Development and Evaluation of a Gesture Recognition-Based Artificial Intelligence Science Popularization System
Bibliographic record
Abstract
Currently, the Chinese government is actively promoting the integration of artificial intelligence (AI) technology into secondary school classrooms. However, the lack of teaching resources, heavy academic pressure, and unexpected public health events hinder the normal development of popular science teaching. In this study, based on the AI curriculum requirements in Beijing's Haidian District, a remote-control system for experiential teaching was developed using Mediapipe's keypoint recognition technology. This system enables remote control of smart homes and manipulation of robotic arm movements, allowing students to experience AI technology in a multi-sensory manner and overcome spatial limitations. To evaluate the effectiveness of this system in fostering students' AI literacy, 40 students from a high school in Hunan Province were selected as the research subjects. The t-value of the test scores between the experimental group and the control group was found to be 4.173. The results indicate that the adoption of the popular science system as a learning aid significantly improves students' mastery of AI knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".