A mixed methods study of the three low trap in high school information technology teaching in underdeveloped China in the AI era

In 2025, with the rapid iteration of AI technology, global educational equity faces the challenge of the “AI divide,” particularly in high school information technology teaching in underdeveloped regions of China. This diagnostic-exploratory mixed-methods study employs automated pipelines for efficient data collection and analysis. Drawing on the Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and Technology Readiness Index (TRI), the study proposes a conceptual “three-dimensional dynamic interaction model” and explores potential association patterns among infrastructure, teacher attitudes, and policy implementation that may contribute to a “three-low trap” (low infrastructure, low teacher capability, low policy implementation) under China’s urban-rural dual structure. Methods: A sequential explanatory mixed design was adopted. First, 189 publicly available online comments (2024–2025) were collected from multiple platforms using an n8n + Crawl4AI automated pipeline. Second, BERT-based sentiment proxy analysis and LDA topic modeling were performed. Finally, triangulation was conducted with 13 semi-structured teacher interviews. Exploratory patterns suggest an overall average willingness proxy score of 1.04/5 (positive rate 0.98%). Preliminary evidence indicates a negative association between low infrastructure and teacher attitudes (β = -0.45, p < 0.01), with LDA topic analysis showing infrastructure accounting for 28% of themes and fsQCA revealing configuration paths with 80.17% coverage. Cross-national comparisons (e.g., Saudi Arabia’s Vision 2030) highlight the distinctive role of China’s urban-rural dual structure. This study’s main contribution lies in its exploratory analysis of potential dynamic association patterns related to the “three-low trap.” The automated pipeline served solely as an auxiliary data collection tool. Practical implications are presented as context-specific suggestions inspired by the exploratory findings. This diagnostic-exploratory study fills a gap in the literature on multidimensional interaction mechanisms in low-resource AI education settings and offers preliminary Chinese perspectives for global low-resource contexts. Future longitudinal research is needed to validate these patterns.