I recently wondered aloud to colleagues and participants at an event, “Aren’t you tired of hearing about AI yet?” While those of us who are working at the edges of emerging technology and digital learning may occasionally get this feeling, I’ve been reminded that not everyone has found their way into the conversation yet, and there is plenty of learning about artificial intelligence for all of us. As educators and those training educators, we must have a comprehensive approach to upskilling for AI.

This post is the first of a three-part series that you can use to guide your own professional development around upskilling in the age of AI or to use as you prepare fellow educators for these shifts in your context.  The three parts of this series include 1) Sampling the Soil, 2) Planting Seeds, and 3) Sustaining Growth. We’ll explore how this analogy can support a comprehensive strategy for AI professional development. 

Sampling the Soil

As AI continues to evolve, it’s essential to prepare educators to understand its implications and keep learning no matter how deep into the conversation we are. This involves asking critical questions and examining the current landscape. 

When I moved into our current home, we had a yard that was very hard to manage – there were shady spots, an incline, and more issues that kept the lawn from looking great. I attempted for years to fix this myself by purchasing specialized seeds and trying my own tricks. When I finally gave in to expert help, the first step was to sample the soil and send it off for testing. Much like testing soil before planting, we need to first grasp what AI is and how it impacts individual fields of study before we fully implement an assortment of tools and policies. 

What’s Happening With AI?

The first step in professional development around AI for faculty and staff is to understand what is behind AI’s emergence. The recent surge in attention is due to accessible and impactful applications like ChatGPT.

In Co-Intelligence, Ethan Mollick notes, “We have invented technologies that have boosted our physical capabilities; and others [. . .] that automate complex tasks; but we have never built a generally applicable technology that can boost our intelligence” (xix). This quote summarizes well the “what is happening” in AI — generative AI is the game-changing aspect that has brought AI onto the scene and out of its hibernation. It’s comparable to generally applicable technology such as  steam engines and printing presses, but this is arguably different in that it’s not just efficiency-driven. 

There are many creative ways that faculty developers are approaching this upskilling task (and I use that term intentionally often to remind ourselves that just as we know our students will re-skill and up-skill throughout their careers, we must as well– and we must take that approach as leaders at institutions when thing about professional development).

The OLC approaches this with an AI Demystified offering where we cover the history, starting with fundamentals, and then asking the hard questions around the ethical considerations of using AI. This is the first offering in OLC’s Teaching and Learning with AI microcredential. 

How does AI impact our fields of study?

After grasping an understanding of AI fundamentals and current implications, we must each consider the various disciplines. We can’t go into each discipline, but let’s dive a little deeper to get a lay of the land for the educational context.

National University. (2024). 131 AI statistics and trends for 2024. [National University Blog] retrieved from https://www.nu.edu/blog/ai-statistics-trends

This data reveals a few things:

• There is a disconnect between the interest of AI and in the workplace and in academic settings.
• There is a disconnect among learners, educators, and even parents.
• There is still a significant amount of professional development needed around AI in education.

The first step for us to take as educators is to consider where in our fields AI has the potential to make an impact? One way to do this is to distinguish tasks from jobs. While there are fears regarding AI and employment (and these are important conversations), let’s consider the here-and-now in that AI first changes tasks, not necessarily jobs. Consider which tasks your students preparing for a career could and should be using AI?

Another step in this process is to consider where students should not be relying on AI, or where there are serious considerations that should be made. Addressing issues such as data bias, authorship, and equitable access to AI tools is crucial in each field. Consider these as a professional yourself but have your learners wrestle with these critical questions as well.

Conclusion

By comprehensively sampling the soil, we can better prepare for integrating AI into their curricula and professional practices. We must first make sure that everyone is on the same level regarding what’s happening with the recent emergence of AI. Then, we can begin examining how this impacts our academic offerings and specific fields of study. From there, we weigh the important questions of when and how AI should be part of the conversation in our courses.

In part two, we’ll consider how we can plant seeds that last in well-prepared ground. This is where we begin exploring the tools and practices.