Zach Heller is part of the Product Leadership team at Penn Foster Group. He began his career in marketing at Lawline.com, an online provider of continuing legal education. From there, Zach joined Distance Education Company, a for-profit online school for creative professionals looking to pursue a passion or start a new career, where he worked for nine years. He has been with Penn Foster Group for the past seven years, starting as a product director before leading the vertical product management function and recently taking the lead role on a new business unit in Cohort-Based Learning.
In our conversation, Zach talks about identifying careers that will remain resilient as AI reshapes work and why Penn Foster focuses on preparing learners for tomorrow’s version of a job. He also discusses using AI to individualize education at scale and how Penn Foster is designing AI tutors that encourage productive struggle and simulation-based learning. Zach also shares why adaptability and lifelong learning are becoming increasingly important.
Viewing careers in terms of AI-resiliency
As AI changes the nature of work, how do you determine which careers are worth building education and training products around?
Some people are far smarter than I am who spend all day thinking about how AI is going to reshape work, so I don’t pretend to know where all this is headed. My opinion on AI is fairly nuanced, whereas others tend to think in extremes, from “AI will replace everyone” to “AI changes nothing.”
Our job at Penn Foster is not to predict the future perfectly, but to build education that can adapt as that future becomes clearer. People often start with the question, “Which jobs will AI replace?” We actually start with, “How is this specific career changing? And then what will employers expect someone to be able to do in three or five years from now?”
We spend a lot of time talking directly with employers, looking at labor market trends, trying to understand hiring needs and how those are changing, and identifying the careers that continue to offer real opportunity and show signs of strong demand in the labor market. Though we’re still preparing people for jobs, which is core to who we are as a business, we’re preparing them more for tomorrow’s version of that job.
What do the roles you see as most resilient to AI have in common?
I like the framing around AI resilience — it’s a more useful way to think about careers. I don’t know that anything is truly AI proof anymore. That said, the careers that seem more resilient combine technical expertise with a lot of human judgment and interaction. They involve working with people, whether they’re coworkers, customers, or patients; making decisions in uncertain situations where there is no clear right answer; communicating effectively; and interacting with the physical world in some shape or fashion.
One example that is near and dear to our hearts at Penn Foster is a veterinary technician. For pet owners, this is the person who assists the veterinarian, performs many of the tests, and handles animals at the clinic. For them, I think AI will help interpret information and streamline documentation. But when somebody has to calm a nervous pet owner, notice subtle changes in an animal’s behavior, or work with the rest of the clinical team, that is still going to take a human being who’s trained in that profession.
AI is becoming incredibly good at generating answers. Humans are still going to need to decide which answers matter in the moment. For that reason, I believe many of these professions will continue to show demand and growth.
Keeping education aligned with a changing workforce
You build a product that helps with training in entry-level healthcare professions, such as medical assistants, dental assistants, pharmacy technicians, and more. As job requirements change, how do you make sure a training program evolves quickly enough to keep pace?
Anyone who’s worked in education knows that, historically, academic timelines don’t necessarily match the real world. I’ve seen areas where it can take years for a program to move from the conceptual phase to full deployment, and then years again for changes to be made once the curriculum is live with a set of learners. The world of work has always changed much more rapidly than that, and now those timelines are accelerating.
We’ve known at Penn Foster that we needed to change our operating model to keep up. One of the things that I’m most excited about is the work we’re doing on cohort-based learning experiences. This is different from our historical model, which is more self-paced. Instead of treating a program as something you update every few years, we’re creating environments where we learn alongside the students we’re serving.
If we see friction in one week of a cohort, or learners consistently struggling with one particular concept, we can make improvements while that cohort is still progressing, rather than months or years later. That’s a completely different operating model than the one we’ve deployed in the past. From one cohort to the next, we can make a curriculum change where we see skills starting to shift in the profession.
As product leaders, it’s exciting because it starts to look more like continuous product development than traditional curriculum development. We’re learning, iterating, and improving results in something a lot closer to real-time than has ever been possible before.
As AI changes what employers expect people to know, how do you identify foundational skills that need to be added before the market explicitly demands them?
There’s a real balance here, because “before the market demands them” is a risk. We don’t want to wait until every employer explicitly asks for something, but we also don’t want to get too far out in front of the market; otherwise we’ll end up teaching things that employers don’t yet actually value.
We source expertise from anywhere we can get it: employers that are putting people through our programs; industry and job-specific advisory boards where we invite experts in from the industry; labor market data; certifying bodies in these various fields; and our own learners and graduates. With all of that put together, we can paint a picture of how industries and careers are evolving.
