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Teachers often assign text comprehension activities to their students and provide them with feedback on the correctness of their answers for learning purposes. In traditional classrooms, teachers have little to no chance to deliver individual feedback in a suitable and timely manner. Digital environments overcome these limitations. Computer-based systems provide researchers and practitioners with the opportunity to trace students' actions while performing tasks, and to immediately transform the data into individualized feedback messages. Elaborated Feedback (EF) has proven to be more effective than mere corrective feedback, such as Knowledge-of-Response feedback (KR, e.g., Correct/ Incorrect) or Knowledge-of-Correct-Response feedback (KCR, e.g., The correct response is X). EF may include additional information, such as monitoring hints, feedback about the student’s accuracy in assessing textual relevance, or inference prompts. When performing question-answering tasks, students are expected to deploy both comprehension and specific reading skills (e.g., searching the text for task-relevant information). This article examines recent research recording online measurements on how students perform in question-answering tasks and how they react to different forms of EF on their question-answering processes. Overall, the results suggest that computer-based EF may improve both comprehension performance outcomes and text processing strategies, although students often pay more attention to the corrective part of feedback messages than to the additional information of the EF, and they rarely engage deeply in processing feedback. Theoretical and practical contributions are discussed.