The “Healthy China” strategy and the “Year of Weight Management” initiative have elevated the weight and nutritional health of college students from an individual behavioral concern to a matter of educational governance. Existing interventions – such as physical education courses, campus runs, and fitness tests – rely on periodic assessments, which fail to sustain daily interventions targeting diet, exercise, and sleep. Large language models (LLMs), with their natural language understanding and multi-source data integration capabilities, can lower participation barriers and enhance the targeting and continuity of interventions. However, technological gains do not automatically translate into health benefits due to three primary challenges: data privacy, bodily discipline, and cost sustainability. Current research predominantly focuses on general digital tools or single scenarios, offering insufficient discussion on the specific characteristics and governance limitations of LLMs. Based on a definition that distinguishes LLMs from general health applications, this paper analyzes the logic and limitations of LLM empowerment in university weight and nutrition management. It proposes a framework centered on the digitization of cafeteria nutrition data, supported by incentive/constraint mechanisms and health literacy development, and safeguarded by multi-stakeholder collaboration and ethical governance. The study argues that LLMs should serve as auxiliary tools for students’ autonomous health management, and their effectiveness depends on the coordinated development of data governance, educational mechanisms, and ethical boundaries.
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Ye, X., Ye, M. (2026) The Logic, Limitations, and Pathways of Using Large Language Models to Support Weight and Nutritional Health Management Among College Students. Global Education Bulletin, 3(3), 81-86. https://doi.org/10.71052/grb2025/XQIP1785