Higher education plays a critical role in driving an innovative economy by
equipping students with knowledge and skills demanded by the workforce. While
researchers and practitioners have developed data systems to track detailed
occupational skills, such as those established by the U.S. Department of Labor
(DOL), much less effort has been made to document skill development in higher
education at a similar granularity. Here, we fill this gap by presenting a
longitudinal dataset of skills inferred from over three million course syllabi
taught at nearly three thousand U.S. higher education institutions. To
construct this dataset, we apply natural language processing to extract from
course descriptions detailed workplace activities (DWAs) used by the DOL to
describe occupations. We then aggregate these DWAs to create skill profiles for
institutions and academic majors. Our dataset offers a large-scale
representation of college-educated workers and their role in the economy. To
showcase the utility of this dataset, we use it to 1) compare the similarity of
skills taught and skills in the workforce according to the US Bureau of Labor
Statistics, 2) estimate gender differences in acquired skills based on
enrollment data, 3) depict temporal trends in the skills taught in social
science curricula, and 4) connect college majors' skill distinctiveness to
salary differences of graduates. Overall, this dataset can enable new research
on the source of skills in the context of workforce development and provide
actionable insights for shaping the future of higher education to meet evolving
labor demands especially in the face of new technologies.

高等教育在推动创新经济方面发挥着关键作用，通过为学生提供劳动力需求所需的知识和技能。本研究填补了一个空白，通过提供从近 3000 所美国高等教育机构的三百万门课程大纲中推断技能的纵向数据集，回答了如何在高等教育中记录技能发展的问题。这个数据集通过自然语言处理从课程描述中提取具体的工作场景活动（DOL 所用来描述职业的 DWAs），并聚合这些 DWAs 来为机构和学术专业创建技能概况。这个数据集可以用于研究技能来源、劳动力发展的背景，并为塑造未来高等教育以满足不断变化的劳动力需求提供可操作的见解。

美国高等教育课程中所教授技能的国家纵向数据集

A national longitudinal dataset of skills taught in U.S. higher  education curricula

This systematic literature review aims to investigate the impact of
artificial intelligence (AI) on the labour force in software engineering, with
a particular focus on the skills needed for future software engineers, the
impact of AI on the demand for software engineering skills, and the future of
work for software engineers. The review identified 42 relevant publications
through a comprehensive search strategy and analysed their findings. The
results indicate that future software engineers will need to be competent in
programming and have soft skills such as problem-solving and interpersonal
communication. AI will have a significant impact on the software engineering
workforce, with the potential to automate many jobs currently done by software
engineers. The role of a software engineer is changing and will continue to
change in the future, with AI-assisted software development posing challenges
for the software engineering profession. The review suggests that the software
engineering profession must adapt to the changing landscape to remain relevant
and effective in the future.

这篇系统文献综述研究了人工智能对软件工程劳动力的影响，重点关注未来软件工程师所需的技能、人工智能对软件工程技能需求的影响以及软件工程师未来的工作前景。综述发现，未来的软件工程师需要精通编程以及拥有问题解决和人际沟通等软技能。人工智能将对软件工程领域造成显著影响，自动化许多目前由软件工程师完成的工作。软件工程师的角色正在变化，并在未来继续变化，人工智能辅助软件开发为软件工程专业带来挑战。该综述认为，软件工程专业必须适应变化的发展，以保持相关性和有效性。

人工智能对劳动力的影响 —— 寻找未来软件工程师的分析能力

Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers

Artificial intelligence (AI), which enables machines to learn to perform a
task by training on diverse datasets, is one of the most revolutionary
developments in scientific history. Although AI and especially deep learning is
relatively new, it has already had transformative impact on medicine, biology,
transportation, entertainment, and beyond. As AI changes our daily lives at an
increasingly fast pace, we are challenged with preparing our society for an
AI-driven future. To this end, a critical step is to ensure an AI-ready
workforce through education. Advocates of beginning instruction of AI basics at
the K-12 level typically note benefits to the workforce, economy, and national
security. In this complementary perspective, we discuss why learning AI is
beneficial for motivating students and promoting creative thinking, and how to
develop a module-based approach that optimizes learning outcomes. We hope to
excite and engage more members of the education community to join the effort to
advance K-12 AI education in the USA and worldwide.

本论文讨论了 AI 对医学、生物学、交通、娱乐等领域产生深远影响的同时，为应对未来 AI 驱动的社会挑战，将 AI 教育纳入 K-12 教育的必要性并提出了基于模块化的教学方法以促进学生的动机和创造性思维。