Just a few years ago, as I was wrapping up graduate study, I conducted a systematic review of Open Educational Resources (OER) to ask a simple question: Can open licensing and freely available materials meaningfully expand educational opportunity?
I was motivated by a conviction that Education is meant to be the inheritance of every citizen and, as such, access to it should not be curtailed by the state, by another individual, or by any other public or private circumstance. Yet, too often remains, in practice, access to the best in Education remains gated by money, licensing, or sheer institutional inertia. Some barriers are visible on a college bill, others hide in daily practice, or in the reluctance of institutions to do things differently. I studied Open Educational Resources because they promised a clean break from all that. Make learning materials free, open, and adaptable, and you unshackle students from costs and teachers from rigid courseware. Freedom begets progress… At least, that was the faith.
The answer was more complicated than the slogans suggested. The basic logic of OER remains compelling. By removing price tags and copyright barriers, these resources could ease the financial burden on students and give teachers freedom to tailor materials to local needs. In theory, access expands and learning follows. In practice, however, the research base reveals persistent weaknesses. Most studies I reviewed neglected to control for the very conditions that shape educational outcomes: prior knowledge, teacher practices, instructional quality, and the difficulty of the materials themselves. As a result, claims that OER boosts learning often rest on shaky ground. When students have no book at all, giving them a free one does help. However, once that floor of access is raised, what matters more is whether the content supports sound instruction and whether the teacher can use it well. Move past the basic rescue provided by access, and the old truths reassert themselves: poor teaching, sloppy sequencing, and weak practice tasks do not become potent simply because they carry an open license.
This distinction between access and instructional rigor is easy to overlook. It is also the same blind spot that now threatens to repeat itself, more dramatically, under the allure of Generative AI. Large Language Models can produce an endless supply of quizzes, explanations, and lesson plans on command, often at zero marginal cost to the end user. Like OER, AI seemingly lowers the cost of producing and distributing knowledge artifacts.
Yet abundance, while seductive, is no guarantee of educational value. A thousand generated practice questions mean little if they misrepresent what makes practice effective. A summary dashed off by a Large Language Model may feel helpful, but for the purpose of instruction, it lacks coherence, clarity, or cognitive scaffolding, the whole project risks flattening student thought into the regurgitation of trivia rather than deepening understanding. Or as Carl Hendrick put it,
If students rely on AI to generate ideas, structure arguments, or retrieve facts, they run the risk of cosplaying domain knowledge while bypassing the effortful processes that lead to genuine learning. The result is a kind of intellectual outsourcing that short-circuits schema construction and leaves students with shallow fluency but fragile understanding. The point is not producing the thing, it’s the effort in producing the thing.
The lesson my dissertation keeps reminding me is that the removal of barriers—whether financial, legal, or technical—must be paired with a rigorous commitment to instructional quality. Open licensing gave us freedom to adapt and share, but without careful study of what works and for whom, that freedom often produced uneven results. We now see the same tension with AI. The real work lies not in churning out more content, but in designing, testing, and refining how that content interacts with how people learn. Good teaching, whether human or machine-assisted, demands a respect for how knowledge is constructed. And as far as we know, that pursuit is best made through clear sequencing, appropriate challenge, timely feedback, and opportunities to practice and apply new ideas.
Open Educational Resources did not transform learning simply because they were free. They helped when they were thoughtfully integrated, well-crafted, and grounded in sound pedagogy. Generative AI, for all its speed and novelty, will follow the same law: it will serve as a tool for expanding opportunity only if paired with the quiet, deliberate discipline of instructional design and careful research.
If we are serious about using AI to broaden educational horizons rather than flatten them, we must look beyond the allure of frictionless generation and focus instead on the sturdy, unglamorous, messy, uneven, and perhaps at times invisible craft of building knowledge.
Access is the beginning, not the end. What happens after we open the door determines whether learning truly takes root.