Panel Leader: Lawrence Susskind (MIT)
Lawrence Susskind (MIT)
Panel Leader and Discussant
Laura Wang, Massachusetts Institute of Technology
The Negotiation Skills Assessment: Developing and Validating an AI-Powered Measure of Negotiation Proficiency
This study introduces the Negotiation Skills Assessment (NSA), an innovative framework and tool that integrates traditional assessment techniques with AI-driven negotiation simulations to evaluate core negotiation skills. By leveraging advancements in AI, the NSA addresses persistent challenges in negotiation assessment, such as biases in self-reported evaluations and the overemphasis on objective outcomes. A pilot implementation revealed significant improvements in students’ negotiation skills following a three-day negotiation course, highlighting the effectiveness of the NSA. These findings demonstrate the potential of combining AI technologies with established evaluation methods to develop scalable, accurate, and engaging tools for assessing and enhancing negotiation abilities.
Emmanuel Dorley, University of Florida
Virtual Agents for Personalized Negotiation Training
Advancements in AI have made it possible to develop agents capable of engaging in negotiation, offering new opportunities for personalized training. Despite its importance, negotiation remains underemphasized in most disciplines and, specifically, in engineering education. In this talk, I will present our research on using AI agents as role-playing partners to help students learn how to negotiate. The approach allows students to practice learned negotiation strategies with an agent and receive personalized feedback on their performance. I will also discuss our ongoing work to enhance these simulations, making negotiation training more accessible, scalable, and personalized.
Samuel “Mooly” Dinnar, Massachusetts Institute of Technology
Negotiation Coaching Bots and Backtable Bots: Using GenAI to Improve Human-to-Human Interactions in Multiparty Negotiation Instruction
This session describes the Negotiation Coaching Bots using GenAI that were developed for teaching in two muti-party negotiation courses at MIT (Spring 2024) and the resulting papers about the experience. Three kinds of bots were created for various stages: a PREPARATION coaching bot for each role-player before entering the actual human-human multi-party interaction in an upcoming specific simulation; a BACKTABLE bot that allows learners to interact with (imaginary) backtable stakeholders from the assigned negotiation partners; and a DEBRIEF bot that coaches individuals to reflect deeply after their negotiation. We share our process of Seven Steps to Building GenAI Negotiation Bots, paying specific attention to the important Four Aspects of Prompt Design, and how we provided the students a choice about their coach’s personality style. We’ll present data from the students’ learning steps and reactions to being coached with bots in 2 different 6-person role-plays, emphasizing the importance of context and asking-questions to productive coaching, and to developing the students’ Personal Theory of Practice. We’ll conclude with ideas and recommendations regarding the further possible development of Negotiation Coaching Bots, and how they can be used in a wide range of teaching and training settings.