For example, chatbots—similar to customer service systems—can guide users through sequences of choices and provide feedback. While useful, they can also narrow learning because they follow predefined algorithms. Not everyone learns the same way, and variability in learning styles is a strength; it allows humans to adapt creatively to changing circumstances and solve complex problems. Traditional approaches often focus on memorization or regurgitation of knowledge, which is necessary as a foundation, but innovation often requires doing and learning in new ways—something algorithms may not support.
In short, technology can enhance executive development, but the human-to-human element remains critical. People bring reflection, connection, and motivation that extend beyond what algorithms can provide. The study below illustrates one approach to blending technology with human coaching, and it’s worth a read.
Executive coaching is commonly used for leadership development and focuses on helping leaders reflect on their behaviors, decisions, and professional goals through guided conversations.
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The study examines how a large language model (LLM) chatbot can support leadership development by encouraging executives to engage in self-reflection between coaching sessions.
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Researchers developed a “coaching copilot” that combines chatbot interaction with traditional human coaching to provide continuous reflection and goal tracking.
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Findings indicate that AI tools can support leadership growth by prompting reflection and extending coaching conversations beyond scheduled meetings.
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The study concludes that AI works best as a supplement to human coaching rather than a replacement, as human coaches provide deeper insight, empathy, and contextual understanding.
Reference
Arakawa, R., & Yakura, H. (2024). Coaching copilot: Blended form of an LLM-powered chatbot and a human coach to effectively support self-reflection for leadership growth. In Proceedings of the 6th ACM Conference on Conversational User Interfaces (CUI ’24). Association for Computing Machinery. https://doi.org/10.1145/3640794.3665549
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