Integrating ChatGPT to Enhance University Students’ Communication Skills: Pedagogical Considerations
Bibliographic record
Abstract
This research examines the impact of integrating generative AI (ChatGPT) on 93 Thai university students’ English communication skills and their perceptions of AI integration. Initially, the students completed a video-recorded conversation simulation on a predetermined topic to be assessed using a scoring rubric, representing their pre-simulation performance. Subsequently, the students engaged in structured instruction. They collaborated in small groups with ChatGPT to create conversation scripts on the same topic, which they later used for post-simulation video recordings, representing the post-simulation performance. A focus group discussion with ten randomly selected participants was conducted to explore the students’ perceptions. A comparison of pre- and post-simulation scores revealed significant improvements, particularly in fluency, vocabulary usage, and overall communication skills. Additionally, focus group findings indicated highly positive sentiments about ChatGPT, emphasizing its role in reducing anxiety, promoting creativity and enthusiasm, and enhancing the interactivity and enjoyment of language learning. This study underscores ChatGPT’s potential as a valuable resource in language education, particularly when combined with teacher support and oversight. By generating scripts grounded in sound pedagogical principles, AI creates an interactive environment for enhancing conversational English, fostering active engagement, and meeting students’ diverse learning needs. Although the findings are promising, the research highlights the necessity of aligning AI implementation with pedagogical principles to guarantee valuable learning experiences. This research contributes to the expanding field of literature on AI in education, offering insights into how tools like ChatGPT can enhance communication abilities and prepare students for real-world applications in academic and professional contexts.
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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 | 0.017 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".