近年來，隨著大型語言模型與生成式人工智慧快速發展，金融業 AI 應用已由營運效率、客戶服務與風險管理，延伸至組織內部知識工作與員工培育情境。壽險業務人員之工作高度仰賴客戶互動、需求判斷與即時應對，其能力養成除產品知識外，更需透過情境演練、主管輔導與實務經驗逐步累積。然而，傳統培育模式在主管時間、練習彈性、回饋一致性與學習歷程追蹤上仍有限制。因此，本研究以個案公司導入之內部 AI 教育工具 AI Coach 為研究對象，探討其對壽險業務人員學習及培育態度與行為之影響，並分析影響持續使用之因素。
本研究採質性個案研究法，透過深度訪談蒐集四位業務人員與兩位業務主管之使用經驗。研究結果發現，受訪者對 AI Coach 並非單純接受或拒絕，而是依職務角色、工作需求與使用經驗形成差異化評價。AI Coach 使部分業務人員的練習由正式訓練延伸至拜訪前後與工作空檔，促使學習行為由被安排轉向「可自行啟動」。同時，AI Coach 作為低人際風險的對練環境，可降低真人演練中的評價壓力，讓使用者較願意嘗試不同說法與反覆修正。其評分與回饋亦能協助使用者發現話術、流程與知識準備上的不足，進而產生記錄、補充與再練習行為；但若分數被理解為能力評價，亦可能造成壓力與框架感。整體而言，AI Coach 的持續使用受到情境貼近度、操作便利性、科技熟悉度、評分與資料紀錄壓力，以及主管推動與團隊共學等因素影響。
關於本研究的價值，主要為補充金融業 AI 應用中較少關注之員工培育面向，並說明 AI 教育工具如何介入工作中學習、情境演練與回饋修正歷程。實務上，企業應明確界定 AI 教育工具作為情境演練與回饋輔助工具之定位，強化分級化、在地化與客製化情境設計，清楚說明評分目的與資料使用範圍，並透過主管示範、團隊共學與既有培育流程銜接，提高工具之持續使用與實務適配性。

With the rapid development of large language models and generative artificial intelligence, AI applications in the financial industry have expanded from operational efficiency, customer service, and risk management to internal knowledge work and employee development. In the life insurance industry, sales agents’ work relies heavily on customer interaction, needs identification, and real-time communication. Their capability development requires not only product knowledge, but also scenario-based practice, managerial coaching, and field experience. This study examines AI Coach, an internal AI-driven learning tool implemented by the case company, and explores its influence on life insurance agents’ learning and training attitudes and behaviors, as well as the factors affecting continued use.
Adopting a qualitative case study approach, this study conducts in-depth interviews with four sales agents and two sales managers. The findings show that users’ attitudes toward AI Coach are not simply acceptance or rejection, but vary according to their roles, work needs, and usage experiences. AI Coach enables some agents to extend practice beyond formal training sessions to pre- and post-visit preparation and work breaks, shifting learning behavior from being externally arranged to self-initiated. As a low-interpersonal-risk practice environment, AI Coach reduces users’ concerns about being evaluated in human role-play settings and encourages experimentation and revision. Its scoring and feedback mechanisms also help users identify weaknesses in communication scripts, interaction flow, and knowledge preparation. However, when scores are interpreted as evaluations of ability, they may create pressure and constrain individual communication styles. Continued use is influenced by scenario relevance, ease of use, technological familiarity, scoring and data-recording concerns, managerial support, and team-based learning.
This study extends the discussion of AI applications in the financial industry to employee learning and development. It also illustrates how AI-driven learning tools can support learning in the flow of work, scenario-based practice, and feedback-based revision. Practically, organizations should clearly position AI-driven learning tools as aids for scenario-based practice and feedback, develop tiered, localized, and customized scenarios, clarify the learning-oriented purpose of scoring and data use, and promote sustained usage through managerial demonstration, team-based learning, and integration with existing training processes.

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