MITHOS: Interactive Mixed Reality Training to Support Professional Socio-Emotional Interactions at Schools
DFKI GmbH（德国人工智能研究中心）；Saarland University（萨尔大学）；FoBiD；TriCat GmbH（TriCat公司）；University of Augsburg（奥格斯堡大学）
Lara Chehayeb, Chirag Bhuvaneshwara, Manuel Anglet, Bernhard Hilpert, Ann-Kristin Meyer, Dimitra Tsovaltzi, Patrick Gebhard, Antje Biermann, Sinah Auchtor, Nils… 展开作者
Lara Chehayeb, Chirag Bhuvaneshwara, Manuel Anglet, Bernhard Hilpert, Ann-Kristin Meyer, Dimitra Tsovaltzi, Patrick Gebhard, Antje Biermann, Sinah Auchtor, Nils Lauinger, Julia Knopf, Andreas Kaiser, Fabian Kersting, Gregor Mehlmann, Florian Lingenfelser, Elisabeth André
Teachers in challenging conflict situations often experience shame and self-blame, which relate to the feeling of incompetence but may externalise as anger. Sensing mixed signals fails the contingency rule for developing affect regulation and may result in confusion for students about their own emotions and hinder their emotion regulation. Therefore, being able to constructively regulate emotions not only benefits individual experience of emotions but also fosters effective interpersonal emotion regulation and influences how a situation is managed. MITHOS is a system aimed at training teachers' conflict resolution skills through realistic situative learning opportunities during classroom conflicts. In four stages, MITHOS supports teachers' socio-emotional self-awareness, perspective-taking and positive regard. It provides: a) a safe virtual environment to train free social interaction and receive natural social feedback from reciprocal student-agent reactions, b) spatial situational perspective taking through an avatar, c) individual virtual reflection guidance on emotional experiences through co-regulation processes, and d) expert feedback on professional behavioural strategies. This chapter presents the four stages and their implementation in a semi-automatic Wizard-of-Oz (WoZ) System. The WoZ system affords collecting data that are used for developing the fully automated hybrid (machine learning and model-based) system, and to validate the underlying psychological and conflict resolution models. We present results validating the approach in terms of scenario realism, as well as a systematic testing of the effects of external avatar similarity on antecedents of self-awareness with behavior similarity. The chapter contributes to a common methodology of conducting interdisciplinary research for human-centered and generalisable XR and presents a system designed to support it.
Exploring Teachers' Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education
Michigan State University（密歇根州立大学）；University of Pittsburgh（匹兹堡大学）
A qualitative analysis of remote patient monitoring: how a paradox mindset can support balancing emotional tensions in the design of healthcare technologies
Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
Fraunhofer IPK（弗劳恩霍夫生产系统与设计技术研究所）；TU Berlin（柏林工业大学）；Université Sorbonne Paris Nord（索邦巴黎北大学）；University of Augsburg（奥格斯堡大学）；EM Lyon Business School（里昂商学院）；EPITA（法国巴黎计算机及自动化学院）；Università di Pisa（比萨大学）；University of Bamberg（班贝格大学）；Universidad Central de Venezuela（委内瑞拉中央大学）；RWTH Aachen University（亚琛工业大学）；Harz University of Applied Sciences（哈茨应用技术大学）；University of Passau（帕绍大学）；SCAI, Sorbonne Université（索邦大学人工智能中心）；University of Hamburg（汉堡大学）；Osnabrueck University（奥斯纳布吕克大学）；ICM, LIP6 UMR 7606 CNRS（法国国家科学研究中心皮埃尔和玛丽居里大学计算机科学实验室）；Sorbonne Université（索邦大学）
