Are pre-service teachers ready to teach the Alpha generation? The impact of pre-service teachers' ChatGPT literacy levels on behavioral intentions toward ChatGPT-4.0
Bibliographic record
Abstract
This study seeks to enhance our understanding of how pre-service teachers working with the Alpha Generation (PSTAG) interact with the Technology Acceptance Model (TAM) in the context of ChatGPT. It specifically examines their perceptions of ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI) toward ChatGPT-4o, utilizing an extended version of the TAM. The survey method was used, and 450 PSTAG participated in the current study. Data were collected through a survey including the ChatGPT literacy scale (ChatGPT-LS) and TAM to determine PSTAG’s ChatGPT-4o literacy level and its relationship with PEOU, PU, and BI. Thirteen hypotheses are developed to test the proposed model. All but one of the hypotheses are supported. This study shows that PEOU and PU play a key role in BI’s use of ChatGPT-4o, and the sub-dimensions of the ChatGPT-LS have a statistically significant effect on PEOU and PU. Technical proficiency was found to have no positive effect on PU. It can be suggested that PSTAG’s ChatGPT literacy level should be improved through courses to increase their behavioral intention to use ChatGPT-4o for educational purposes.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".