REVIEW 2 cited by
Social Skill Training with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life. However, practice environments for social skills are typically out of reach for most people. How can we make social skill training more available, accessible, and inviting? Drawing upon interdisciplinary research from communication and psychology, this perspective paper identifies social skill barriers to enter specialized fields. Then we present a solution that leverages large language models for social skill training via a generic framework. Our AI Partner, AI Mentor framework merges experiential learning with realistic practice and tailored feedback. This work ultimately calls for cross-disciplinary innovation to address the broader implications for workforce development and social equality.
Forward citations
Cited by 2 Pith papers
-
Practicing with Language Models Cultivates Human Empathic Communication
Personalized LLM feedback after practice conversations with AI partners significantly improves human empathic communication on six preregistered dimensions without homogenizing responses, while trait empathy fails to ...
-
FaciliTrain: Practicing Facilitation Skills through AI-Simulated Group Dialogue
A voice-based multi-participant AI simulation trains five facilitation techniques; a small controlled pilot shows comparable accuracy across feedback conditions, a comfort trade-off, and strong preference for AI feedback.
Discussion (0). Continue with ORCID to comment.