The sight is becoming increasingly familiar: a student in their final year of university, laptop open, typing a few instructions into a generative AI tool. Within minutes, what used to be a weeks-long assignment—whether a business plan, market analysis, or research paper—is ready to submit. The results are polished, precise, and formatted to impress. Yet the question that lingers is quietly unsettling: are students still learning, or are they just getting better at outsourcing?
For decades, higher education has leaned on essays, reports, and polished deliverables as benchmarks for academic performance. The idea was that if a student could craft a compelling essay or a sound business strategy, they must have internalized the key concepts. But with tools like ChatGPT and Claude now at everyone’s fingertips, the final product no longer tells the full story. The work might look great—but did the student really do the thinking, the analysis, the struggling and refining that leads to genuine understanding?
In recent semesters, many instructors have quietly observed puzzling spikes in student performance. A student who used to struggle with clarity and structure suddenly delivers near-publishable work. Another hands in a pitch deck so slick it looks like it came from a professional consultancy. Yet when asked to explain their choices, they stumble. It's not that AI is doing anything inherently wrong—it's that we haven't yet adapted our ways of teaching and assessing to accommodate its presence. 🎓
The problem isn't that students are using AI. It's that education systems haven't quite caught up with what that means. Teaching staff often find themselves playing detective, trying to figure out whether work is original or AI-enhanced. But even the most advanced AI detectors yield inconsistent results, and asking every student to justify every sentence of a report is unrealistic. The issue isn't the tool—it's the framework around it.
Some educators are starting to shift the focus from product to process. Instead of obsessing over the end result, they ask: how did the student arrive at this? What was their path from idea to execution? For instance, instead of grading a single final report, students submit a progression of drafts—an outline, a rough version, peer feedback, and finally a polished submission. This approach reveals the student’s thinking, their capacity to revise, to incorporate feedback, and to question their own assumptions.
One lecturer shared the story of a student who turned in a flawless market entry plan for a startup in Southeast Asia. On paper, it was impeccable. But during a ten-minute face-to-face conversation, it became clear the student hadn’t actually considered geopolitical risks or regional consumer behavior. The plan had holes—but the AI had masked them beautifully. By integrating short oral discussions after submissions, educators are able to surface the depth of a student’s understanding in a way no document ever could.
Real-time, in-class tasks are also gaining traction. Instead of assigning essays to be completed at home, some instructors are organizing classroom writing sessions, collaborative presentations, and group debates—spaces where students must respond to challenges on the spot. These moments are often chaotic, imperfect, even messy—but they are undeniably real. One engineering professor likened it to “taking the training wheels off” and letting students show what they can do without the algorithm riding shotgun. 💡
Rather than banning AI tools outright, a growing number of teachers are asking students to reflect on how they use them. Some courses now require students to keep AI reflection journals where they document their interactions with generative tools. A student in a management course wrote candidly about asking ChatGPT to help outline a conflict resolution strategy for a case study. The AI response was sound, but vague. The student then used it as a springboard, cross-checking it with readings and adding examples from their part-time job at a restaurant. That assignment wasn't just about completing a task—it was about thinking critically and ethically with the help of AI, not instead of it.
Assessment rubrics, too, are undergoing transformation. More and more, they reward not just correctness, but clarity of thought, self-awareness, and growth over time. Some professors now assign value to the way students revise their work or engage with critique, not just the final artifact. One teacher shared how a previously underperforming student excelled after being allowed to talk through their ideas in voice memos, then shape them into an essay with the help of both AI and human feedback. It wasn’t faster, but it was richer—and far more meaningful. 💬
Of course, this new approach brings its own challenges. Time is a real concern. Process-based grading can feel overwhelming. But many institutions are adopting peer review models and structured self-assessment tools to lighten the load. When students review one another’s drafts, they develop both empathy and critical faculties—and often catch errors before the teacher needs to. It becomes a shared responsibility, not a top-down judgment.
Then there’s the tech gap. While some students breeze through assignments on the latest laptops with lightning-fast internet, others are juggling old hardware and unstable Wi-Fi. One architecture student recounted how her design portfolio crashed multiple times while uploading to a cloud platform from a local café. Institutions must acknowledge these disparities and provide alternative formats, support centers, and flexible deadlines when needed. Equity can’t be an afterthought in this new landscape.
Faculty support is essential. Many educators are just beginning to explore what prompt engineering means or how to recognize AI’s blind spots. Professional development is no longer optional—it’s survival. Some universities now offer boot camps on AI literacy, ethical dilemmas, and creative assessment strategies. One tutor described how a shared marking session changed her approach: she learned to distinguish work that merely sounds impressive from work that shows genuine intellectual movement.
Policies also need catching up. In the absence of national guidelines, many universities are drafting their own AI usage codes, setting clear expectations around transparency, citation, and responsibility. One professor implemented a simple rule: if a student used AI in any part of the assignment, they had to describe how and why. This shifted the tone from punishment to partnership—AI wasn't the enemy, but a tool to be used wisely.
What’s emerging is a richer, more complex picture of education—one where the emphasis is on human agency, critical reflection, and meaningful growth. A psychology student may use AI to draft ideas for an experiment, but the real value lies in how they refine it, test it, and interpret the results based on lived experience and academic knowledge. A marketing student might begin with a bot-generated customer persona, but the depth comes in questioning its assumptions and tailoring it to real-world demographics.
Students, it turns out, don’t just want shortcuts. They want to learn—they just don’t want to be stuck in outdated methods that no longer reflect the world they live in. And that world includes AI, whether we like it or not. But AI doesn’t erase the need for human insight. It makes that insight more necessary than ever. After all, no machine can truly understand what it feels like to pitch a business idea that might change your family’s future. Or to find your own voice in a paper about ethics and identity. Or to explain a complex theory to a room full of skeptical peers, sweat on your brow, hands shaking slightly, and still push through. That’s the real test. And that’s the kind of learning that lasts. 🌱