The Apprentice Without the Master: Evidence from AI-Mediated Newcomer Onboarding

时间：2026年9月18日 上午10:00-12:00

地点：承泽园333教室

Abstract:

Companies use newcomer onboarding programs to build newcomers’ task skills and shape their identification with the occupation. Historically, these two distinct outcomes tend to move together—well-designed onboarding programs tend to improve both. We ask whether this alignment is inherently linked, or whether it is contingent on the fact that human instructors have traditionally bundled the activities that produce both outcomes within the same interactions. The growing adoption of AI-mediated onboarding provides an opportunity to examine this question. We probe it using two complementary studies at a large teleservice company that introduced a generative-AI training tool into newcomer onboarding. Study 1 is a field experiment involving 503 recruits; Study 2 is a difference-in-differences analysis involving an additional 439 recruits from a second office. Across both studies, AI deployment substantially improves task skill acquisition while reducing occupational identification. These effects vary systematically with newcomers’ prior occupational experience: the skill benefit concentrates among experienced newcomers, whereas the identification cost concentrates among those entering the occupation for the first time. Mechanism analyses of survey evidence further suggest that AI improves task skills partly through more timely feedback and reduces occupational identification partly through intensified felt dehumanization. Our findings contribute to socialization theory by showing that two central outcomes of onboarding can diverge when onboarding interactions are reorganized. This study also extends research on technology-mediated socialization by demonstrating that AI can simultaneously enhance task skill learning and weaken occupational identification. As AI onboarding spreads, newcomers without prior occupational experience may face systematic disadvantages.

Bio：

Qi Li, Assistant Professor, Department of Management, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen.

Qi Li graduated from the management & organizations department of the S.C. Johnson College of Business at Cornell University. Her research focuses on strategic management, strategic human resource management, technological innovation (Artificial Intelligence), and corporate social responsibility for private enterprises in emerging economics. 

Prior to enrolling at Johnson, Qi was a research assistant to the director of the Ash Center for Democratic Governance and Innovation at Harvard Kennedy School. Qi holds an MPA from Harvard University’s Kennedy School of Government, and an MBA from the Olin Business School at Babson College, where she received a full merit scholarship. Before coming to Harvard, Qi was an entrepreneur in China who launched a successful education venture (later acquired). She has also interned at the U.S. Congress (House of Representatives), the Development Research Center of the State Council of China, and the China Development Bank. Qi has also served as the president of the Harvard Kennedy School Chinese Students and Scholars Association. Through her student leadership role at HKS, Qi has developed a deep understanding of Chinese political dynamics via her numerous collaborations and interactions with minister and deputy-minister level members of the Chinese Leadership.

Qi’s research has appeared in Organization Science and Academy of Management Journal, and the Stanford Social Innovation Review. In recent years, she has also developed several Harvard Business School teaching cases exploring humanoid robots, AI-enabled healthcare platform and corporate leadership, aiming to bridge practical applications to business scholarship. She was also recognized by Poets & Quants as one of the World’s Best Undergraduate Business School Professors in 2022. In 2025, she was invited to deliver an open lecture for the School of Management and Economics during the University Open Day. Most recently in 2026, her work on diverse employment arrangements in the gig economy was selected as one of the four Finalists for the INFORMS TIMES Best Working Paper Award.