While providing feedback is a critical task in education, assessing open-ended responses and crafting personalized feedback for each student is time-consuming and challenging. Large Language Models (LLM) can be used to support this process by leveraging their natural language understanding capabilities to evaluate open-ended answers and generate automated feedback. However, in real-world learning environments, empirical gaps remain regarding: I) How do students perceive LLM-generated feedback compared to teacher-provided feedback? and II) What impact does LLM assistance have on instructor feedback practices? Thus, this paper presents a controlled experiment with 60 secondary school students in Brazil, comparing traditional teacher feedback with LLM-assisted feedback using the Tutoria platform. Results showed no significant difference in students’ perceptions of feedback quality between the two approaches, with 85% unable to distinguish LLM-generated feedback from teacher feedback. Notably, LLM assistance produced feedback messages 2.6 times longer (p < 0.001) without significantly increasing grading time (p = 0.047, ES = 0.09).