Establishing Reliability and Validity for a Survey Tool Testing Higher Education Teachers’ Intentions to Adopt Artificial Intelligence in Their Teaching Practices
Bibliographic record
Abstract
Continued efforts to incorporate the use of artificial intelligence within post-secondary education efforts requires its incorporation by teachers in their day-to-day educational practices. Generative artificial intelligence, through platforms like ChatGPT, introduce opportunities and cautions to its’ use with teaching-learning, but it is equally important to consider a more comprehensive picture of artificial intelligence’s (AI) use within education. The merits of using AI in teaching practices to support student learning may be undervalued by teachers and therefore under utilised. A paucity of available tools to explore teachers’ intentions to use AI is a pressing issue. The creation and testing for reliability and validity of one tool, the Artificial Intelligence Acceptance Measurement Survey (AIAMS) is discussed in this article. It is believed that the AIAMS will serve to support further research and understanding of what influences teachers and their intentions to use AI within their teaching efforts. The important role teachers realise with student learning suggests that research focused on this area is imperative as we move forward in working with AI 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 | 0.062 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".