Assessments of natural language can provide vast information about individuals' thoughts and cognitive process, but they often rely on time-intensive human scoring, deterring researchers from collecting these sources of data. Natural language processing (NLP) gives researchers the opportunity to implement automated textual analyses across a variety of psychological domains. NLP techniques can be used to score written language and provide instantaneous feedback. For example, the Reading Strategy Assessment Tool (RSAT) can score short constructed responses produced while students read texts for presence of various comprehension processes. Another application of NLP is automated essay scoring (AES), which has been used in high-stakes tests. AES systems are developed using machine learning techniques: a corpus of essays is scored by human raters and split into a training and test set. A model is trained to predict human ratings of essays using linguistic features derived using NLP. AES is now often as accurate as expert human raters. While AES systems simply provide scores, automated writing evaluation (AWE) systems also provide feedback on student writing. A continued challenge in both AES and AWE systems is ensuring accurate scoring across a variety of genres, prompts, topics, modes of writing, and writer characteristics. Novel approaches suggest that NLP techniques can be used not only for assessment, but also to better understand how natural language can reveal individual characteristics. For instance, the cohesion of self-explanations can be used to predict the coherence of a reader's mental representation of a text. NLP techniques have also been used to examine emotions and other psychological states from written responses, using tools such as the Linguistic Inquiry and Word Count (LIWC) and WordNet-Affect. Additionally, NLP has been used to increase student learning through implementation in a variety of intelligent tutoring systems (ITSs). When users provide open-ended responses to an ITS, NLP techniques are used to score the responses and provide automated feedback. AutoTutor is an ITS that provides instruction on technical topics such as computer literacy. After being asked a question, students engage in a dialogue of approximately 20 to 100 conversational turns with AutoTutor. The tutor uses NLP to determine how closely a student's response matches the expected answer; if the match is not sufficiently close, the tutor may pump the student for information, provide hints and prompts, or provide the answer, depending on the student's response. Another example is the Interactive Strategy Trainer for Active Reading and Thinking (iSTART), an ITS that helps high school and adult students learn and practice reading comprehension strategies. Students write self-explanations of texts as they read, and their self-explanations are assessed using a series of NLP algorithms. A pedagogical agent provides feedback based on various features of the student's response. These various applications of NLP indicate its great potential for use in learning, assessment, and exploring the relations between language and psychological processes. [This chapter was published in: "APA Handbook of Research Methods in Psychology, Second Edition: Vol. 1. Foundations, Planning, Measures, and Psychometrics," edited by H. Cooper et al., American Psychological Association, 2023, pp. 361-380.]