ChatGPT-4 as a lesson planning assistant: Activity theory insights into volunteer ESL teachers’ lesson preparation and task design
Bibliographic record
Abstract
Research on the use of generative artificial intelligence (GAI) in English Language Teaching (ELT) is growing rapidly, yet few studies focus on its application in volunteer-driven English as a Second Language (ESL) teaching contexts. This study explores how integrating ChatGPT-4 can support volunteer ESL teachers in lesson planning and task design. The research investigates the role of GAI in addressing the challenges faced by untrained volunteer ESL teachers, such as limited resources, time constraints, and lack of training. Using Activity Theory as a framework, this qualitative case study analyses the interactions between volunteer teachers and ChatGPT-4 over four weeks. Findings suggest that ChatGPT-4 enhances lesson planning by saving time, increasing teacher confidence, and generating useful materials. However, it also highlights the necessity for teachers to critically evaluate GAI suggestions to ensure quality. This study contributes to the literature by demonstrating GAI's potential to enhance pedagogical practices in community-based ESL contexts and proposes a balanced approach to GAI integration, emphasising the importance of human oversight. Future research should focus on longitudinal studies, incorporating learner feedback, and exploring broader ELT and English language learning (ELL) contexts to build on this work.Keywords: ESL; volunteer teaching; generative artificial intelligence; lesson planning; Activity Theory; ChatGPT-4Part of the Special Issue Activity theory in technology enhanced learning research (part 2)
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".