ViLabS: Deep learning AI and computer vision-based smart website for improving basic laboratory techniques

Engineering notes

Cognitively, the experimental group achieved a significantly higher N-Gain (0.71) than the control (0.41) (p<0.05). Autonomously monitored psychomotor accuracy also significantly outperformed classical manual demonstrations (p<0.05).

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

To address excessive cognitive load and the lack of subject-based psychomotor evaluation in chemistry education, this study evaluates ViLabS, a deep-learning and computer vision website, for improving students' cognitive and psychomotor abilities. Using an explanatory sequential mixed-methods design, a quasi-experimental study involved 40 Grade X students selected via purposive sampling. Data were collected through cognitive tests, AI system logs, video-observation rubrics, and practicality questionnaires, followed by in-depth interviews. Results revealed ViLabS is highly feasible and practical (>92%). Cognitively, the experimental group achieved a significantly higher N-Gain (0.71) than the control (0.41) (p<0.05). Autonomously monitored psychomotor accuracy also significantly outperformed classical manual demonstrations (p<0.05). Thematic analysis confirmed the AI's instant corrective feedback minimized cognitive load and fostered precise muscle memory. Despite technical constraints such as internet and lighting dependency, ViLabS shows strong efficacy in accelerating theoretical understanding and kinesthetic proficiency. Future research should expand datasets and develop offline mobile applications. Ultimately, this study contributes a proactive, multimodal AI framework that advances the paradigm of AI-assisted laboratory learning.

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