AI/ML, Employability and Higher Education - Roundup 21 Jul 2025

The articles highlight the transformative impact of AI on the job market, with predictions ranging from widespread job losses to new opportunities in AI-adjacent roles. Universities face the challenge of adapting curricula to prepare students for this changing landscape, emphasizing both technical AI skills and critical thinking abilities that complement AI technologies.

There are stark contrasts in predictions about AI’s impact on employment. Some sources, like Anthropic’s CEO, forecast up to 50% of entry-level white-collar jobs being eliminated within five years (Lichtenberg, 2025), while others argue that AI will create new jobs and enhance productivity (Williams 2025). Additionally, while some studies show AI boosting productivity, others suggest it may actually slow down experienced workers in certain contexts (Rogelberg 2025).

• Lichtenberg (2025) discusses divergent views on AI’s impact on jobs, with predictions ranging from massive job losses to increased productivity. This article is relevant as it highlights the uncertainty surrounding AI’s effects on the job market, which universities must consider in curriculum planning.
• Criddle and Jack (2025) explores how AI is reshaping higher education, including the use of AI tools in classrooms and concerns about their impact on learning. This article is crucial for understanding how AI is directly affecting educational practices and student experiences.
• Williams (2025) provides an optimistic perspective on tech jobs despite AI disruptions, emphasizing opportunities in AI-adjacent roles. This article offers insights into potential career paths for students in a rapidly evolving tech landscape.
• Cohen Booth (2025) examines the interplay between falling birth rates and AI automation in shaping the future workforce. This article is relevant for understanding broader demographic and technological trends that will affect future job markets.
• Rogelberg (2025) reports on a study showing that AI tools unexpectedly slowed down experienced software developers. This article challenges assumptions about AI’s productivity benefits and has implications for how AI skills are taught and applied.
• Heikkilä (2025) discusses the shift in AI companies from low-cost data labelers to high-paid experts for creating sophisticated training data. This article highlights evolving skill requirements in the AI industry, which is relevant for curriculum development.
• Jin, Hagey, and Cohen (2025) details the intense competition for top AI talent among tech companies, illustrating the high demand for advanced AI skills. This article underscores the potential career opportunities for students who develop expertise in AI.
• Fortson (2025) explores how AI is changing the legal profession, with implications for job prospects and required skills for future lawyers. This article demonstrates how AI is transforming specific professions, which is crucial for preparing students in various fields.

The articles collectively paint a picture of rapid and significant change in the job market due to AI, with both challenges and opportunities for future graduates. While there’s disagreement on the extent of job displacement, there’s consensus on the need for education to evolve. Universities must strike a balance between teaching technical AI skills and fostering the human capabilities that complement AI, such as critical thinking, creativity, and ethical reasoning. The goal is to prepare students not just to use AI tools, but to navigate and shape a workforce where human-AI collaboration is increasingly the norm.

1. Changes in the broader labour market: Educators must prepare students for a job market where AI is both creating and eliminating roles. Lichtenberg (2025) and Williams (2025) highlight the potential for significant job losses in some sectors, but also the emergence of new AI-adjacent roles. This suggests a need to foster adaptability and continuous learning skills in students, alongside specific AI competencies.

2. Changes in jobs and tasks within jobs: AI is reshaping the nature of work across various professions. Fortson (2025) shows how AI is changing legal work, while Heikkilä (2025) illustrates the shift towards more sophisticated data labeling in AI development. Educators should focus on teaching students how to work alongside AI, emphasizing tasks that require human judgment, creativity, and complex problem-solving skills that complement AI capabilities.

3. Types of study needed for students to thrive: While AI literacy is crucial, Rogelberg (2025) suggests that overreliance on AI tools can sometimes hinder productivity. Therefore, educators should balance teaching AI skills with developing students’ critical thinking, domain expertise, and ability to evaluate AI outputs. Criddle and Jack (2025) highlights the importance of ethical considerations in AI use, indicating that curricula should include discussions on the societal impacts and ethical implications of AI technologies.

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