Optimizing Academic Engagement and Mental Health through AI: An Experimental Study on LLM Integration in Higher Education

Min Zhang^*

• Xi'an International University, Xi'an, China

Background: In alignment with UNESCO's Sustainable Development Goal 4 (SDG4), which advocates for inclusive and equitable quality education, the integration of Artificial Intelligence tools—particularly Large Language Models (LLMs)—presents promising opportunities for transforming higher education. Despite this potential, empirical research remains scarce regarding the effects of LLM use on students' academic performance, mental well-being, and engagement, especially across different modes of implementation. Objective: This experimental study investigated whether a guided, pedagogically grounded use of LLMs enhances students' academic writing quality, perceived mental health, and academic engagement more effectively than either unguided use or no exposure to LLMs. The study contributes to UNESCO's "Futures of Education" vision by exploring how structured AI use may foster more inclusive and empowering learning environments. Method: A total of 246 undergraduate students were randomly assigned to one of three conditions: guided LLM use, unguided LLM use, or a control group with no LLM access. Participants completed a critical writing task and standardized instruments measuring academic engagement and mental well-being. Prior academic achievement was controlled for, and writing quality was assessed using Grammarly for Education. Results: Students in the guided LLM condition achieved significantly higher scores in writing quality and academic engagement compared to the control group, with large and moderate effect sizes, respectively. Modest improvements in mental health indicators were also observed. By contrast, unguided use yielded moderate gains in writing quality but did not produce significant effects on engagement or well-being. Conclusion: The findings highlight the critical role of intentional instructional design in the educational integration of AI tools. Structured guidance not only optimizes academic outcomes but also supports students' well-being and inclusion. This study offers empirical evidence to inform ongoing debates on how digital innovation can contribute to reducing educational disparities and advancing equitable learning in the post-pandemic era.