[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86056-en":3,"doc-seo-86056-105":29,"detail-sidebar-cat-0-en-105":87},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86056,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","FaciliTrain Practicing Facilitation Skills through AI-Simulated Group Dialogue","Skilled facilitation enables inclusive small-group dialogue, yet scaling deliberate practice is difficult because it requires expert coaches, live partners, and iterative feedback cycles. FaciliTrain is a voice-based training system where learners assume the facilitator role within an AI-simulated, multi-participant conversation, apply five evidence-based facilitation techniques, and receive structured AI feedback for reflection. A mixed-methods study with 24 participants combines formative and controlled pilot designs, reporting comparable live accuracy and contrasting comfort changes. Thematic analysis yields four design insights for scalable voice-based interpersonal training.","arXiv :2607 . 10850v1 [ cs .HC] 12 Jul 2026  \nFaciliTrain: Practicing Facilitation Skills through AI-Simulated Group Dialogue  \nHANG JIANG, Northeastern University & MIT, USA YUANXIN ZHU, Cornell University & MIT, USA DIYI YANG, Stanford University, USA  \nYOON KIM, MIT, USA DEB ROY, MIT, USA JAD KABBARA, MIT, USA  \nSkilled facilitation supports inclusive small-group dialogue, but deliberate practice is hard to scale: it depends on expert coaches, live practice partners, and iterative feedback. We present FaciliTrain, a voice-based training system in which learners step into the facilitator role of an AI-simulated multi-participant conversation, apply five evidence-based techniques, and receive structured AI feedback to support reflection. We report findings from a mixed-methods study with 24 participants, conducted as a formative study (N = 12) and a controlled pilot (N = 12; 6 treatment, 6 control) . Both conditions achieved comparable accuracy on a live evaluation task, though treatment participants’ self-rated comfort declined significantly while control participants’ comfort improved (p = .018) . Reflexive thematic analysis identifies four themes: the taxonomy externalizes implicit facilitation intuitions; Making Connections is the most cognitively demanding technique; voice acts as a deliberate-response forcing function; and participants overwhelmingly preferred AI feedback over self-practice. We discuss design implications for voice-based, AI-supported interpersonal skill training at scale.  \nCCS Concepts: • Human-centered computing → Voice / audio interfaces; Social learning; Interactive systems and tools.  \nAdditional Key Words and Phrases: facilitation training, voice interfaces, conversational AI, social skills, multi-participant simulation, intergroup dialogue  \nACM Reference Format:  \nHang Jiang, Yuanxin Zhu, Diyi Yang, Yoon Kim, Deb Roy, and Jad Kabbara. 2026. FaciliTrain: Practicing Facilitation Skills through AI-Simulated Group Dialogue. In Proceedings of Proceedings of the ACM on HumanComputer Interaction (CSCW’26). ACM, New York, NY, USA, 7 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 Introduction  \nEffective facilitation helps small-group conversations remain inclusive, constructive, and grounded in participants’ lived experience. In polarized or cross-difference contexts, skilled facilitation can determine whether dialogue produces understanding or stalemate [10] . Yet facilitator training is resource-intensive: it depends on expert coaches, live practice partners, and iterative feedback cycles that are hard to scale.  \nAuthors’ Contact Information: Hang Jiang, Northeastern University & MIT, Cambridge, MA, USA, hang.jiang@northeastern. edu; Yuanxin Zhu, Cornell University & MIT, New York, NY, USA, yz3477@cornell.edu; Diyi Yang, Stanford University,  \nStanford, CA, USA, [diyiy@stanford.edu](diyiy@stanford.edu); Yoon Kim, MIT, Cambridge, MA, USA, [yoonkim@mit.edu](yoonkim@mit.edu); Deb Roy, MIT, Cambridge,  \nMA, USA, [dkroy@mit.edu](dkroy@mit.edu); Jad Kabbara, MIT, Cambridge, MA, USA, [jkabbara@mit.edu](jkabbara@mit.edu).  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires [prior specific permission and/or a fee. Request permissions from permissions@acm.org](prior specific permission and/or a fee. Request permissions from permissions@acm.org).  \nCSCW’26, Salt Lake City, UT, USA  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM.  \nACM ISBN 978-1-4503-XXXX-X/2026 [https://doi.org/XXXXXXX.XXXXXXX](https:/","cbCaimtPcrSuUmVb","https://ap.wps.com/l/cbCaimtPcrSuUmVb","pdf",3748589,1,7,"English","en",105,"# Introduction\n## Motivation and Background\n## FaciliTrain System Overview\n## Contributions and Study RQ","[{\"question\":\"What were the key outcomes from the mixed-methods study with 24 participants?\",\"answer\":\"Both conditions achieved comparable accuracy on a live evaluation task. Treatment participants’ self-rated comfort declined significantly while control participants’ comfort improved (p = .018).\"},{\"question\":\"What themes were identified from the reflexive thematic analysis?\",\"answer\":\"The analysis reports four themes: a taxonomy that externalizes implicit facilitation intuitions; Making Connections as the most cognitively demanding technique; voice as a forcing function for deliberate responses; and participants strongly preferring AI feedback over self-practice.\"}]",1784208120,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":82,"head_meta":84,"extra_data":86,"updated_unix":27},"facilitrain-practicing-facilitation-skills-through-ai-simulated-group-dialogue","",{"@graph":35,"@context":81},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/facilitrain-practicing-facilitation-skills-through-ai-simulated-group-dialogue/86056/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77],{"name":72,"@type":73,"acceptedAnswer":74},"What were the key outcomes from the mixed-methods study with 24 participants?","Question",{"text":75,"@type":76},"Both conditions achieved comparable accuracy on a live evaluation task. 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