Digital Meritocracy or Hidden Inequality? AI Assisted Thesis Writing among Low Income Sports Students under the Common Prosperity Policy in China
Main Article Content
Abstract
This study examines the impact of Gen AI tools on academic equity among undergraduate sports students in China under the common prosperity policy. Using a mixed-methods approach, 25 graduation theses each from low-income and non-low-income groups at a university in Yunnan, China were collected and semi-structured interviews were conducted with 18 students. Quantitative content analysis and Mann-Whitney U test were used to compare group differences. Quantitative analysis showed that low-income students lagged significantly behind non-low-income students in originality, analytical depth, structural coherence, and AI trace evidence, while no significant difference was observed in disciplinary appropriateness. Qualitative findings revealed that these gaps were driven by multiple usage strategies and resource access pathways. The results indicated that the spread of AI technology has not automatically eliminated educational inequality but instead reproduces the digital divide through access conditions, usage skills, algorithmic register bias, and unbalanced educational support. Based on this, four intervention strategies were proposed: centrally deploying a campus-wide AI writing platform, embedding AI literacy training into writing courses, improving AI usage guidelines and feedback mechanisms, and implementing human-machine collaborative teaching models to harness AI’s positive potential for educational equity.
Highlights
Provides a mixed methods analysis of AI-assisted thesis writing among low income sports students in China under the Common Prosperity policy.
Reveals significant disparities in originality, analytical depth, structural coherence, and AI trace evidence between low income and non low income students, while disciplinary appropriateness shows no group difference.
Identifies structural mechanisms including access conditions, usage strategies, algorithmic register bias, and lack of educational support that reproduce the digital divide in AI writing.
Integrates Van Dijk’s digital divide model and Sen’s capability approach to explain why equitable access does not guarantee equitable capability conversion.
Proposes four targeted interventions: centralized AI platform provision, embedded AI literacy training, institutional usage guidelines, and human AI collaborative teaching models.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright: CC BY-NC-ND 4.0
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