How Managers Perceive AI-Assisted Conversational Training for Workplace Communication

Abstract

Effective workplace communication is essential for managerial success, yet many managers lack access to tailored and sustained training. Although AI-assisted communication systems may offer scalable training solutions, little is known about how managers envision the role of AI in helping them improve their communication skills. To investigate this, we designed a conversational role-play system, CommCoach, as a functional probe to understand how managers anticipate using AI to practice their communication skills. Through semi-structured interviews, participants emphasized the value of adaptive, low-risk simulations for practicing difficult workplace conversations. They also highlighted opportunities, including human-AI teaming, transparent and context-aware feedback, and greater control over AI-generated personas. AI-assisted communication training should balance personalization, structured learning objectives, and adaptability to different user styles and contexts. However, achieving this requires carefully navigating tensions between adaptive and consistent AI feedback, realism and potential bias, and the open-ended nature of AI conversations versus structured workplace discourse.

accepted to CUI '25

References in corpus (11)

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• A Network-based End-to-End Trainable Task-oriented Dialogue System
• Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growth
• Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions
• Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
• Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
• Social Simulacra: Creating Populated Prototypes for Social Computing Systems
• Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language
• Emotional Intelligence of Large Language Models
• Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review
• Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution