Research in artificial intelligence has made great strides in developing 'cognitive tutors' that teach a range of technical skills. These automated tutors allow students to practice, observe their mistakes, and provide personalized instructional feedback. Evidence shows that these methods can increase learning above and beyond traditional classroom instruction in topics such as math, reading, and computer science. Although such skills are crucial, students entering the modern workforce must possess more than technical abilities. They must exhibit a range of interpersonal skills which allows them to resolve conflicts and solve problems creatively. However, both traditional curricula and learning technologies afford students limited opportunities to learn these interpersonal skills, particularly in STEM fields. This thesis seeks to fill this gap by developing automated learning methods for teaching the crucial interpersonal skill of negotiation. Negotiation skills can help workers obtain equitable compensation, gain greater control over their work responsibilities, and help them work effectively with teammates. Currently, students often must resort to costly resources to learn how to negotiate. Options include self-study guides with limited value, or costly professional training programs that are developed by various companies and educational institutions. For those who can not afford costly training options, they find themselves at a loss for options. However, technology is beginning to fill this gap. AI has made strides in creating agents that can negotiate with people, and research shows students can improve their negotiation abilities by practicing with such agents . To date, these methods allow students to practice but little emphasis is placed on the analysis of mistakes and feedback. Yet lessons from cognitive tutors emphasizes that feedback is crucial for learning . To address this limitation, this thesis contributes to advancing the science of interpersonal skill training by developing and evaluating the effectiveness of an automated analysis of student errors and personalized feedback in a negotiation training system. This is done by innovating artificial intelligence techniques to analyze student behavior, identify weakness in their understanding, and provide targeted and personalized feedback. In Chapter 2, I provided an overview of the state of the art in teaching negotiation and their limitations. In Chapters 3 and 4, I develop metrics for assessing student's negotiation abilities based on a set of principles and show that these metrics can be used to provide personalized feedback. In Chapter 5, I extend these metrics to include better predictors of negotiator's understanding of an opponent. Chapter 6 builds on the previous chapters by incorporating the opponent modeling and feedback proposed in Chapters 3, 4, and 5 into a mini negotiation course. I show how my previous work can be combined with video lectures to mimic how negotiation is taught in the classroom. Results show that these metrics are good predictors of negotiation outcomes, and participants who received personalized feedback do fair better in subsequent negotiations than those who did not. Lastly, I show that these diagnostic metrics do have applications outside of a training setting. Chapter 7 illustrates how these methods can provide insight into societal issues. [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://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]