Modeling the relationships between secondary school students’ AI learning attitude, AI literacy and AI career interest
Bibliographic record
Abstract
The importance of artificial intelligence (AI) literacy has grown significantly, and there is a rapidly increasing demand for AI professionals. However, AI faces a talent shortage, and the widening gender gap exacerbates this issue. Given the crucial role of secondary schools in nurturing students’ interest in AI and shaping their career paths, this study sought to investigate the link between secondary school students’ attitude toward AI learning, their AI literacy, and their interest in AI careers while examining possible gender differences. We used the AI Learning Attitude Survey, AI Literacy Survey, and AI Career Interest Survey to collect data from 622 secondary school students who were selected with a stratified sampling method. The survey data were then analyzed using structural equation modeling. The findings revealed that: (1) female students demonstrate lower levels of AI learning attitude, AI literacy, and AI career interest compared to their male counterparts, whereas no gender differences were found in the model of learning attitude-AI literacy-career interest; (2) positive AI learning attitude and AI literacy predict higher AI career interest, with learning attitude positively influencing AI literacy; (3) AI literacy significantly mediates the relationship between AI learning attitude and AI career interest. The findings highlight the necessity for K-12 schools to introduce both formal and informal AI education initiatives at an early stage. By fostering inclusive and supportive environments, schools can inspire all students, especially girls, to boost their AI literacy and consider career opportunities in this field. Such efforts will play a vital role in advancing diversity, equity, and inclusion within the AI sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".