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