Examining the Potential Benefits and Ethical Risks of GenAI in Lesson Planning: A TAM Approach
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) is a transformative technology in education, especially in lesson planning (LP). This research examines pre-service teachers' (PSTs) perceptions of GenAI benefits and ethical risks in LP, with consideration for the Technology Acceptance Model (TAM). The findings show that PSTs are generally cognizant of the benefits and ethical ramifications of GenAI use. PSTs demonstrated a positive attitude toward integrating GenAI in lesson planning and recognized the relevance and varying levels of incorporation into their current practice. The data also highlighted that the relationship of key TAM variables influenced how PSTs view and adopt GenAI. The findings provided support for the collect construct of perceived usefulness (PU) and perceived ease of use (PEU) mediating the relationship between attitude (ATT) and use (AU). These findings contributed relevant information to inform teacher education, illustrating the need for training that balances the practical benefits and ethical dimensions of GenAI. This research can serve as a starting point for future research, curricular design, and policy making regarding the responsible and informed use of GenAI in teacher preparation.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".