{"id":11250,"date":"2026-01-27T13:02:27","date_gmt":"2026-01-27T21:02:27","guid":{"rendered":"https:\/\/learnercentered.org\/?p=11250"},"modified":"2026-01-28T13:26:59","modified_gmt":"2026-01-28T21:26:59","slug":"rethinking-samr-in-the-age-of-ai-why-the-model-needs-a-second-axis","status":"publish","type":"post","link":"https:\/\/learnercentered.org\/blog\/rethinking-samr-in-the-age-of-ai-why-the-model-needs-a-second-axis\/","title":{"rendered":"Rethinking SAMR in the Age of AI: Why the Model Needs a Second Axis"},"content":{"rendered":"<div style=\"margin-top: 30px;\"><em>This article originally appeared on <a href=\"https:\/\/www.gettingsmart.com\/2026\/01\/15\/rethinking-samr-in-the-age-of-ai-why-the-model-needs-a-second-axis\/\">Getting Smart<\/a> and has been republished with their permission.<\/em><\/div>\n<div style=\"margin-top: 10px; margin-bottom: 30px;\"><em>Written by Vriti Saraf, Nate McClennen, &amp; Katie Martin<\/em><\/div>\n<hr \/>\n<div class=\"border-t border-gray-700 dark:border-white dark:text-white pt-0.5 mb-3\">\n<p class=\"font-sans font-semibold text-gray-700 dark:text-white\"><strong>Key Points<\/strong><\/p>\n<\/div>\n<ul class=\"space-y-3\">\n<li class=\"flex items-start space-x-2.5 font-sans font-semibold leading-snug\">\n<div>\n<div class=\"text-gray-700 dark:text-white w-3 h-3 svg-fill mt-1.5\">The SAMR model needs a second axis (positive vs. negative impact) to better evaluate AI\u2019s effect on teaching and learning.<\/div>\n<\/div>\n<\/li>\n<li class=\"flex items-start space-x-2.5 font-sans font-semibold leading-snug\">\n<div>\n<div class=\"text-gray-700 dark:text-white w-3 h-3 svg-fill mt-1.5\">AI\u2019s role in education is nuanced\u2014its success depends on whether it fosters relationships, deepens thinking, and expands access rather than replacing meaningful interactions.<\/div>\n<\/div>\n<\/li>\n<\/ul>\n<div><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-11252 aligncenter\" src=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/SAMR-Model-Rethink.jpg\" alt=\"\" width=\"1024\" height=\"512\" srcset=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/SAMR-Model-Rethink.jpg 1024w, https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/SAMR-Model-Rethink-300x150.jpg 300w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/span><\/div>\n<div style=\"margin-top: 30px;\">When the SAMR model was introduced by Ruben Puentedura in the early 2000s, it quickly became one of the most widely used frameworks for talking about technology integration in schools. Its four categories -Substitution, Augmentation, Modification, Redefinition \u2013 gave educators simple language to describe how technology changes learning tasks.<\/div>\n<p>At its best, SAMR made something abstract feel concrete. Teachers could point to a lesson and say:<\/p>\n<p>\u201cI\u2019m substituting paper quizzes with digital ones.\u201d<\/p>\n<p>\u201cI\u2019m modifying research by letting students collaborate in a shared doc.\u201d<\/p>\n<p>\u201cI\u2019m redefining storytelling with multimedia tools.\u201d<\/p>\n<p>But as SAMR spread, the field reshaped it. What was originally a descriptive model became prescriptive. Substitution was cast as \u201centry-level,\u201d Augmentation \u201cbetter,\u201d and Redefinition the gold standard. SAMR stopped functioning as a tool for reflection and instead became a staircase to ascend.<\/p>\n<p>In the era of AI, that staircase behaves like a <a href=\"https:\/\/www.newworldencyclopedia.org\/entry\/Penrose_triangle\" target=\"_blank\" rel=\"noopener\">Penrose illusion<\/a> \u2013 an infinite climb that doesn\u2019t necessarily lead anywhere. AI can make simple tasks powerful or make advanced tasks hollow. The single-axis interpretation of SAMR simply isn\u2019t enough.<\/p>\n<h2 id=\"h-a-new-insight-emerging-from-the-portrait-of-a-teacher-project\" class=\"wp-block-heading\"><strong><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-11257 aligncenter\" src=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI.webp\" alt=\"\" width=\"1200\" height=\"913\" srcset=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI.webp 1200w, https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI-300x228.webp 300w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/>A New Insight Emerging From the Portrait of a Teacher Project<\/strong><\/h2>\n<p>This limitation became clear during a recent meeting of the Advisory Council for\u00a0<a href=\"https:\/\/www.ed3dao.com\/portraitofateacher\">The Portrait of a Teacher in the Age of AI<\/a>, a national research and design initiative led by the non-profit organization Ed3. The project brings together educators, researchers, technologists, district leaders, and policy thinkers to rethink what excellent teaching looks like in an AI-rich world.<\/p>\n<p>A central question emerged:<\/p>\n<p>\u201cDoes SAMR need a second axis?