Not long ago, artificial intelligence in education felt novel. It was something shiny, experimental, and, for many educators, possibly unsettling at times. When ChatGPT arrived in November 2022, the initial conversations and concerns were more focused on fear. I recall receiving emails, text messages, phone calls, and visits from educators who were concerned about cheating, plagiarism, lost skills, and what instantly felt like an overwhelming pace of change. It was something else to adjust to, not long after the overwhelming feeling that many felt in March of 2020.
But since that initial adjustment to the increased use of AI in our world at the end of 2022 and through 2023, I’ve seen a shift happening. At first, there was skepticism, uncertainty, and hesitation, and not just in the world of education. However, as we’ve continued to adjust to new tools and new ways of working, I’ve noticed a shift from considering AI as a “what if” to the acceptance that AI is here and its use is increasing. It’s embedded in tools educators already use, and if it hasn’t already, then it will potentially slowly but surely become part of the daily routine and workflow of teaching and learning.
I’ve spoken about this shift from novelty to normalcy and how it brings a new challenge: educator upskilling.
A few years ago, I started researching the training available to educators and other professionals in AI. At the end of 2023, 87% of the educators in the United States had not received any training. In my workshops, some attendees are having their first training experience, more than 3 years after ChatGPT made its debut. So I think that we need to focus on an important question, whether in education or not. The question is no longer whether educators need professional learning around AI. Most people agree that they do. The bigger issue is whether we are approaching AI professional development in ways that are deep, sustained, and human-centered, or whether we’re still experiencing the one-and-done sessions that barely scratch the surface. With AI and the pace of change in education and the world, we need to do better and be prepared.
Shifting to Ongoing Capacity Building
When I completed my doctorate nearly two years ago, my research focused heavily on professional learning in emerging technologies, with a strong emphasis on AI. Even then, the message was clear. A single PD session, or even a series of short, tool-based trainings, was not enough, especially if completed early in the year or during a limited time span.
Yet, that is what I am learning about how AI PD is structured today. Through surveys in my sessions and conversations with other educators, there is a common experience happening, which is:
- A 30-minute overview.
- A 15-minute “certified educator” badge.
- A walkthrough of one tool done well.
While these experiences can be helpful, especially for getting started and when time is limited, in the long term, they don’t build AI literacy. They build familiarity, whether with AI concepts or an AI tool. But familiarity is not AI literacy. Not for us as educators, nor for the students we are preparing for a future surrounded by AI and a world of work that seeks employees skilled in AI.
AI literacy requires more than a single lesson or session. It is the same for our students. To develop AI literacy, we need to understand:
- How AI systems work at a basic understanding
- When and why AI should (or should not) be used
- How to evaluate outputs critically and teach students to do the same
- How to address bias, privacy, and ethical concerns
- How to design learning experiences where AI supports thinking instead of replacing it
As this list suggests, AI is not just another edtech tool. AI tools impact accessibility, assessment, curriculum design, feedback, student agency, and even educator workload and workflow. AI also brings in other areas we need to focus on, such as data privacy, digital citizenship, and digital wellness. With all of these areas to consider, upskilling has to move beyond how to use a specific edtech tool and shift toward how to think, decide, and lead in our classroom environment that brings in so much AI.
Educators don’t need to become AI engineers. But they do need to develop confidence in making instructional decisions involving the use of AI. They need a common language and guidance on how to support students, families, and colleagues as they interact with AI. Schools need a system for evaluating AI tools and pedagogical practices, and there must be time available to do this. In my work, the number one answer to what holds educators back from exploring AI is a lack of time, followed by a lack of support. So, how can educators best prepare for AI, and what does professional development look like?
AI Professional Development
St. Vrain Valley School District in Colorado has done some truly innovative things over the years. In June of 2025, I saw a night drone show put on by high school students, which they also did for the FETC conference this year. They have tried new and exploratory ways to provide professional learning for their educators. In 2023, they started a different approach to AI training, which focused more on exploratory, collaborative, and sustained learning that mirrors how adults actually learn.
Leaders in the St. Vrain Valley School District designed an initiative called Exploration AI, focused on building capacity over time rather than delivering information during a one-and-done session. Their approach involves three key components: Self-Directed Gamified Learning, EdCamp-Style Pop-Ups and Collaborative Learning, and School-Based AI Champions.
Their approach can be applied to other schools. For example, educators participate in an AI-focused Bingo-style learning experience during the year. Teachers explore AI tools and scenarios in creative ways, ranging from personal to professional uses. Having choices like this promotes curiosity in learning rather than compliance, exploration, and active learning rather than simply listening. As we want for students, learning is best when choice is involved, and it is meaningful and relevant to the learner.
Schools like St. Vrain can offer edcamp-style sessions or other collaborative learning experiences for their educators. I’ve used the phrase “Leveraging your teacher talent” for years to talk about ways to engage educators in professional development. Edcamps give educators the opportunity to learn with and from each other. They can share challenges, successes, and ask questions about AI use in other classrooms. Collaboration helps with building a shared language and understanding. It helps build confidence and comfort, especially when dealing with uncertainty surrounding AI. It builds community, which is highly impactful for educators and for students. An approach to learning like this mirrors what we want our students to experience, which is inquiry-driven, social, and reflective learning.
St. Vrain identifies school-based champions, who are educators working with leadership teams to determine how to integrate AI learning into professional development sessions. Schools seeking to provide the ongoing training that educators and students need may find it challenging to allocate time during the school day. However, schools sometimes have early dismissals or late start days, or find ways to embed AI training into ongoing activities and conversations. The school connectors are similar to my thoughts about “teacher talent.” Finding the educators in your school who help the leadership team understand the needs of the educators, as well as help colleagues to make a shift from hesitation to being more confident in the use of AI.
A system of distributed leadership is highly beneficial for sustainability. I’ve spoken about this many times and shared this in my doctoral work when creating a plan for AI-related PD. AI upskilling and AI policy writing should not be dependent on one person or one department. It needs to be shared in order to truly understand the needs and make sure they are being met.
As AI becomes increasingly part of everyday teaching, educators’ needs are becoming clearer. Effective upskilling experiences should be ongoing, rather than episodic, because learning happens across months, not minutes. It should be tool-agnostic, at least initially, with a focus on AI concepts and decision-making, rather than features of a specific tool. It must be human-centered and grounded in strong pedagogy, ethics, and digital well-being. And it needs to be collaborative, to promote reflection and conversation.
AI is no longer knocking at the classroom door or the door to your house. It’s already inside, and it has been. Those who feel most prepared are not those who attended a single session or mastered a single AI platform. Those who are prepared were given time, trust, and opportunities to explore, question, and learn collaboratively. Meaningful AI upskilling requires an ongoing commitment to professional growth in a rapidly changing, highly technological world.
