The Potential of AI Chatbots in Enhancing Lesson Planning in K-12 Education - Insights from Practicum-Experienced Teaching Graduate Students
Bibliographic record
Abstract
This study explored the usefulness of generative artificial intelligence (AI) chatbots as a support for teacher candidates’ K-12 lesson planning. The research utilized a mixed-methods approach that included surveys and interviews to explore how preservice teachers used generative AI during their practicum experiences. Findings revealed that approximately three-quarters of the teacher candidates used AI chatbots as an instructional support during practicum. These chatbots were used to help teacher candidates create lesson plans, develop interactive activities, and provide students with feedback. Teacher candidates felt these tools enhanced their efficiency and the educational quality of their lessons. However, some teacher candidates raised concerns about technical issues and the potential for over-reliance on technology. Additionally, the study discusses issues related to data privacy, intellectual theft, and other ethical considerations, underscoring the need for careful integration and oversight of AI technologies in educational settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".