An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology
Bibliographic record
Abstract
Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of technology by practitioners. In the case of Artificial Intelligence (AI) driven learning analytics tools, the issue is even more complex due to practitioners’ AI-specific misconceptions, myths, and fears (i.e., mass unemployment and ethical concerns). In recent years, artificial intelligence has been introduced increasingly into K-12 education. However, little research has been conducted on the trust and attitudes of K-12 teachers regarding the use and adoption of AI-based Educational Technology (EdTech). The present study introduces a new instrument to measure teachers' trust in AI-based EdTech, provides evidence of its internal structure validity, and uses it to portray secondary-level school teachers' attitudes toward AI. First, we explain the instrument items creation process based on our preliminary research and review of existing tools in other domains. Second, using Exploratory Factor Analysis we analyze the results from 132 teachers’ input. The results reveal eight factors influencing teachers’ trust in adopting AI-based EdTech: Perceived Benefits of AI-based EdTech, AI-based EdTech's Lack of Human Characteristics, AI-based EdTech's Perceived Lack of Transparency, Anxieties Related to Using AI-based EdTech, Self-efficacy in Using AI-based EdTech, Required Shift in Pedagogy to Adopt AI-based EdTech, Preferred Means to Increase Trust in AI-based EdTech, and AI-based EdTech vs Human Advice/Recommendation. Finally, we use the instrument to discuss 132 high-school Biology teachers' responses to the survey items and to what extent they align with the findings from the literature in relevant domains.The contribution of this research is twofold. First, it introduces a reliable instrument to investigate the role of teachers’ trust in AI-based EdTech and the factors influencing it. Second, the findings from the teachers’ survey can guide creators of teacher professional development courses and policymakers on improving teachers’ trust in, and in turn their willingness to adopt, AI-based EdTech in K-12 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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".