How Managers Perceive AI-Assisted Conversational Training for Workplace Communication

TL;DR

This work investigates how managers perceive AI-assisted training for workplace communication and uses a functional probe, CommCoach, to elicit design requirements. By combining a formative study with a three-phase user study of 17 managers, the authors identify core design tensions and opportunities around customization, in-situ feedback, and human-AI collaboration. The contributions include design implications for AI-mediated managerial training and the CommCoach prototype itself as a platform to explore reflective learning in leadership development. The findings highlight that scalable AI tooling must balance personalization, realism, and bias considerations while complementing human mentorship to be effective in organizational contexts.

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.

Figures (6)

• Figure 1: User-experience flow for CommCoach, the functional probe used during user study interviews (Note: text has been truncated in some cases to accommodate space, indicated with elipses). (1) Users can either start with the default scenario or design custom scenarios through an interactive process with the coach agent. Once a user is satisfied with a scenario, they can accept it and (2) start a new conversation with the conversational partner agent. (3) If the coach agent deems it necessary, it will intervene and provide immediate context-aware feedback. (4) Users then have the ability to explore a specific feedback item further through a dialogue with the coach, or can retry messages to simulate different conversational outcomes.
• Figure 2: Framework for AI-assisted managerial communication training. User Input introduces diverse communication styles, approaches, and barriers that the system must interpret and adapt to in order to provide effective training. The three core components within the dotted box, the Contextual Interpreter, Partner Simulation, and Feedback Module, work together to adapt AI-driven role-play and feedback to user communication styles, ensuring relevant and effective training. These components interact dynamically to refine AI responses, balance realism in conversational partners, and provide tailored feedback, ultimately shaping the System Output in the form of structured reflection and controlled conversation flow. To maintain alignment with real-world managerial training needs, the system is guardrailed by Organizational Objectives & Outcomes and Human-AI Teaming. Organizational Objectives & Outcomes ensure AI responses remain consistent with workplace objectives and structured leadership frameworks, while Human-AI Teaming positions AI as a supplemental tool that supports, rather than replaces, human mentorship and collaborative learning.
• Figure 3: UI screenshot of the functional probe, CommCoach
• Figure 4: Users can create custom scenarios through the scenario editor.
• Figure 5: Immediate feedback is provided to users via the side panel. Hovering over specific feedback items will show more information in a popup.