Many students may find math word problems uninteresting; therefore, lacking the motivation to solve them. The content in most math word problems in use today is outdated, deliberately generic, and does not fully engage students. The development of technologies that personalize math word problems seeks to improve the engagement of students. Research suggests that personalized problems improve student attitudes and interest in math. However, skepticism exists regarding their ability to positively impact student performance. The most commonly used method for math word personalization is the template-based approach that inputs students' interest questionnaire answers into slots within the template. Recent advances in math education and personalization research suggest that the template-based method may not provide content at the depth level that reflects the real-world experiences students have with their interests. Without engaging in relevant learning experiences, student academic interest may decline. The decline in student attitudes and academic interests may negatively impact their mathematics achievement. Additionally, the mathematics achievement gap among students of color and lower-socioeconomic status still exists while placing little emphasis on inclusive learning systems with personalized content. Thus, this research investigates the domains of math education technology and student engagement using natural language processing tools. This dissertation examines the feasible ways to engage students while practicing math word problem-solving and the existing challenges and opportunities in math technologies for teachers and students. This research seeks to address the challenges and to capitalize on the existing opportunities while enhancing user learning and engagement. In doing so, this work explores the design, implementation, and evaluation of a math word problem generator called InterestMe Math using the expectancy-value theoretical framework. Unlike most math word problem personalization systems, this system uses students' interest and natural language generation to personalize math word problems and further engage students and improve the foundational math skills of 5th and 6th graders. We examine the system by measuring the system's usability and the readability and cohesiveness of the generated word problems. Additionally, we measure the impact on student performance and interest. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]