grades vs – A new AI reality is colliding with grade-focused schooling. Misryoum argues that banning tools won’t fix motivation—rewards must shift toward learning and student well-being.
Education has long relied on a simple idea: measure performance, assign a grade, and move students forward. But in classrooms where learning is the supposed goal, grades have often acted like the destination.
AI is now accelerating a problem that was already built into the system: if the fastest route to a good grade is to “perform” rather than “learn. ” then using AI to do the work becomes an almost rational strategy.. Misryoum sees this as the central education tension of the moment—when assessment culture rewards output over understanding. students will naturally take shortcuts that look smarter on paper.
The deeper mismatch is not technological; it’s psychological.. Learning, especially for beginners, tends to require productive difficulty—struggling, making mistakes, revisiting concepts, and repairing misunderstandings.. AI systems are designed to reduce friction: they can generate answers quickly and smoothly. often without judgment and without the discomfort that pushes learners to build durable knowledge.. Misryoum’s perspective is that this can be corrosive for students who are still developing foundational skills. because the very effort that strengthens learning may be bypassed.
Early classroom and research signals discussed in this commentary suggest a troubling pattern: when AI is used to complete schoolwork. it may slow skill acquisition and weaken self-regulation.. The concern is not that AI is “cheating” in a moral sense—rather. it can change what students believe school is for.. If the endgame is grades, students will treat AI as a performance machine.. If the endgame becomes learning, AI can be a support tool instead of an escape hatch.
What makes this moment especially difficult for education leaders is how predictable the response can be.. When schools react primarily with bans. detection tools. or stricter monitoring. students often find workarounds. and classroom relationships can become more adversarial.. Misryoum’s editorial point is that policing does not automatically create motivation; it may only change the methods students use to survive a system that feels controlled and disconnected.
That’s why the commentary argues for a bigger shift: replace grades as the organizing logic of schooling and build instruction around student flourishing.. The framework is motivation-centered.. Students thrive when they experience autonomy (a sense that goals matter to them). competence (feeling genuinely capable. not merely credentialed). and connection (relationships with teachers and peers that make effort feel worthwhile).. Misryoum reads this as a practical design challenge for schools. not an abstract philosophy—classrooms need to give students reasons to engage that are internal. not just externally enforced.
In classroom terms. motivation research aligns with instructional strategies that are well known but often hard to implement at scale: meaningful choice in learning tasks. feedback that focuses on process and understanding. explanations that connect content to students’ values. and structures that normalize setbacks instead of treating them as failure.. The commentary also notes that need-supportive teaching often declines as students move into higher grades—precisely when support is most needed.. Misryoum highlights the systemic risk here: students who already feel less agency and more pressure are also the students most likely to seek shortcuts when high-stakes performance dominates the culture.
The AI question then becomes less about technology ethics and more about educational design.. If students use AI to get good grades while learning less. that isn’t just a rule-breaking issue—it’s an incentive issue.. Misryoum believes schools should ask a tougher question than “How do we detect AI use?” Schools should ask: “What does our grading system train students to do. day after day?” If the answer is “optimize performance. ” AI will simply make that strategy easier.
There are logistical reasons grades have been so hard to replace.. They offer shorthand in large systems where teachers handle many students and where accountability frameworks demand quick signals.. Still. the commentary’s core claim is that learning has often been treated as an accidental byproduct rather than a deliberate outcome.. Misryoum’s editorial stance is that AI removes the excuse for that convenience—because it makes performance without learning not only possible. but scalable.
Globally. education systems are already grappling with AI in exams. assignments. and tutoring contexts. and many are choosing to tighten assessment rules.. Misryoum sees a likely divergence in what happens next: one path is technical enforcement—more surveillance. more detectors. more formal bans.. The other path is pedagogical reform—altering assessment. classroom practices. and curriculum pacing so that understanding and growth are the rewarded behaviors.
The stakes are not only academic.. When students feel pressured. controlled. or disconnected from why learning matters. they look for tools that help them escape that experience.. AI can become one of those tools.. Misryoum’s implication for the future is clear: the most effective way to reduce problematic AI use may be to redesign school so that students don’t need to escape in the first place.
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