CRAILF: A Zero-Cost Python-Based Gamified Framework for Enhancing AI Literacy Among Rural High School Students

Purpose: AI literacy has become a core competency in K-12 education; however, rural high school students in China face severe equity barriers due to resource scarcity and costly tools. Existing frameworks heavily rely on commercial software and lack gamified narratives, personalized scaffolding, deep ethical focus, and process evaluation. This study proposes and evaluates the Cyber Rural AI Literacy Framework (CRAILF)—a zero-cost, pure Python gamified framework—to bridge these gaps and assess its impact on AI literacy, technical acceptance, and ethical awareness in low-resource rural contexts. Methodology: A mixed-methods single-group pre-post quasi-experimental design with process tracking was employed. Twenty rural high school students from Hebei Province participated in a 6-week offline intervention using open-source Python libraries (networkx, matplotlib, pygame). Data included logs, quizzes, heatmaps/radar charts, and TAM questionnaires, analyzed through statistics, effect sizes, t-tests, and coding. Findings: High acceptance was observed (TAM mean 5.8/7, Cohen’s d = 0.92), with overall AI literacy improving by 28% (d = 0.85). Perception/learning domains scored 80–82, while the ethics domain scored 68 (bias reflection increased by 15%). Personalized scaffolding was activated in 65% of cases, and disadvantaged students showed a 25% improvement. Originality/Value: This study presents the first pure Python zero-cost gamified AI literacy framework, integrating immersive scenarios and dynamic visualization, extending AI literacy theory and the I-TPACK model, and offering a replicable zero-cost pathway for equitable AI education in developing regions.