Research Methods in Applied Linguistics· 2026Q1
Task engagement through the lens of generative artificial intelligence: A thematic reanalysis of Lambert, Philp and Nakamura (2017)
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- 2026year
Short summary
Generative AI, guided by the Engagement in Language Use (ELU) framework, identified five themes (disclosure, affirmation, critical thinking, self-assessment, self-presentation) in a learner discourse dataset, provisionally termed Self-Expression in Language Use (SELU).
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Key points
- Generative AI was used to reanalyze a dataset on task engagement from Lambert et al. (2017).
- The Guided AI Thematic Analysis (GAITA) procedure utilized the Engagement in Language Use (ELU) framework.
- Five themes were identified: disclosure, affirmation, critical thinking, self-assessment, and self-presentation.
- A new coding template, Self-Expression in Language Use (SELU), was provisionally developed through human-GenAI interaction.
AI-generated from the title and abstract; the full text is not read.
Abstract
This brief report presents initial results from ongoing research on using generative artificial intelligence (GenAI) in task engagement research. We report a thematic reanalysis of the dataset from Lambert, Philp and Nakamura (2017) using MAXQDA Tailwind and Guided AI Thematic Analysis (GAITA) (Nguyen-Trung, 2025). Lambert et al. (2017) operationalised task engagement in terms of frequencies of language forms serving specific functions in learners’ discourse. This discourse analytic framework was termed Engagement in Language Use (ELU) (Lambert & Aubrey, 2023). ELU was provided to the GenAI as an initial analytic template to guide but not limit the analysis. The GAITA procedure identified five themes in the dataset - disclosure, affirmation, critical thinking, self-assessment, and self-presentation - each with sub-clusters and codes. Results provide an initial dataset-specific coding template and analytic heuristic, generated through human-GenAI interaction, provisionally termed Self-Expression in Language Use (SELU). The results raise questions for task engagement theory and demonstrate affordances of GAITA for future task engagement research.
The authors' abstract, as published at the source. Research Methods in Applied Linguistics, 2026 · DOI ↗
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Field: Human Factors and Ergonomics
Human Factors and ErgonomicsSocial Sciences