTs received professional learning (PL) on teaching LCCS; however, it did not address individual teachers’ content and pedagogical knowledge gaps in teaching LCCS (Faherty et al. 2023). With the rapid development of generative AI (GenAI), education faces unprecedented opportunities for innovation. Consequently, as GenAI becomes increasingly embedded in educational contexts, understanding its possible role in teachers’ PL is essential. National policy and strategy publications, such as the Guidance on Artificial Intelligence in Schools 2025 and the Digital Strategy for Schools to 2027, promote the integration and use of Artificial Intelligence (AI) into teaching practices and PL.

This presentation outlines the rationale for exploring the use and impact of GenAI on LCCS teachers' PL and highlights the main research question:

How do LCCS teachers interpret the value and influence of GenAI in their PL?

Sub-questions

1. What is the impact of using GenAI on LCCS teacher PL?
2. What are LCCS teachers' experiences and perceptions of using GenAI for their PL?
3. How do LCCS teachers engage with GenAI to support their PL?

I propose using Garrison's Self-Directed Learning theory (Garrison, 1997) and the AI Competency Framework for Teachers (Miao and Cukurova, 2024) as a lens for examining LCCS teachers' engagement with GenAI.

The proposed research will employ a qualitative study, grounded in a pragmatic worldview. I propose using a qualitative study with triangulation to explore the use of GenAI in LCCS teachers' PL, thereby ensuring the rigour and validity of the findings. Data will be collected through document analysis, written reflections, and semi-structured interviews. Braun and Clarke's Reflexive Thematic Analysis will identify patterns in qualitative data, while descriptive statistics will summarise data without seeking generalised conclusions. The research adheres to ethical principles of informed consent and confidentiality. The findings aim to inform future Teacher Professional Learning (TPL) provisions, contribute to AI integration policies, and support schools in integrating AI technologies.

My proposed research has been informed by papers by ElSayary et al. (2025), Li et al. (2025) and Liu (2025).

I am in the second year of my doctoral studies at DCU and preparing for the research phase of my Ed.D. I would appreciate feedback on the viability of my proposed research methodology.

References

see bibliography