Enhancing In-Service English Teachers’ Lesson Planning Skills Using Genre-Based Approach and AI for Teaching Fiction Reading
Bibliographic record
Abstract
This study addresses the gap in professional development resources for in-service English teachers in designing effective lesson plans for fiction reading. In particular, it explores how the integration of genre-based instruction (GBI) and Artificial Intelligence (AI) can support pedagogical planning in EFL contexts. GBI provides a structured framework to scaffold students’ reading through stages such as building field knowledge, supported reading, and genre-focused writing. AI tools, including ChatGPT, assist teachers in adapting texts, creating multimedia content, and designing story maps to support learners’ comprehension. The research employed a mixed-methods design involving eight in-service teachers enrolled in a graduate program in Thailand. Data were collected through pre- and post-tests, lesson plan evaluations, questionnaires, and semi-structured interviews. Results showed a statistically significant improvement in GBI knowledge (pre-test mean = 6.38; post-test mean = 14.00, *p* < .05) and an increase in lesson planning quality (mean scores between 4.63 and 5.00). Teachers further reported higher confidence and perceived usefulness of AI in supporting diverse learner needs. Future research should explore this integration across other genres and with larger, more diverse participant groups to assess long-term instructional outcomes and adaptability.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".