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Detailed feedback on courses and lecture content is essential for theirimprovement and also serves as a tool for reflection. However, feedback methodsare often only used sporadically, especially in mass courses, becausecollecting and analyzing feedback in a timely manner is often a challenge forteachers. Moreover, the current situation of the students or the changingworkload during the semester are usually not taken into account either. For aholistic investigation, the article used a digital survey format as formativefeedback which attempts to measure student stress in a quantitative part and toaddress the participants' reflection in a qualitative part, as well as tocollect general suggestions for improvement (based on the so-called One-MinutePaper) at two educational institutions. The feedback during the semester isevaluated qualitatively and discussed on a meta-level and special features(e.g. reflections on student work ethic or other courses) are addressed. Theresults show a low, but constant rate of feedback. Responses mostly covertopics of the lecture content or organizational aspects and were intensivelyused to report issues within the lecture. In addition, artificial intelligence(AI) support in the form of a large language model was tested and showedpromising results in summarizing the open-ended responses for the teacher.Finally, the experiences from the lecturers are reflected upon and the resultsas well as possibilities for improvement are discussed.

Scalable Two-Minute Feedback: Digital, Lecture-Accompanying Survey as a Continuous Feedback Instrument