The research field of end-user programming has largely been concerned with helping non-experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands to obviate this entirely by allowing users to generate code from naturalistic language prompts. In this essay, we explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI. We posit the "generative shift hypothesis": that generative AI will create qualitative and quantitative expansions in the traditional scope of end-user programming. We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers. We speculate whether each of these reasons might be fundamental and enduring, or whether they may disappear with further improvements and innovations in generative AI. Finally, we articulate a set of implications for end-user programming research, including the possibility of needing to revisit many well-established core concepts, such as Ko's learning barriers and Blackwell's attention investment model.

翻译：最终用户编程的研究领域主要关注帮助非专家用户掌握足够编程技能以完成其任务。生成式AI有望通过允许用户从自然语言提示中生成代码来彻底消除这一需求。本文探讨了在生成式AI普及的世界中，“传统”编程语言对非专家最终用户编程者是否仍具相关性。我们提出“生成式转移假说”：生成式AI将在定性层面和定量层面扩展最终用户编程的传统范畴。我们概述了传统编程语言可能仍对最终用户编程者具有相关性和实用性的若干原因，并推测这些原因是具有根本性和持久性的，还是会随着生成式AI的进一步改进和创新而消失。最后，我们阐述了这些发现对最终用户编程研究的一系列启示，包括可能需要重新审视许多已确立的核心概念，例如Ko的学习障碍模型和Blackwell的注意力投资模型。