A good example of not getting ahead of the market is our medical billing and coding program. Since 2023, we’ve been hearing from pundits that this job was going to be more or less wiped out by AI. While it’s true that AI is changing the workflow for people in these positions, it’s not reducing the need for people this many years later.
Medical coders increasingly need to validate and work alongside those kinds of intelligent systems, rather than simply producing every code manually as they used to. Still, the demand for the core knowledge hasn’t disappeared. If anything, we’ve actually seen it grow over that time. For us, that means the difference between adapting an existing program versus looking for the next best thing and maybe retiring an old program.
Across industries and roles, we are looking at how we can teach AI literacy, not just the tools themselves, because the tools are going to change. The same AI platforms that you and I are using today are going to be different in five years. But understanding how to evaluate the output AI is giving you, recognizing when it’s wrong, and using it responsibly — that’s one of the new, durable skills we need to incorporate in all of our programs.
Individualizing learning at scale
When you design an AI tutor, how do you build an experience that knows when to answer, when to ask a question, and when to guide the learner toward an answer themselves?
This gets at one of the stickiest problems in AI and education so far. If you ask someone what makes a great tutor, they’ll usually say someone who knows the subject matter better than anybody else. Frankly, that’s wrong. Great practitioners do not always make great teachers because teaching is a skill in its own right.
A great tutor is somebody who knows you, the student, better than anyone else, because they can understand how you learn. They can understand what motivates you, recognize where you get stuck, and understand when you’re frustrated or ready for a new challenge. We’re not completely there yet as an industry, but we’re getting remarkably close to being able to do what a great tutor does with AI. The tooling allows us to design around the individual in a way that’s never really been possible before.
The reason it’s taking more time than people may have expected with a tutor, compared to something like a general customer service bot, is important to call out because the goal here isn’t simply answering questions faster. A really good tutor knows how to probe your thinking in different ways, when to ask follow-up questions, where to provide hints, and when to let you struggle on your own to answer the question, because learning is hard. There has to be some struggle involved for real learning to occur.
I’ve seen a lot of examples so far of chatbots that just give students the answer. That might help someone complete an assignment or pass a test, but it’s a real disservice to actual learning. I’ve definitely seen pieces of this AI tutor done well. I have yet to see anyone put it all together, but I do think we are very close.
How is AI changing the learning experience itself?
Historically, Penn Foster achieved scale through standardization. We are serving hundreds of thousands of learners a year, and the only way to effectively do that was to make operating those programs efficient. Think about things like standard courses that we can use across multiple programs, or a single support model that every learner engages with.
In the last few years, we’ve been able to turn that concept on its head. Technology now allows us to deliver individualized learning experiences at scale. We’re adapting program pacing so some people can move faster and some can move slower. We can give examples to different kinds of learners depending on their experience, offer more practice or remediation where it’s most needed at an individual level, and recognize when someone is ready to move on or move ahead. For the first time, personalization or individualization and scale don’t have to be competing priorities for us.
For product leaders, anytime you’re in an industry where that kind of paradigm shift is happening, speed matters, but I would say what matters even more than speed is your willingness to question old assumptions. I’ve seen a lot of companies get stuck and get in their own way because they don’t realize how quickly this is changing. They assume what’s worked for them in the past will always work in the future, and it’s not always true. If you can’t spot those trends and shift accordingly, then somebody else is going to do it before you do.
Practicing the human side of work
When you’re building interactive experiences for learners, how do you decide which parts of a job need to be practiced rather than simply explained?
Practice is so important, no matter what we’re talking about. This is classic learning science — reading about something and becoming fluent in it are two completely different things. We know that people learn through a series of practice, feedback, reflection, and repetition. That’s the model of a good learning experience. That’s especially true for career education, where you’re trying to master new skills, not just remember new facts.
If your future job involves interacting with patients or customers, or working with equipment, machinery, or software — which, let’s face it, describes most jobs — you have to practice those situations before you encounter them on the job. It’s really a prerequisite to success.
Technology is making this much easier to do at scale. At Penn Foster, we began investing in real simulations and interactive experiences a few years ago, and we can build them even faster today with new tools that our product team is building. We want to present learners with a real situation, the kind that they might encounter on the job, and then present them with decisions they need to make and give them feedback in real-time as they make those decisions.
Then we want them trying again and again, because that’s what’s going to build confidence before they’re ever in the real workplace. We can couple online learning experiences with simulations or scenario-based learning.
We still take on-the-job learning experiences very seriously as well. Nothing really beats getting into a clinical setting, having some supervision, and actually getting a chance to use your hands and do the job.
