AI Chatbot Simulations in Teacher Training: Core Teaching Competencies Developed Through Virtual Practice
Bibliographic record
Abstract
This study examines the role of AI chatbots in simulating real-world teaching scenarios and developing core teaching competencies among pre-service teachers. Guided by the SAMR model, the research employed a single-case qualitative design involving seven Bachelor of Physical Education interns from a teacher education institution in the Philippines. Data were gathered exclusively through focus group discussions, which explored the participants’ experiences using AI chatbots during their practicum. Thematic analysis revealed that chatbot simulations contributed to the development of six key teaching competencies: instructional planning and structuring, content mastery and clarification, designing engaging activities, communication and language precision, reflective practice and pedagogical decision-making, and professional confidence and self-efficacy. Participants described AI chatbots as helpful rehearsal partners that allowed them to clarify concepts, refine lesson plans, anticipate student reactions, and improve instructional language in a low-pressure setting. The chatbot interactions also prompted critical reflection on teaching strategies and enhanced the interns’ confidence before entering the actual classroom. Findings suggest that AI-powered simulations can be meaningfully integrated into teacher education programs to bridge the gap between theory and practice. The study recommends embedding AI-supported activities into practicum courses and providing guidance on the ethical and pedagogical use of AI tools in education.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".