Samuel Dinnar, C. Dede, Emmanuel Johnson, C. Straub, Kristjan Korjus
{"title":"Artificial Intelligence and Technology in Teaching Negotiation","authors":"Samuel Dinnar, C. Dede, Emmanuel Johnson, C. Straub, Kristjan Korjus","doi":"10.1111/NEJO.12351","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI), machine learning (ML), affective computing, and big-data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role-play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology-based negotiation systems, including avatars and computer-controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.","PeriodicalId":46597,"journal":{"name":"Negotiation Journal","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/NEJO.12351","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Negotiation Journal","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1111/NEJO.12351","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 8
Abstract
Artificial intelligence (AI), machine learning (ML), affective computing, and big-data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role-play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology-based negotiation systems, including avatars and computer-controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.
期刊介绍:
Negotiation Journal is committed to the development of better strategies for resolving differences through the give-and-take process of negotiation. Negotiation Journal"s eclectic, multidisciplinary approach reinforces its reputation as an invaluable international resource for anyone interested in the practice and analysis of negotiation, mediation, and conflict resolution including: - educators - researchers - diplomats - lawyers - business leaders - labor negotiators - government officials - and mediators