\u201d<\/p>\n<p>While reviewing data from our first national survey, where teachers evaluated their use of AI across the SAMR spectrum, we saw a pattern that the original model does not account for. Teachers described Substitution, Augmentation, Modification, and Redefinition in terms of how those actions affected learning, not just what category they belonged to. Some uses strengthened relationships, deepened thinking, or expanded access. Others weakened those very things.<\/p>\n<p>Every level of SAMR showed both a positive and a negative expression.<\/p>\n<p>That observation led to a crucial reframing: SAMR doesn\u2019t just describe what kind of change technology introduces, it also needs to describe whether that change is constructive or destructive.<\/p>\n<h2 id=\"h-the-missing-dimension-samr-positive-and-samr-negative\" class=\"wp-block-heading\"><strong>The Missing Dimension: SAMR Positive and SAMR Negative<\/strong><\/h2>\n<p>Across the survey and the Council\u2019s discussion, the same nuance reappeared: the SAMR level does not determine the quality of practice. The direction of impact does.<\/p>\n<p>Substitution is a clear example.<br \/>\nReplacing repetitive paperwork with AI can free teachers for conferences, feedback, and real human interaction. That\u2019s SAMR positive.<\/p>\n<p>But Substitution can also replace meaningful teacher-student moments with automated scoring that no one reviews, or with chatbot-only communication that eliminates human connection. That\u2019s SAMR negative.<\/p>\n<p>The same duality holds across the model:<\/p>\n<ul class=\"wp-block-list\">\n<li>Positive Redefinition: AI enables richer inquiry, real-world projects, multilingual storytelling, and creative expression.<\/li>\n<li>Negative Redefinition: AI shortcuts struggle, isolate learners, or turn collaboration into parallel conversations with a bot.<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-11254 aligncenter\" src=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI-Image.webp\" alt=\"\" width=\"1024\" height=\"576\" srcset=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI-Image.webp 1024w, https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-AI-Image-300x169.webp 300w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/span><\/p>\n<p><span style=\"font-weight: 400;\">These pitfalls aren\u2019t the fault of educators. They&#8217;re symptoms of systems still rooted in a school-centered paradigm, where learning is standardized, time-bound, and disconnected from authentic growth.<\/span><\/p>\n<p>This leads naturally to a reframing of the model itself. Rather than a single ladder, SAMR becomes a two-axis system:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Axis 1:<\/strong>\u00a0Mode of integration (Substitution, Augmentation, Modification, Redefinition)<\/li>\n<li><strong>Axis 2:\u00a0<\/strong>Direction of impact (Negative \u2194 Positive)<\/li>\n<\/ul>\n<p>Seen this way, SAMR is not a climb from \u201cless innovative\u201d to \u201cmore innovative.\u201d It becomes a set of modes, each capable of enriching learning (positive) or eroding learning (negative).<\/p>\n<p>Four nuances emerge from this new perspective:<\/p>\n<p><strong>1. The level doesn\u2019t predict the quality.<\/strong><\/p>\n<p>The assumption that Substitution is shallow and Redefinition is transformational doesn\u2019t hold. Both can elevate or erode learning depending on how they\u2019re used.<\/p>\n<p id=\"h-2-efficiency-and-erosion-can-look-identical-at-first-glance\" class=\"wp-block-heading\"><strong>2. Efficiency and erosion can look identical at first glance.<\/strong><\/p>\n<p>Efficiency becomes positive when it recovers time for presence, attention, and feedback. It becomes negative when it replaces judgment or narrows human interaction.<\/p>\n<p id=\"h-3-redefinition-can-be-transformative-or-it-can-empty-learning-of-substance\" class=\"wp-block-heading\"><strong>3. Redefinition can be transformative, or it can empty learning of substance.<\/strong><\/p>\n<p>AI can help students do work they could never do alone. It can also give them polished products without struggle, inquiry, or iteration.<\/p>\n<p id=\"h-4-the-dividing-line-is-relational-not-technological\" class=\"wp-block-heading\"><strong>4. The dividing line is relational, not technological.<\/strong><\/p>\n<p>Positive uses strengthened connection, feedback, and access. Negative uses distanced teachers from students, encouraged over-reliance, or replaced human insight with automated output.<\/p>\n<p>SAMR\u2019s four levels describe\u00a0<em>the type<\/em>\u00a0of change. The positive-negative axis describes\u00a0<em>the impact\u00a0<\/em>of that change. Only with both can we understand how AI is shaping teaching and learning.