Student-constructed responses, such as essays, short-answer questions, and think-aloud protocols, provide a valuable opportunity to gauge student learning outcomes and comprehension strategies. However, given the challenges of grading student-constructed responses, instructors may be hesitant to use them. There have been major advances in the application of natural language processing of student-constructed responses. This literature review focuses on two dimensions that need to be considered when developing new systems. The first is type of response provided by the student—namely, meaning-making responses (e.g., think-aloud protocols, tutorial dialogue) and products of comprehension (e.g., essays, open-ended questions). The second corresponds to considerations of the type of natural language processing systems used and how they are applied to analyze the student responses. We argue that the appropriateness of the assessment protocols is, in part, constrained by the type of response and researchers should use hybrid systems that rely on multiple, convergent natural language algorithms.