Can you share an interactive experience your product team has built?
One of the best examples I can give is in our HVAC technician program. This is somebody who’s learning how to respond to a service call and fix an air conditioner that’s gone down. We can give them an interactive simulation where they work with the equipment, choose the right tools for the job, and identify different parts of the unit and how to take them apart and inspect them. They do this with a 3D model they engage with on the screen.
Each time they’re asked to make a selection or perform an action, they’re getting feedback. If they got it wrong, we prompt them to try again and give them a little bit more information. If they got it right, we reinforce the knowledge involved and help them understand where that would come into play in an actual service call environment.
Students don’t even realize their progress because they’re used to learning by reading static text or watching a video. When they’re actually doing, they’re learning better than in any of those other environments, but it feels more like play. It feels more like getting a chance to practice, which creates more engagement. Our student satisfaction scores have gone up once we’ve started introducing more of this. It’s a win-win because it gives students what they want and also helps them learn the material better.
Which interpersonal skills will help one worker win out over another as AI takes on more technical work?
Skills like effective communication, curiosity, empathy, and human judgment are becoming increasingly valuable and skill-defining. I want to be careful not to create a false choice between the interpersonal and the technical — many of the careers that we serve still require a person to pass a certification exam, and those certification exams measure deep technical knowledge associated with the field. Technical mastery still matters, but the opportunity is to layer increasingly authentic practice experiences on top of that knowledge. That way, when someone shows up to work on day one, whether it’s in an office setting or a clinical setting, they feel more like a seasoned professional.
That’s where simulations become incredibly powerful. We have scenarios where you’re practicing difficult customer conversations or explaining a diagnosis to a pet owner, going back to the vet tech example. We can use those simulations, technology, and real-time feedback to get people comfortable with more of that interpersonal element of a career.
It’s one of the few ways to safely practice human interaction at scale. You can do it online in ways you didn’t use to be able to. As AI handles more of the routine, technical work, those interpersonal moments become even more important. If you can show a potential employer that you’re competent and confident on the job, and have an ability to work with a team and work with customers effectively, that’s what they’re going to look for and how they’re going to make their hiring decisions.
Building a career that evolves with AI
What does it mean for a career to be AI resilient now, and how do you expect that definition to change over the next decade?
My answer to this has changed in the last couple of years. If you asked me that a year or two ago, I probably would have talked more about choosing the “right professions.” Today, I think it’s a little bit more about people’s mindsets. Even working with the product managers on my team, the people who will thrive aren’t necessarily the ones who know the most today. They’re the people who stay open and willing to learn and adapt as technology changes the roles that we’re all in.
You can’t assume that your job, or even your career in the field that you’re in, will look the same in five or 10 years the way that you used to. That’s scary, but the one thing I know for sure is that change is inevitable. Resisting change is not going to get you anywhere. It’s about acknowledging that and getting comfortable evolving alongside it. As new tools come along, work with them, practice them, get to know them, and make your own judgments about how valuable they are in your day-to-day.
I also think that changes how we think about education. Education can’t be something that you’re ever really finished with. It’s not the thing that comes before the career phase of your life. It has to evolve with you and your career over time. That means it’s a lifelong endeavor, a lifelong process.
At Penn Foster, we hope to become a lifelong partner for workers in these fields and employers in these industries — a trusted source of knowledge, information, and skills that they can continue to come back to as this technology changes and as these roles change over time.
Can you share any advice you’d offer to an 18-year-old high school graduate, and would that differ for someone who’s mid-career?
A big part of our work at Penn Foster is our large online high school program. We are working with 15-, 16-, and 17-year-olds every day, and they’re working toward their high school diploma. As you would expect, a lot of them want us to help them make those decisions about what comes next.
For the last 30 years or so, as a society, we’ve been sending one message: college for all. The only surefire way to get a middle-class lifestyle is to get your four-year degree, and that probably has always been wrong. It’s more wrong today than ever because a lot of these professions that can lead to a stable career don’t require a college degree.
None of the careers that we’re preparing people for, outside of a few associate degrees, require college. They require a set of skills, and they often require a certification to get started, but then they can lead to a successful career.
My biggest piece of advice is to ignore the people who tell you that there’s only one way. Broaden your exposure to different careers and different fields, and don’t be afraid to try something. It’s not the biggest risk in the world to do something for a couple of years and then decide that you want to go a different way.
People get hung up on: I have to make this choice at 18, and it’s the only time I get to make this choice. The stakes are super high. I think the stakes are lower than a lot of people realize. As education becomes something that you come back to again and again throughout your life, it’s also that recognition that you can change your mind and try different things.
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