How many Volkswagen Beetles from before 1980 do you see on the streets in 2026? Not a lot, but also not none. While the number of daily drivers has declined from year to year, there is still a solid fleet out there on the streets.

How many Tesla Model Ys do you think we will see on the road in 2071—45 years from now? If your answer is anything other than zero, I would be surprised.

The difference is a matter of design philosophy. Beetles are designed to be maintained. Drivers change the oil and filters and pay for periodic repairs. And in return, the useful lifespan of the car can extend for decades.

Teslas, on the other hand, are a product of the tech industry’s penchant for planned obsolescence. They are designed to produce the best possible value in their first few years on the road. They provide fantastic driving experience with frequent software updates and integrated, computer-controlled everything. Then, when the changing technological environment renders their chips and boards obsolete, the expectation is that drivers will discard them and move on.

Working on curriculum design for a professionally oriented graduate program, I think about this distinction a lot. Especially in programs where career advancement is an important end goal, the longevity of a degree's value is perhaps the most essential design priority. Students invest vast resources, both time and money, to achieve their graduate degree. And they are unlikely to perceive the mayfly lifespan of a Tesla as a worthwhile ROI.

Like a Beetle, the return on investment on a graduate degree should be long useful lifespan. With regular maintenance—continuing education to keep their skills and expertise in tune—graduates should expect their degree to retain its value in the marketplace.

This implicit contract is complicated by the weight of consensus which, in 2026, is increasingly nudging graduate programs to prioritize AI literacy. Surely, the argument goes, AI use has become an essential workplace skill that students will need to advance in their careers. Therefore, a program whose goal is career advancement has a duty to teach it.

This is true as far as it goes. However, rushing into a version of AI literacy that is too thoroughly defined by today’s technology risks sacrificing the long-term value of a graduate education for a relatively short-term gain. As Wharton professor Ethan Mollick noted on LinkedIn in January 2026, chatbot-like GPTs are "an obvious dead end, likely to be replaced with subagents and skills." And in a few months or years, those too will be obsolete, replaced by the next exciting evolution.

Because the technological environment is moving so fast, acting without sufficient deliberation runs the risk of training students on skills that will be obsolete before graduation.

So how might we embrace our new, AI-enabled reality without sacrificing graduate degrees’ useful lifespan? The mighty Beetle can give us a modicum of inspiration. Volkswagen sold 21.5 million Beetles worldwide over 65 years of production. And while a split rear window model from the 1950s has some differences from a ’70s Super Beetle, every model leans into the car’s strengths: the iconic design, the air-cooled engine, the fuel efficiency, etc.

To contend with the need for AI literacy, graduate programs, too, should lean into what they already do best: teach students how to learn, how to think critically in their fields, and how to maintain their expertise over time. This is not about ignoring practical AI skills—students may well need hands-on experience with current tools. Instead, it is about developing the enduring competency to assess and interact with a technological environment that will look very different three, five, or fifteen years down the road.

Normalize Generative AI

To teach generative AI from a place of strength, programs’ top pedagogical priority should be to frame it as a normal technology. As Princeton professors Arvind Narayanan and Sayash Kapoor put it, we must reject “technological determinism” and both the “utopian and dystopian visions of the future of AI which have a common tendency to treat it akin to a separate species.” We should be “guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion.”

Practically speaking, this means making it one among the many systems that students might deploy in their professional worlds. Generative AI may be a standalone topic in some courses—for example, in data science, where the technology itself is transforming techniques core to the field. However, in many cases, students would be better served by understanding how AI—and AI-enabled platforms—integrate into existing frameworks.

In the University of Pennsylvania’s Master of Health Care Innovation, students learn about AI throughout the curriculum, through the lenses of:

  • Design thinking
    What are the exigent problems faced by the people our work is intended to serve? And what systems—generative AI or something else—might improve their worlds?

  • Health policy
    What are the regulatory pathways for FDA approval of a new medical device? What is the emerging consensus for how cutting-edge technologies—like AI-enabled products—should be positioned for sustainable success?

  • Business strategy
    How might we curate technology to foster ongoing, mutually beneficial relationships among firms and customers? AI may be one important component, but so might wearables, or email sends, or periodic postcards via snail mail.

Specific frameworks will differ depending on the discipline and program. The point, however, is to make AI learning simultaneously relevant and transferable. Give students a sense of how generative AI fits their professional goals and the strategic goals of their fields. And help them see how they might evaluate other transformative technologies using those same techniques and skills.

Place AI in Context

Making learning relevant and transferrable also means placing AI in a social and relational frame. In 2026, generative AI is transforming students’ professional lives. But transformative technologies are relatively common and creating long-term value means helping students ride the wave. In 2006, it was handheld computing; in 1996, it was the World Wide Web; in 1986, it was the personal computer. Understanding how emerging technologies transform the dynamics of professional life and practice prepares students for whatever comes next.

Some questions relevant to a wide range of professional degrees include:

  • How AI in the workplace affects team dynamics
    2025 research suggests that generative AI use has a difficult relationship with interpersonal trust at work. How might we understand these challenges with regards to implementation?

  • How AI affects customer relationships
    Are patients as willing to engage in frank conversations with their physicians, for example, in the presence of something like ambient listening technology?

  • AI’s impact on professional practice in students’ fields
    Will future work tend more toward bot supervision and management? And how will that affect how students practice their skills?

  • How the situation might change as we progress along the adoption curve
    What happens when regulation catches up with the new reality? How are popular views of the technology likely to change over time?

Questions like these integrate AI into professional practice while offering structures for assessing and interacting with future technologies. A long useful lifespan means that students should still be able to apply their learning when—like iPhones and the World Wide Web—AI is no longer the next big thing.

Conclusion

None of this means that graduate programs can afford to ignore generative AI. Students legitimately perceive AI to be an important facet of their current professional needs. And they crave clear, consistent expectations for acceptable and effective use. Supplementing a long-term outlook with skills that will help them get hired right now demonstrates the kind of student-centered thinking that is simultaneously appealing and high value.

Creative pedagogy can ensure that students graduate with demonstrable proficiency in current AI tools—and hands-on experience they can discuss in interviews—while keeping an eye on the next horizon. Instructors might, for example, lean into metacognition, asking students to reflect critically on their experiences, even as they use AI chatbots as thought partners to complete assignments. And they might teach students how to critically evaluate research methods and results by verifying and analyzing the outputs of AI-assisted analyses.

Resource permitting, programs might also offer focused modules on current AI tools alongside their core curriculum. This allows students who need targeted, short-term skills to gain that knowledge while still building a strong conceptual foundation.

Admittedly, a curriculum that looks like a Volkswagen Beetle does not, on its face, sound very exciting. The Beetle is an economy car loved by enthusiasts, aging hippies, and luddites leery of electronic fuel injection. While a Tesla Model Y—sitting at the cutting edge—has been the bestselling electric vehicle in the United States at least since 2023. But consider what students are actually buying: a graduate degree is not a piece of tech where most of the value is up front, but an investment that provides meaningful long-term returns.

A graduate education that helps students build sustained, evaluative skills may not sound as exciting as an “AI-powered educational experience.” But it is, genuinely, invaluable.

By Adam D. Zolkover, MA, Associate Director for Curriculum Design and Online Education, MEHP Online

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