AI 中文总结
研究旨在解决促进技能刻意练习难规模化问题，提出基于语音的FaciliTrain系统，学习者在模拟对话中应用技术获AI反馈。混合方法研究发现两组评估准确率相当，实验组舒适度降，对照组升，还确定了四个主题并讨论了设计含义。
AI 中文摘要
熟练的促进技能有助于开展包容性的小组对话，但刻意练习难以规模化：它依赖专家教练、现场练习伙伴和迭代反馈。我们提出了FaciliTrain，这是一个基于语音的训练系统，学习者在其中扮演人工智能模拟的多参与者对话的促进者角色，应用五种基于证据的技术，并获得结构化的人工智能反馈以支持反思。我们报告了一项对24名参与者的混合方法研究结果，该研究分为形成性研究（N = 12）和对照试点（N = 12；6个实验组，6个对照组）。在现场评估任务中，两组的准确率相当，但实验组参与者的自我评定舒适度显著下降，而对照组参与者的舒适度有所提高（p =.018）。反思性主题分析确定了四个主题：分类法将隐性促进直觉外化；建立联系是认知要求最高的技术；语音起到了刻意回应强制功能；与自我练习相比，参与者压倒性地更喜欢人工智能反馈。我们讨论了大规模基于语音、人工智能支持的人际技能培训的设计含义。
英文摘要
Skilled 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.
CommentsAccepted to CSCW Poster 2026
