{"id":662,"date":"2026-10-21T16:01:45","date_gmt":"2026-10-21T20:01:45","guid":{"rendered":"https:\/\/solutionsreview.com\/edtech\/?p=662"},"modified":"2026-10-21T16:02:09","modified_gmt":"2026-10-21T20:02:09","slug":"the-ai-that-improves-learning-doesnt-remove-the-thinking","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/edtech\/2026\/10\/21\/the-ai-that-improves-learning-doesnt-remove-the-thinking\/","title":{"rendered":"The AI That Improves Learning Doesn\u2019t Remove the Thinking"},"content":{"rendered":"

\"The<\/p>\n

Brittany Greer, Executive Director and Founder at Rosie Riveters<\/a>, explains how AI that actually improves the learning experience won’t remove the importance of thinking from that experience. This article originally appeared in Insight Jam<\/a>, an enterprise IT community that enables human conversation on AI.<\/strong><\/em><\/p>\n

\"\"<\/a>The question facing schools is no longer simply whether students will use artificial intelligence. It is whether that use will improve what students learn.<\/p>\n

It is surprisingly easy to confuse a better product with a better learning outcome. AI can help a student finish an assignment faster or produce more polished writing. But neither proves that the student understands more.<\/p>\n

There is another question we should be asking alongside that one: Can AI help educators create more time and better conditions for learning that students have to do themselves?\u00a0That includes time to build something, discover why it does not work, and try again. Time to explain an idea to a classmate and reconsider it. Time for a teacher to recognize when a student needs an explanation and when they need another opportunity to work through the problem.<\/p>\n

The opportunity is to use the tool\u2019s efficiency to make room for the learner\u2019s effort.\u00a0<\/strong><\/p>\n

The research gives us reason to be thoughtful. A 2026 systematic review and meta-analysis of generative AI in STEM education found widely varying results. Its initial positive overall finding largely diminished after the researchers accounted for potential publication bias. Most of the studies also involved higher education, limiting what they can tell us about younger learners.<\/p>\n

The useful question is therefore more specific than whether AI \u201cworks.\u201d What learning does a particular use support, and what does the student still have to do?<\/p>\n

Researchers at Old Dominion University, for example, are developing MAESTRA, an engineering tutor that combines interactive simulations with questions intended to help students connect what they observe to mathematical concepts. It is a design being developed and evaluated, rather than established proof of improved learning. But it illustrates an important direction: helping students reason through an experience. That principle matters well beyond an interaction with a chatbot.<\/p>\n

When something does not work, a student has information to interpret and a decision to make. An educator might ask what they expected to happen, what they observed, and what they could change. The student tests an idea and uses the result to decide what comes next. That is productive struggle. It requires purposeful challenges and thoughtful support. Leaving a student confused indefinitely is not the goal. Giving away the solution at the first sign of uncertainty can also remove the very thinking the experience was designed to develop.<\/p>\n

Hands-on activity alone does not guarantee that thinking, either. Students can follow instructions and finish a project without understanding why it works. The learning depends on the decisions they make, the explanations they develop, and the opportunities they have to revise. Those opportunities take time.<\/p>\n

A student may need another attempt. A group may need to compare competing explanations. A teacher may need to pause the planned lesson because the class has uncovered a misconception worth exploring.<\/p>\n

If every gain in efficiency becomes an expectation to cover more material, we can lose the educational value of the time we have saved. This is where I would like to see more attention in the AI conversation: how the technology could support the educator holding all of this together.<\/p>\n

A teacher working with 25 students is continually gathering evidence. A student\u2019s prediction, a conversation during a build, a written reflection, and an unsuccessful attempt may each reveal something different about their understanding. The challenge is finding time to bring that evidence together and respond to it.<\/p>\n

Consider a classroom in which students are building and testing simple circuits. Several students finish successfully, but their explanations suggest they do not yet understand why the circuit works. Another student\u2019s circuit remains unfinished, yet their observations show increasingly sound reasoning. Completion alone would give the teacher an incomplete picture.<\/p>\n

A teacher-directed AI tool could help organize existing observations and student explanations, flag possible patterns for review, and suggest questions for the next lesson. The teacher could check those suggestions against what they actually saw and decide who needs a demonstration, who needs a different challenge, and who needs more time.<\/p>\n

This is a potential use to develop and test. It depends on appropriate protection of student information and on teachers being able to inspect and correct the tool\u2019s interpretation. Its value would come from helping educators use their own classroom evidence without adding another layer of documentation. Students would not necessarily need to spend more time interacting with AI for that support to be useful.<\/p>\n

AI could help teachers make more room for hands-on learning while becoming more responsive to what happens during it. But a promising use is not enough. We need evidence that it helps.<\/p>\n

A randomized trial involving students in grades seven through nine found that targeted AI interventions did not significantly outperform standard ChatGPT use on subject knowledge or the tested learning strategy. Giving an AI system educational instructions does not automatically make it an effective educational intervention.<\/p>\n

The same scrutiny should apply to tools designed for teachers. Does organizing classroom information actually reduce their workload? Does it help them make better instructional decisions? Does the time saved translate into meaningful opportunities for students to practice and deepen their understanding?<\/p>\n

And ultimately, what can students do with what they have learned? Can a student explain why a design failed and make a reasoned adjustment? Can they apply a concept when the materials or circumstances change? Can they recognize an error without waiting for someone else to identify it?<\/p>\n

Engagement and completion can provide useful information. Neither is sufficient evidence of understanding. A correct answer has never told us everything about the thinking behind it. This is also where the conversation about technology meets a larger question: Do our education systems support the learning we say we value?<\/p>\n

Through a decade of dialogue with educators and administrators, including Rosie Riveters\u2019 professional development on productive struggle, we repeatedly hear about the difficulty of making room for this work when clear benchmarks and rewards for it are missing.<\/p>\n

We can encourage teachers to prioritize reasoning. But if pacing expectations leave little room for revision, and success is judged primarily by how quickly students reach correct answers, teachers are being asked to work against the conditions around them.<\/p>\n

Changing those conditions starts with examining what systems formally expect and measure, then working with educators to understand how those expectations shape classroom practice. Where do assessments recognize reasoning? What flexibility do teachers have to revisit a concept? What counts as progress when a student\u2019s understanding improves before their project succeeds?<\/p>\n

The answers should guide practical adjustments to how learning is supported, measured, and rewarded. Hands-on learning and productive struggle need to be possible within the school day, without depending on an outside program or an educator continually finding extra time.<\/p>\n

AI could help make evidence of that learning easier to organize and use. It cannot, on its own, ensure that the learning receives time or that educators are supported and recognized for making it happen. The measure of educational AI should be how much meaningful thinking it makes possible rather than how much thinking it can replace.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Brittany Greer, Executive Director and Founder at Rosie Riveters, explains how AI that actually improves the learning experience won’t remove the importance of thinking from that experience. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI. The question facing schools is no longer simply whether students will […]<\/p>\n","protected":false},"author":1516,"featured_media":664,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[14],"tags":[26,109,111,110],"acf":[],"yoast_head":"\nThe AI That Improves Learning Doesn\u2019t Remove the Thinking<\/title>\n<meta name=\"description\" content=\"Brittany Greer at Rosie Riveters explains how AI that improves the learning experience won't remove the importance of thinking.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/solutionsreview.com\/edtech\/2026\/10\/21\/the-ai-that-improves-learning-doesnt-remove-the-thinking\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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