Speakers
Abstract section 4: Outcomes/results
Participants will learn practical strategies for using NotebookLM to support multilingual qualitative data analysis. The session will demonstrate tools for multilingual audio transcription, bilingual or multilingual data display, and AI-assisted querying of single or multiple data sources. Attendees will also see examples of generating summaries and identifying themes across languages to facilitate coding processes. By the end of the session, participants will gain ideas for integrating AI tools into their research workflow while developing critical awareness of how AI can assist, rather than replace, human interpretation in multilingual educational research.
Abstract section 2: Contribution/research questions
This practice-oriented presentation demonstrates how NotebookLM can support the coding and analysis of bilingual and multilingual qualitative data. The presenters will share practical prompt-engineering strategies and workflows used to organize, code, and interpret multilingual research data in EFL and ESL contexts. The session aims to help educators and researchers develop AI-assisted approaches to qualitative analysis while strengthening AI literacy skills.
Abstract section 5: References
Hodges, C., & Ocak, C. (2023). Integrating generative AI into higher education: Considerations.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16).
Abstract section 1: Relevance
As artificial intelligence (AI) becomes increasingly embedded in educational technologies and everyday digital tools, higher education must adapt to its integration in beyond pedagogy (Hodges & Ocak, 2023). In language education research, AI tools can support the analysis of multilingual data sets, particularly in contexts where English coexists with other languages. Developing the competencies to critically and effectively use AI tools aligns with the concept of AI literacy, which emphasizes the ability to collaborate with and responsibly apply AI technologies (Long & Magerko, 2020). This presentation addresses how AI-assisted tools can support multilingual qualitative data coding in EFL research contexts.
Abstract section 3: Content/method
The presenters will demonstrate how NotebookLM can be used to manage and analyze multilingual data sets. The session will show step-by-step examples of prompting strategies for displaying interview transcripts, organizing single or collective data, generating summaries, and identifying themes across languages. Participants will observe practical examples of AI-assisted coding workflows applicable to multilingual research contexts and action research in language education.
| Title | AI-Assisted Coding of Multilingual Data with NotebookLM |
|---|---|
| Teaching Context | College and university education |