In 1904, the US Census reported 65,381 people gainfully employed as hostlers. (If you don’t know what a hostler is, read on.) By the 1920s Census, only 18,967 people were employed as hostlers. What happened? It’s safe to say that an emerging technology had something to do with it.
In case you didn’t already know, or didn’t just Google it, a hostler was someone employed to look after the horses of people staying at an inn or hotel. A new technology, the automobile, drastically reduced the need for hostlers. The demand for hostlers was replaced by an even greater demand for new occupations such as gas station attendants and mechanics. Hostlers in 1910 would have benefited by thinking of themselves as in the “transportation services” industry rather than the business of taking care of horses. Then they would have recognized the need to develop some new skills, like changing engine oil, to meet the new demands of a shifting industry.
Government talent acquisition and development is likely going through a similarly disruptive period, affected both by emerging technologies on the demand side and demographic shifts coupled with postpandemic shifts in worker attitudes on the supply side. According to the National Council of State Legislators, government hiring postpandemic has lagged far behind private employers. At the state and local level, a big part of this challenge has been difficulty in filling education jobs. Federal agencies have also had trouble filling cybersecurity jobs. In October 2023, the Pentagon reported that nearly one-quarter of Department of Defense cyber jobs were unfilled. The unemployment rate and new jobs reports don’t tell the full story. There's a mismatch between the skills that people have and the skills that are in demand. “Skills gaps in the federal workforce remain a significant barrier to reaching better end results in agencies’ work,” according to Drew Friedman in a Federal News Network article about the important role that chief learning officers play in government agencies.
For transportation services in the early 1900s, the disruptive technology was automobiles. For learning and development in the 2020s, it’s a digital transformation of the talent marketplace hastened by artificial intelligence (AI) and other technologies.
AI is both a threat to the status quo and a great opportunity for improving how talented people will deliver government services. AI could be used positively to personalize customer service, in public health and economic policy, and in strengthening national defense. How might AI also be used to help people as lifelong learners advance in their careers and increase their contributions as public servants?
There’s been a lot of buzz about how to use generative AI for L&D, such as for image generation or script writing. However, in the emerging augmented intelligence economy, AI is becoming more than just a tool. AI agents, in a sense, will become part of the team. These new team members will help us continually learn and improve as they continually learn how to better support our work. For many functions within government, how work gets done will change as AI agents “join the team.” This may include changing how people learn and broadening how we support learning and career advancement.
Just like it would have been valuable for the hostler in 1910 to see their role broadly as taking care of people’s mode of transportation, today it’s valuable to think about L&D broadly as “helping people learn,” whatever that takes, rather than something narrower such as “developing courses.” With this perspective, we can be ready to retool the L&D function toward what is needed in the AI economy, including:
- More just-in-time and on-the-job microlearning; fewer traditional courses
- Blended talent development and performance management; AI-enabled coaching; job aids that mix new learning with performance enhancement
- A more holistic perspective of development: supporting each employee’s well-being as a contributor to job satisfaction, continued employment, and overall performance
- Considering the broad range of factors that affect learning, and not just the instructional content
- Data-informed personalized feedback and adaptive instruction, including AI-enabled coaching
If you’re an instructional designer overwhelmed by the thought of having to single-handedly learn how to do all of this, don’t worry. You’ll have lots of help. The L&D challenges of this new age call for multidisciplinary teams. In what is called learning engineering, learning professionals work with experts across disciplines such as the learning sciences, human-centered design, learning environment design, software engineering, and data analytics to iteratively solve challenges that go beyond the expertise of any one of them. No one on the team is expected to do it all. The learning engineering process also employs strategies common across other engineering disciplines, such as modeling, modularization, feedback loops, and iterative development, to make complex challenges more manageable.
To learn more about learning engineering, download the free chapter “What Is Learning Engineering” from Learning Engineering Toolkit. For additional insights about learning in the intelligence augmentation economy, read this free-to-view article from Proceedings of the 2022 Human Factors and Ergonomics Society Annual Meeting, or this chapter in Design Recommendations for Intelligent Tutoring published by the US Army Combat Capabilities Development Command – Soldier Center.