In this pilot study, we explored the use of generative AI—specifically GPT-4—to evaluate student feedback in a bilingual software engineering course offered at the University of Turku. Our aim was twofold: to examine whether ChatGPT can meaningfully evaluate student course feedback and propose suitable enhancements, and to compare its evaluations with those made by a course teacher. We collected voluntary feedback from 18 consenting students across three course instances in 2023 and 2024, resulting in a total of 390 feedback entries. These responses were first translated into English and then anonymized. Using structured questionnaires aligned with defined pedagogical goals, we then analyzed the responses through a dual evaluation process: (1) AI-based assessment using a custom JavaScript application integrating GPT-4 and GPT-4o-mini, and (2) manual evaluation by the teacher. Both followed a standardized Likert-scale format with brief textual comments, and all evaluations were consolidated into thirty-six manually maintained recording sheets. Evaluation results were visualized using heat maps across five key themes derived from the pedagogical goals. Our comparative analysis showed general alignment between the two evaluators, with key differences in the perceived content clarity and video quality of the course. We further extended our discussion to examine GPT’s applicability and limitations as a feedback evaluator. In particular, we identified its potential to quickly assess structured student feedback in courses with high participation, where manual evaluation may be time-consuming for course teachers. These findings collectively provide insights into using generative AI in course feedback analysis to enhance teaching within software engineering curricula.