How Managers Perceive AI-Assisted Conversational Training for Workplace Communication

2025-05-20

AI Summary

This study investigates managers’ anticipated needs and usage contexts for AI-augmented workplace communication training. Employing a functional probe methodology, we designed and evaluated CommCoach—a conversational, AI-driven role-playing system integrating situation modeling and explainable feedback—through semi-structured interviews with managers. Our qualitative analysis reveals four core requirements: feedback transparency, human-AI collaboration, situation awareness, and controllability of virtual interlocutors. We propose a “personalized–structured–adaptive” triadic design principle to balance these competing demands. Furthermore, we identify three critical tensions inherent in such systems: adaptivity versus feedback consistency, realism versus bias risk, and open-ended dialogue versus workplace discourse structuring. Grounded in empirical evidence, our findings provide a theoretically informed, practice-oriented design framework and actionable pathways for developing effective AI-enhanced communication training systems.

Technology Category

• Multiagent Systems: Agent Communication
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Managers lack tailored communication training solutions
• AI's role in improving communication skills is unclear
• Balancing personalization and consistency in AI feedback

Innovation

• AI-assisted conversational role-play system
• Adaptive low-risk workplace simulations
• Human-AI teaming with transparent feedback