The Effectiveness of the Integration of ChatGPT into Flipped Classrooms from Teachers’ and Learners’ Perspectives
Bibliographic record
Abstract
This study investigates the integration of ChatGPT, an advanced AI language model, into the flipped classroom model for language teaching and its impact on learning outcomes. The flipped classroom approach shifts instructional content delivery to pre-class activities and emphasizes active learning during class time, potentially enhancing student engagement and academic performance. This research explores how ChatGPT can further augment this model by providing interactive and personalized language learning materials before class and facilitating dynamic in-class activities. Using a mixed-methods research design, qualitative data from surveys and interviews were analyzed first to provide deeper insights into the students' experiences and attitudes towards ChatGPT-supported learning. Quantitative data were then collected through a survey of 124 students across different levels of language learning (Year 2, Year 3, and Year 4), focusing on perceived effectiveness of ChatGPT, levels of engagement and interaction, potential negative impacts, and overall satisfaction. Findings indicate that the integration of ChatGPT significantly enhances student engagement, language proficiency, and overall satisfaction with the learning process, while also highlighting some concerns regarding its use. This study contributes to the growing body of literature on AI in education, demonstrating the potential of ChatGPT to transform language teaching and learning in a flipped classroom setting.
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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 | 0.005 | 0.036 |
| 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 | 0.001 |
| 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, 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".