Key points:
AI is rapidly reshaping education, but not always in ways that support learning. A growing number of AI tools promise to “help” students by doing assignments, writing papers, solving problem sets, or even completing exams automatically.
While these tools may appear convenient, they raise an important question: Are they removing barriers to learning, or removing learning itself?
When AI replaces effort, learning pays the price
AI is becoming deeply embedded in academic life. According to Tyton Partners’ Time for Class 2025 report, 30 percent of instructors and 42 percent of students report using generative AI weekly or daily. As AI becomes a routine part of the learning experience, the question is no longer whether it’s being used, but how those tools are shaping the learning process.
The fast rise of AI that completes coursework for students poses real risks to learners, instructors, and institutions. Widely accessible automation tools, such as Google’s Homework Helper, Companion’s Einstein, Quick Solver AI, or Eduhack.ai can bypass the very effort that learning requires. Learning isn’t meant to be effortless. It is inherently iterative and time-consuming. Progress comes through practice, application, and repetition. That effort is not a flaw in the system; productive struggle is the foundation of how people learn.
When we evaluate AI in education, we should ask a simple question: Does this technology remove barriers to learning, or does it replace the desirable effort that makes learning possible? If the answer is the latter, we risk eroding the educational process itself. When students rely on AI to generate answers rather than work through complex problems, the learning loop breaks down. Without application and repetition, learning simply doesn’t happen.
At the same time, we should acknowledge why students might turn to these tools in the first place. Too often, learning experiences feel passive or disconnected. When courses are engaging, personalized, and interactive, students are far more likely to apply the effort required to learn critically.
AI should support that effort, not replace it. Tools that help learners better understand concepts, provide guidance, or reduce unnecessary friction in the learning process can be incredibly powerful. In fact, the Time for Class report shows that 84 percent of students still prefer human-focused guidance, reinforcing the importance of AI that supports–not replaces–instruction.
AI’s greatest challenge isn’t technology–it’s privacy and trust
The risks of automated AI tools extend beyond learning outcomes. Data privacy and security are equally critical concerns. We are seeing tremendous innovation in the AI ecosystem and some of it is genuinely transformative. But there are still many unknowns with these emerging solutions. Many newer or less mature vendors lack strong safeguards, and unfortunately some tools are designed to capitalize on students looking for quick workarounds. Students may download applications without fully understanding how their data is being used, stored, or shared without their consent.
Educational institutions have a solemn responsibility to protect learner data, and AI developers must share that responsibility. AI systems should be transparent about how they operate, what data they collect, and how that data is used. Institutions should always know exactly what technology they are deploying and how it interacts with their learning environments.
I’ve always been serious about the protection of learner data and believe it should not be used to train large language models. Protecting student data is not optional–it is foundational to building trust in educational technology.
Look for AI that transforms learning
The goal should not be to avoid AI in education. The goal should be to leverage AI that strengthens learning rather than replaces it. One of the most promising approaches I’ve seen from educators is a shift toward more active learning and assessment. Instead of assignments and tests that reward memorization, active experiences ask learners to analyze, interpret, and apply knowledge in meaningful and engaging ways. This deeper engagement naturally discourages shortcut tools and promotes real understanding.
AI developed with purpose for learning can support this shift.
When applied thoughtfully, it can help educators transform their lectures or static materials into interactive content that encourages participation and application. For example, AI can convert recorded lectures or slides into dynamic modules where students actively engage with concepts, test their understanding, and apply what they’ve learned–all tied directly to learning outcomes.
AI can also provide learners with on-demand support: answering course questions, guiding them back to relevant materials after quizzes, or helping them explore concepts outside of traditional office hours. Used this way, AI becomes a learning companion, not a substitute.
The AI choices institutions make now will shape learning for generations
Institutions are still working to establish guardrails for AI use. According to the most recent Time for Class report, 45 percent of instructors say preventing cheating is one of their top instructional challenges. As AI tools become more embedded in academic life, thoughtful governance and transparency will be essential to ensure these technologies strengthen learning rather than undermine it.
As institutions evaluate AI solutions, a few principles should guide their decisions.
AI should be learning-led by design and built around robust learning science fundamentals. Technology should reinforce proven teaching practices, not bypass them. AI should help educators work more efficiently while preserving the quality and integrity of course content and aligning to learning outcomes. In short, educators must remain in control.
Human oversight should always be built into AI systems. Educators–not algorithms–should make the final decisions about course content, feedback, and assessments. Keeping people at the center remains a top priority for leaders. Justin Rose, associate vice president, Information Management and Digital Learning at Southeastern University, puts it this way: “Humans working in concert with emerging technologies is always going to be the key recipe for success.”
Transparency and governance are also essential. Institutions should control how AI interacts with their data. They should be able to set permissions, define guardrails, and understand exactly how systems operate. Educational impact must be measurable.
AI should improve outcomes we can observe: stronger engagement, deeper understanding, and meaningful time savings for educators. AI will undoubtedly play a major role in the future of education. The question isn’t whether it will be used, but how it will be used.
If we design AI systems that shortcut the learning process, we risk undermining the very purpose and value of education. But AI that deepens engagement, makes learning more personal, and supports meaningful effort has the potential to become one of the most powerful tools educators, learners, and institutions alike have ever had.
The future of AI in education should never be about replacing learning; it should always be about strengthening it.
