Keywords
rural high school education,
gamified learning
personalized scaffolding
zero-cost framework
Python programming
ethical intervention
process evaluation
urban-rural digital divide
inclusive education
How to Cite
Abstract
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.
References
Akman, E. (2025). The impact of AI-based visual designs on students' AI literacy and attitudes toward AI. Interactive Learning Environments, 33(8), 5118–5136. https://doi.org/10.1080/10494820.2025.2530630
Chiu, T. K. F. (2026). Intelligent-TPACK (I-TPACK) framework developed from TPACK through integration of artificial intelligence literacy and competency. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2026.2615818
Deng, L. (2025). Shengcheng shi rengong zhineng funeng gexinghua xuexi de luoji yu shixian lujing [Logic and implementation path of generative artificial intelligence empowering personalized learning].
Guangdong Sheng Jiaoyu Ting. (2024). Guangdong sheng zhong xiaoxue xuesheng rengong zhineng suyang kuangjia (shixing) [Guangdong Province primary and secondary school students' artificial intelligence literacy framework (trial)]. http://edu.foshan.gov.cn/attachment/0/534/534607/6554307.pdf
Johansen, F., Stalheim, O. R., Løvsletten, M., Furulund, L., & Toft, H. (2025). Investigating virtual reality and the virtual medicine room for nursing education: Student perceptions and technology acceptance. Interactive Learning Environments, 33(10), 6124–6137. https://doi.org/10.1080/10494820.2025.2491629
Kassenkhan, A. M., Moldagulova, A. N., & Serbin, V. V. (2025). Gamification and artificial intelligence in education: A review of innovative approaches to fostering critical thinking. IEEE Access.
Konstantinidou, A., Nisiforou, E. A., & Vrasidas, C. (2026). Enhancing personalized learning with artificial intelligence and analytics: University staff's insights. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2026.2619907
Lan, G. S., et al. (2025). Quanqiu shijiao xia jiaoyuzhe rengong zhineng suyang kuangjia: Neirong jiagou, shijian shili he yingyong celüe [Educator artificial intelligence literacy framework from a global perspective: Content architecture, practical examples, and application strategies]. Kaifang Jiaoyu Yanjiu [Open Education Research], 31(2), 55–66.
Liang, B. Y. (2025). Shengcheng shi rengong zhineng funeng gaozhong xinxi jishu ketang jiaoxue de tiaozhan yu yingdui celüe [Challenges and coping strategies of generative artificial intelligence empowering high school information technology classroom teaching]. Chuangxin Jiaoyu Yanjiu [Innovation Education Research], 13(8), 104–111.
Shang, J. J., & Jiang, Y. (2024). Youxihua xuexi: Rang xuexi geng kexue, geng kuaile, geng youxiao [Gamified learning: Making learning more scientific, happier, and more effective].
Shi, Y., & Mao, Y. H. (2024). Rengong zhineng suyang de gainian, kuangjia yu jiaoyu [Concepts, frameworks, and education of artificial intelligence literacy]. Tushuguan Luntan [Library Tribune], 44(11), 90–100.
Touretzky, D. S., Gardner-McCune, C., Martin, F., & Seehorn, D. (2021). Five big ideas in artificial intelligence. AI4K12 Initiative. https://ai4k12.org/wp-content/uploads/2021/01/AI4K12_Five_Big_Ideas_Poster-1.pdf
Wang, F., Zhou, X., Li, K., Cheung, A. C. K., & Tian, M. (2025). The effects of artificial intelligence-based interactive scaffolding on secondary students' speaking performance, goal setting, self-evaluation, and motivation in informal digital learning of English. Interactive Learning Environments, 33(7), 4633–4652. https://doi.org/10.1080/10494820.2025.2470319
Wang, Q., Huang, L. M., & Wang, R. N. (2025). AI-TPACK kuangjia xia gaoxiao Yingyu jiaoshi shuzi suyang tisheng lujing yanjiu [Research on the path to improving digital literacy of college English teachers under the AI-TPACK framework]. Jiaoyu Jinzhan [Advances in Education], 15(10).
Xie, M. J., & Luo, L. L. (2025). The status quo and future of AI-TPACK for mathematics teacher education students: A case study in Chinese universities. arXiv. https://doi.org/10.48550/arXiv.2503.13533
Xue, L., Mahat, J., & Ghazali, N. (2026). Technology acceptance model in artificial intelligence in education: A meta-analysis. SAGE Open.
Zhou, X., Li, Y., Chai, C. S., & Chiu, T. K. F. (2025). Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 education from a systematic review. Interactive Learning Environments, 33(10), 5766–5788. https://doi.org/10.1080/10494820.2025.2487538
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