Impact of Large Language Models on Personalized Learning, Assessment Automation, and Student Outcomes in Higher Learning Institution
DOI:
https://doi.org/10.70232/jtal.v2i1.22Keywords:
Large Language Models, Higher Education, Personalized Learning, Ethical ConcernsAbstract
This study investigated the multifaceted influence of Large Language Models (LLMs) on teaching and learning within a private higher education institution in Rwanda during the 2024–2025 academic year. A total of 658 students and 28 lecturers participated, providing a comprehensive perspective on both user experiences and professional concerns. Using a quantitative approach, the study employed Multivariate Analysis of Variance (MANOVA) to examine how the use of LLMs relates to students’ perceptions of personalized learning effectiveness, academic performance improvement, online engagement, satisfaction with assessment feedback, and motivation for lifelong learning. Findings from the student indicated that LLMs are widely perceived as beneficial across multiple dimensions of the learning process. Students reported that LLMs enhance personalized learning by providing adaptive guidance, improving academic performance through instant clarification and practice support, and increasing online engagement by offering interactive and accessible learning assistance. The results further showed that LLMs contribute to greater satisfaction with feedback mechanisms and stimulate motivation for continuous and self-directed learning. These statistically significant associations point to the strong potential of LLMs to enrich higher education outcomes. In contrast, the lecturers’ data revealed notable concerns related to data privacy, ethical use, and algorithmic bias. Lecturers expressed significant apprehension regarding students’ overreliance on LLMs, the risks associated with inaccurate or biased outputs, and the potential erosion of academic integrity. Their perceptions underscore the need for safeguards that ensure responsible and ethical use of AI in academic settings. Overall, the findings highlighted a dual reality: while LLMs hold transformative potential for improving learning experiences, their integration must be supported by robust institutional policies, targeted capacity-building initiatives, and ongoing research. Such measures are essential to promote equitable, ethical, and effective adoption of LLMs in higher education.
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