Many urban areas are facing demographic shifts, such as aging populations and declining birth rates, with potentially critical consequences for urban environments and their communities. Equipping urban economies with necessary skills for sustainable growth is crucial in helping regions and cities avoid falling into a talent development trap, which represents a decline in the working-age population, a mismatch between education and the labor market (both in terms of quantity and quality), and difficulties in retaining talent. If left unaddressed, this talent development trap can further deepen structural labor market inefficiencies and innovation shortfalls.
Many cities and regions, as hubs of economic and social activity, are at the forefront of addressing these challenges. They are tasked with attracting and retaining talent, for example, in sectors facing critical shortages, such as health care, the technical industry and education. The need for skills transcends traditional education and degrees, emphasizing the importance of diverse and inclusive labor markets.
In the midst of this paradigm shift, Artificial Intelligence (AI) emerges as a major force reshaping the landscape of the labor market and skill demands. AI, with its profound implications for productivity, innovation, and job creation, introduces both opportunities and challenges for urban economies.
On the one hand, it acts as a catalyst for the creation of new job categories and the enhancement of workplace efficiency, particularly in sectors related to the green transition. On the other hand, the rapid integration of AI technologies necessitates a reevaluation of the skill sets required in the workforce. This underscores the urgency for cities to not only foster AI literacy and specialized technical skills but also to emphasize the development of soft skills, such as creativity, problem-solving, and emotional intelligence, which are less susceptible to automation.
Moreover, AI can play a crucial role in addressing the talent development trap by providing advanced tools for skills forecasting, personalized learning, and job matching, thereby facilitating a more adaptive and responsive approach to workforce development which ensures that nobody gets left behind.
Leveraging a skills-based approach as well as AI for inclusive labor markets requires a detailed and thoughtful approach. This complexity presents an opportunity to devise a research agenda that not only addresses the challenges but also harnesses the potential synergies between AI technologies and a skills-based approach.
The following research theme is proposed as a framework for academic inquiry, challenging researchers to explore and devise innovative solutions that promote a AI and skills-based labor market. This agenda aims to ultimately catalyze innovation, foster inclusivity, and build resilience in the labor market.
1. In-depth analysis of (new) occupational skills:
research should focus on conducting in-depth analyses of occupational skills, identifying the evolving needs within various sectors, particularly those undergoing rapid technological change. This theme challenges researchers to explore how demographic shifts impact the demand for specific skills and how these changes influence labor market efficiencies. This might include:
- Graduate students entering the labor market: A detailed investigation into how skill taxonomies can be applied to improve the employability of graduate students. This could involve:
- Identifying key skills: Collaborating with employers and academic institutions to determine the most valuable skills for new graduates.
- Mapping educational outcomes to industry needs: Evaluating how current educational programs align with the skills required by employers and identifying gaps.
2. Certification and qualification for emerging sectors:
examine the role of professional certifications tailored to the green economy and other emerging sectors. Research could focus on the gap between traditional educational pathways and the requirements of the modern labor market, proposing new frameworks for credentialing.
3. Measuring and assessing skills:
explore new data collection and assessment strategies that effectively capture possessed skills and proficiency levels. Research could focus on learning outcomes in the context of informal and non-formal learning.
This might include:
Developing and testing new tools and methodologies for assessing skills, including:
- Digital portfolios and micro-credentials: Implementing and evaluating the effectiveness of digital portfolios and micro-credentialing systems in documenting and showcasing skills.
- Real-world simulations and projects: Integrating real-world projects and simulations into assessments to provide more practical and applicable skill evaluations.
- Curriculum development: Working with educational institutions to integrate skill assessment frameworks into their curricula, ensuring that students are continuously developing and demonstrating relevant skills.
This research paper is created for ánd with the new research community on AI & Skills. You can join the collaboration group: AI & Skills Research Community in order to be part of this growing community of researchers interested in the topic. And you can join the next upcoming event: Online Colloquium on Skills & AI Feel free to join either of these, even when you're still in doubt on whether this topic fully fits your interest, we're exploring the topic together!