一句话：提出“能力债务”概念框架，揭示过度依赖生成式AI会抑制学习并影响产出质量，并给出组织缓解指南。
生成式AI在软件相关工作中迅速普及，其能以较低技能生成看似优质的产物，因此对学习、技能构建与工作产出的影响亟需理解。本文提出“能力债务”概念框架，用以描述过度依赖GenAI如何抑制个体学习、削弱能力积累，并反过来影响产出质量。作者将框架应用于组织层面，梳理GenAI已显现的多种影响，并据此推导组织可采取的缓解指南，以在利用GenAI效率的同时保持人类在驾驶座。该工作为理解GenAI对能力发展的长期风险及组织干预提供了概念工具。
With generative AI (GenAI) being taken up in many sectors of the economy, especially in software-related areas, there is a growing urgency to understand how GenAI impacts our work. Because GenAI enables us to produce seemingly great outputs with little skill, the impact on learning and skill-building is especially interesting. In this paper, we present a conceptual framework to describe the concept of competence debt—illustrating how extensive use of GenAI can inhibit learning and impact the quality of our outputs. We apply the conceptual framework to describe various already visible impacts of GenAI on organizations and use it to infer guidelines on how organizations can mitigate the negative effects of GenAI usage.
@inproceedings{LudersKB26,
title = {Keeping Humans in the Driver’s Seat: A Conceptual Framework for GenAI and Competence Debt},
author = {Clara Marie Lüders and Olaf Krużycki and Carolin Brandt},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3806707}
}