Removing AI and Technology Adoption Obstacles in Education
How are secondary and post-secondary educators reacting to AI, both in how they teach it and how their students use it? It’s an ever-evolving picture.
In the Greater Nashville area, where I live, the first obstacle is finding technology teachers at all. In Davidson County, where I volunteer my time, only two of the 31 schools have technology in their systems. That’s six percent. In some cases it’s not a question of accepting AI as a tool they could use to teach – it’s about simply accepting technology as it exists today. ChatGPT and other AI tools are still overwhelming to many teachers. In a charter school my company adopted, we went through four technology teachers in one year.
This shortage is in part due to an industry-to-school bottleneck. Working in data science is far more lucrative than teaching it, and as someone who has taught, worked in industry, has at least a working familiarity with big data, and has industry clients in my own business, I’ve seen firsthand that the credibility of an industry background is hard to replicate based on a career spent solely in a school system or on a college campus.
The way we’ve historically taught also needs to be reconsidered in the face of AI.. The theory of most pedagogy remains:
- I’m going to speak to you.
- I’m going to tell you what you need to learn.
- You’re going to regurgitate to me what you learned.
In the age of AI, this formula no longer works. If you haven’t changed your teaching style to accommodate technology and AI in education, you probably should feel threatened. That’s because students aren’t just ready for technologies like AI. They’re eating it up. Students can look on their phones and find everything they need. In many cases, at the college level, students know more about technology than professors do. The dynamic is difficult when families are spending so much money on education, especially after two years of COVID self-serve learning. Many students’ feelings could be summed up in this way: “I could just do this at home. What do you have to teach me?”
Technology moves fast. So should teaching
Some things may move slowly in academics, but technology isn’t one of them. Textbooks published in 2018 are often out of date today. And it’s hard for any textbook to compete with a generative AI tool that answers anyone’s questions in near real-time.
The speed of technology being what it is, we don’t want to stifle it. In my opinion we need to update the way we teach to maximize the potential of AI in education. This means less worrying about whether someone used AI to draft their book report and more about adopting a different style of introducing information. It may mean cutting down on lecture time, especially if that involves clicking through a set of PowerPoint slides, and putting technology into the curriculum in ways students will use it to learn.
With my high school students, we take a more hands-on approach to everything data related. We’re learning SQL. We’re learning Python. They know HTML. But rather than going to YouTube University, we’re sitting in class and doing hands-on assignments like looking at data analytics in their social media and managing business accounts. In my Data Science for Social Justice class, we worked with Allen Hillery and took the lynching files from Ida B. Wells’ data. I called it a hackathon for social justice. The assignment was to take the flat files from Wells’ research and use Python to turn them into richer data. The students were initially unhappy with me about it, but the assignment made them think and work hard to figure it out. They also worked as a team, as we do in real life, and inquiries have been coming in from around the country as to how we did what we did.
Time to get on board
Successfully teaching students about technologies like AI means giving them the opportunity to be free to create. This has to happen in the classroom as well. It can’t just be the teacher giving you everything you need to learn. Of course, memorization is part of learning but it shouldn’t be just for memorization’s sake. Here are a few other recommendations.
Reframe familiar concepts. In an age where algorithms are determining more of what we experience in society, I believe it’s helpful to emphasize that a number is a person. By that I mean, a number is much more than something you plug into an equation. It could be ROI, it could be demographic data or financial information, but at some level there is still a person behind those numbers. That understanding changes your thought process and how you use data.
Start sooner. If data education doesn’t begin before college, students need to do the best they can to get what they need. I came from a STEM background with a major in Chemistry, then became an epidemiologist, which is when I started learning about data. Today, you have to be able to use data in some way or else you’re behind. I learned HTML because I had to. Not everyone will go the computer science route, however everybody needs to understand data.
Broaden the audience. Diversity continues to be important in data fields, but I’ve observed that we’ve gone backwards in the U.S. in technology skills, and that’s why I’m working so hard to create a pipeline for people of color to come through the system. If we’re falling behind as a nation in technology, we as data leaders bear a large responsibility to ensure that we encompass people of all types and welcome them in.
Break the silos. Do we really know how to manage the data in our environments? The answer is often no. In academic institutions we’re far too siloed into departments. Even a math chair doesn’t really converge into the aspects of technology that I feel would allow for more growth and progress. Top schools are taking AI and technology by the horns and using it as best they can, but as a whole we are behind in technology education and need to work in more cross-disciplinary ways.
AI systems and other data technologies increasingly run on streaming and real-time data, and we have to move at the same pace. It’s up to us as data leaders to grab hold of new, up-and-coming technologies so that we can engage our students and they feel like they’re being engaged.
I think of my own students as part of a pipeline. When they graduate, they can go into industry but they also should consider teaching. Industry’s always going to be there, but industry can’t function if we don’t have a pipeline to feed in technologists. If nobody is coming after them, where will our next leaders come from?