This dissertation explores the use of natural language processing (NLP) and large language models (LLMs) to analyze student self-regulated learning (SRL) strategies in response to exam wrappers. Exam wrappers are structured reflection activities that prompt students to practice SRL after they get their graded exams back. The dissertation consists of three manuscripts that compare traditional qualitative analysis with NLP-assisted approaches using transformer-based models including GPT-3.5, a state-of-the-art LLM. The data set comprises 3,800 student responses from an engineering physics course. The first manuscript develops two NLP-assisted codebooks for identifying learning strategies related to SRL in exam wrapper responses and evaluates the agreement between them and traditional qualitative analysis. The second manuscript applies a novel NLP technique called zero-shot learning (ZSL) to classify student responses into the codes developed in the first manuscript and assesses the accuracy of this method by evaluating a subset of the full dataset. The third manuscript identifies the distribution and differences of learning strategies and SRL constructs among students of different exam performance profiles using the results from the second manuscript. The dissertation demonstrates the potential of NLP and LLMs to enhance qualitative research by providing scalable, robust, and efficient methods for analyzing large corpora of textual data. The dissertation also contributes to the understanding of SRL in engineering education by revealing the common learning strategies, impediments, and SRL constructs that students report they use while preparing for exams in a first-year engineering physics course. The dissertation suggests implications, limitations, and directions for future research on NLP, LLMs, and SRL. / Doctor of Philosophy / This dissertation is about using artificial intelligence (AI) to help researchers and teachers understand how students learn from their exams. Exams are not only a way to measure what students know, but also a chance for students to reflect on how they studied and what they can do better next time. One way that students can reflect is by using exam wrappers, which are short questions that students answer after they get their graded exams back. A type of AI called natural language processing (NLP) is used in this dissertation, which can analyze text and find patterns and meanings in it. This study also uses a powerful AI tool called GPT-3.5, which can generate text and answer questions. The dissertation has three manuscripts that compare the traditional way of analyzing exam wrappers, which is done by hand, with the new way of using NLP and GPT-3.5, evaluate a specific promising NLP method, and use this method to try and gain a deeper understanding in students self-regulated learning (SRL) while preparing for exams. The data comes from 3,800 exam wrappers from a physics course for engineering students. The first manuscript develops a way of using NLP and GPT-3.5 to find out what learning strategies and goals students talk about in their exam wrappers and compares it to more traditional methods of analysis. The second manuscript tests how accurate a specific NLP technique is in finding these strategies and goals. The third manuscript looks at how different students use different strategies and goals depending on how well they did on the exams using the NLP technique in the second manuscript. I found that NLP and GPT-3.5 can aid in analyzing exam wrappers faster and provide nuanced insights when compared with manual approaches. The dissertation also shows what learning strategies and goals are most discussed for engineering students as they prepare for exams. The dissertation gives some suggestions, challenges, and ideas for future research on AI and learning from exams.