<\/p>\n<h2 id=\"h-samr-isn-t-sequential-and-never-was\" class=\"wp-block-heading\"><strong>SAMR Isn\u2019t Sequential and Never Was<\/strong><\/h2>\n<p>Another misconception surfaced in the Council\u2019s conversation: SAMR was never meant to be sequential. Teachers don\u2019t follow a predictable path from S \u2192 A \u2192 M \u2192 R.<\/p>\n<p>AI makes this especially clear.<\/p>\n<p>A teacher might begin with Redefinition because simulations or adaptive tutors open possibilities that never existed. Later, they might rely on Substitution to save time generating materials so they can meet individually with students.<\/p>\n<p>Seen this way, SAMR functions as a set of modes, each useful under different instructional conditions.<\/p>\n<p>Once the hierarchy of SAMR collapses, a better set of evaluative questions emerges:<\/p>\n<ul class=\"wp-block-list\">\n<li>Does this use of AI deepen human connection or weaken it?<\/li>\n<li>Does it expand teacher capacity or restrict it?<\/li>\n<li>Does it open opportunities for students or narrow them?<\/li>\n<\/ul>\n<p>These questions focus on the direction of impact, not perceived sophistication. Every level of SAMR can be excellent, and every level can be harmful. The distinction lies in whether the practice is progressive or regressive.<\/p>\n<h2 id=\"h-the-adoption-curve-connection\" class=\"wp-block-heading\"><strong>The Adoption Curve Connection<\/strong><\/h2>\n<p>Finally, we concluded that SAMR interacts with the technology adoption cycle.<\/p>\n<p>Early adopters tend to explore Redefinition sooner because they\u2019re comfortable with experimentation. Later adopters often begin with Substitution because it reduces stress and workload, providing an on-ramp that feels safe. This reframing shifts the narrative.<\/p>\n<p>Teachers linger in certain parts of SAMR not because they lack ambition or creativity, but because they\u2019re at different phases of adoption.<\/p>\n<p>Mapping SAMR to the adoption curve helps leaders set realistic expectations and design more effective supports.<\/p>\n<p><strong><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-11255 size-full\" src=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-in-AI-e1768575544901.webp\" alt=\"\" width=\"1600\" height=\"747\" srcset=\"https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-in-AI-e1768575544901.webp 1600w, https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-in-AI-e1768575544901-300x140.webp 300w, https:\/\/learnercentered.org\/wp-content\/uploads\/2026\/01\/Rethinking-SAMR-in-AI-e1768575544901-1200x560.webp 1200w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/strong><\/p>\n<h2 id=\"h-toward-a-two-axis-samr-model\" class=\"wp-block-heading\"><strong>Toward a Two-Axis SAMR Model<\/strong><\/h2>\n<p>This updated structure turns SAMR from a ranking tool into a diagnostic one, far better suited to the realities of AI, where efficiency can erode relationships, and advanced tools can either elevate or hollow out learning.<\/p>\n<p>A two-axis SAMR model acknowledges the complexity of teacher practice, respects the diverse contexts educators navigate, and centers human judgment as the defining variable. As AI becomes more embedded in classroom work, the question that matters isn\u2019t \u201cHow high on SAMR is this?\u201d but:<\/p>\n<p>\u201cIs this use of AI accelerating or slowing down learning outcomes?\u201d<\/p>\n<blockquote style=\"color: #005293;\"><p><span style=\"font-weight: 400;\">The questions raised in this article sit at the heart of the Portrait of a Teacher in the Age of AI initiative. If you\u2019re curious about where this research is heading or how you might support or collaborate on the work, we\u2019d welcome your involvement. Check out the\u00a0project <a href=\"https:\/\/www.ed3dao.com\/portraitofateacher\"><span style=\"font-weight: 400;\">here<\/span><\/a> and join the Brain Trust for regular updates!<\/span><\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>This article originally appeared on Getting Smart and has been republished with their permission. Written by Vriti Saraf, Nate McClennen, &amp; Katie Martin Key Points The SAMR model needs a second axis (positive vs. negative impact) to better evaluate AI\u2019s effect on teaching and learning. AI\u2019s role in education is nuanced\u2014its success depends on whether&#8230;<\/p>\n","protected":false},"author":7,"featured_media":11256,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[11],"tags":[99],"class_list":["post-11250","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-digital-ecosystem","wpautop"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Rethinking SAMR in the Age of AI<\/title>\n<meta name=\"description\" content=\"SAMR needs a positive and negative qualifier added to the model as we move forward with AI integration in schools.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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