n academic year 2023/24, our university collected over 200,000 student feedback responses evaluating teaching staff and course experiences. The survey included demographic data, 10 Likert scale questions on teaching quality, a question on student attendance, and three open-ended questions about student experiences. This paper explores the integration of Large Language Models (LLM) Gemini for sentiment analysis to evaluate students’ feedback quantitatively and qualitatively. We statistically analyze the Likert scale responses. To address the linguistic diversity of open-ended responses, written in both Cyrillic and Latin scripts with standard and slang expressions in several languages, we employed a preprocessing step using Gemini to standardize the input for further analyses. Sentiment analysis aims to identify various sentiment nuances, including direct answers, contradiction, multipolarity, mixed sentiment, sarcasm, irony, negation, ambiguity, understatement, and over-exaggeration. By comparing these insights with quantitative feedback, we aim to uncover deeper patterns between student perceptions and teaching performance. While the focus is on sentiment analysis, we also discuss the evaluation of the results provided by LLM. For the sentiments with less answers, the evaluation of GenAI was done manually. For the sentiments with more than 1000 entries, we suggest a semi-automated approach for sentiment categorization, to be explored in future work. This study enhances our understanding of student feedback through advanced computational methods, providing a more nuanced perspective on teaching quality and student satisfaction.