Summary for Educators

AI Isn’t the Main Problem—It Just Shows Us What That Problem Is By Tyler Rablin (Edutopia)

In this thoughtful article, Tyler Rablin argues that generative artificial intelligence (AI) is not the real issue in education; instead, it reveals a deeper, long-standing problem in schools—the prioritization of grades and points over genuine learning. Rablin contends that students gravitate toward the most “energy-efficient” path to achieve outcomes because the human brain is wired for efficiency. In classroom settings where points and grades are the dominant focus, students naturally seek shortcuts, and AI simply makes reaching those shortcuts easier. Thus, the question educators should ask isn’t whether or not to use AI, but whether students are fundamentally focused on learning itself. 

Rablin begins by pointing out that the brain’s inherent drive to work efficiently isn’t a flaw. Historically, efficient energy use was vital for survival—for example, choosing the simplest route to food or home to conserve energy in case of danger. Today, that same drive leads students to prioritize the quickest path to high grades rather than engaging deeply with content for understanding. Generative AI strategies that bypass effortful thinking illustrate how the educational system’s emphasis on grades misaligns with authentic learning goals. If students set learning rather than points as their objective, using AI to skip cognitive work wouldn’t serve them; they would instead harness AI in ways that deepen understanding. 

A key lens through which Rablin examines this issue is Goodhart’s Law: when a measure becomes the target, it ceases to be a good measure of the underlying construct. In education, grades were intended to measure student learning, but as they become targets themselves, their value as true indicators of mastery diminishes. Rablin asserts that students often work toward grades, not toward comprehension or skill development, and this misalignment drives both behavior and instructional choices. Given this reality, debates about AI usage in classrooms are premature unless teachers first address whether their students are truly learning or merely striving for better grades. 

To shift focus back to learning, Rablin offers four practical strategies educators can implement—even without completely overhauling their grading systems.

1. Attach Learning Memos to Assignments A learning memo requires students to explain how they met specific learning objectives in their work. Instead of merely submitting an assignment for a grade, students articulate how the task reflects conceptual understanding or skill application. This reflective component encourages students to think about what they learned and how they learned it, rather than simply chasing a numerical score. Rablin notes that even when generative AI assists with the surface work, the memo prompts students to engage cognitively with essential concepts. 

2. Redesign Assessments with Clear Progressions Rablin highlights the benefit of aligning assessments with clearly defined learning progressions. Such mastery checks help students see what they already understand and what they need to work on next. By breaking success into concrete steps, teachers can help students build confidence and see learning as a process of incremental growth rather than a race for high marks. This clarity can reduce students’ reliance on shortcuts and encourage genuine engagement with the material. 

3. Build in Intentional Reflection about Learning Reflection is a powerful tool for reinforcing learning purposes. Whether after a debate, a project draft, or an assessment, asking students to reflect on what they’ve learned—and why it matters—edges the classroom culture away from score-chasing. Rablin’s reflections aren’t about how good a work is but how it demonstrates understanding. These small but consistent reflections help students internalize that growth matters more than grades. 

4. Prioritize Conversations about Learning Regular, intentional dialogue about learning—either one-on-one or in whole-class discussions—keeps the focus on understanding rather than performance. Questions like “What’s your next win?” encourage students to set micro-goals tied to learning progress, not points. Sharing “unexpected wins” publicly further legitimizes personal growth as an educational priority. These conversations foster agency and reinforce that classrooms value learning trajectories over numerical outcomes. 

Rablin acknowledges the temptation some educators feel to eliminate technology, including AI, from classrooms to prevent misuse. However, he argues that technology’s presence highlights pre-existing misalignments in educational priorities. Simply removing AI won’t solve the deeper issue if the system continues to treat grades as the ultimate goal. To truly support students as learners, classroom systems must re-center on learning itself—measuring it, celebrating it, and guiding students toward it. Generative AI thereby becomes a lens that reveals educational priorities rather than the cause of instructional challenges. 

Original Article

AI Isn’t the Main Problem—It Just Shows Us What That Problem Is By Tyler Rablin (Edutopia)
Source: https://www.edutopia.org/article/prioritizing-learning-despite-ai-t... Edutopia

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Prepared with the assistance of AI software

OpenAI. (2025). ChatGPT (4) [Large language model]. https://chat.openai.com