Digital feedback is now a common part of higher education. Polls, quizzes, response systems, and other digital tools can bring more students into the lesson and help teachers see how the class is responding. But participation is not the same as learning. Students can answer questions and join discussions without noticing misunderstandings, questioning how confident they are, or changing the way they approach learning. This concern is at the heart of the chapter. Digital feedback is most valuable when it helps students check whether what they think they understand is reflected in what they can actually do. Using Student Response Systems (SRS) in flipped higher education as an example, the chapter looks at how this can happen through a sequence of pre-class preparation, diagnostic questions, comparison, discussion, and follow-up application. The aim is not to introduce a new teaching strategy. Rather, the chapter brings together familiar practices from flipped learning, Just-in-Time Teaching, Peer Instruction, formative feedback, and self-regulated learning, and examines them through the lens of metacognitive calibration. Students begin with their own sense of how well they understand the material. Diagnostic questions give them a chance to test that impression, while class response patterns can reveal where confidence and performance do not quite match. Discussion helps students make sense of those differences, and follow-up tasks allow them to rethink their reasoning and decide what to change next. The chapter also considers what these ideas mean for AI-enhanced formative feedback. Although the case does not involve AI, it highlights principles that should remain important as feedback becomes more automated: making students’ thinking visible, helping them assess their own understanding, supporting them in working through discrepancies, keeping the teacher’s role central, and giving learners opportunities to revise. The chapter concludes with practical design principles for educators and instructional designers.