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SAMR Model

created 2026-09-10 · updated 2026-09-10 · technology acceptance model, tpack, ai technologies, learning design, teacher ai competency, educational development, k 12, higher ed

The SAMR model — a technology-integration framework developed by Ruben Puentedura that classifies the extent to which a technology transforms learning along four levels: Substitution, Augmentation, Modification, and Redefinition. The lower two levels (Substitution, Augmentation) enhance an existing task — the technology does what was done before, better or more conveniently; the upper two (Modification, Redefinition) transform it — the task itself changes to something not previously possible. In AI in education, SAMR is the standard lens for asking whether generative AI is being used to incrementally improve existing practice or to reconceptualize learning, and it sits alongside TPACK as a way of describing how teachers integrate technology rather than why they accept it.

Questions to Consider

  • When AI is introduced into a course, is it a Substitution (a chatbot replaces a search box) or a Redefinition (a task becomes possible that was not before)? What determines which level is appropriate — and is transformation always the goal?
  • SAMR and technology adoption models answer different questions: adoption theory asks why a teacher or institution accepts a tool; SAMR asks how deeply the tool changes learning. Which question does a given AI-integration study actually answer?
  • A systematic review of AI integration in higher education found most uses sat at the Substitution or Augmentation level, with only one study approaching Redefinition. If that is typical, what does it suggest about the gap between AI's potential and its classroom reality?
  • SAMR is frequently invoked in teacher professional development to help educators plan technology use. Does classifying a lesson's SAMR level change what a teacher actually does, or is it chiefly a descriptive label?
  • Critics argue SAMR, like TPACK, rests on a humanist ontology that treats cognition as unchanged by technology, and that it says nothing about power, data ownership, or equity. Is a level-of-integration lens sufficient, or does AI-era integration need a more critical frame?

Introduction

SAMR describes the depth of technological transformation of learning tasks. Its four levels form an ascending scale from enhancement to transformation: Substitution (the tool replaces another with no functional change — a chatbot in place of a search engine), Augmentation (the tool replaces and improves — a word processor's spell-check over a typewriter), Modification (the task is significantly redesigned — students collaborate in real time on a shared AI-generated draft), and Redefinition (new tasks previously inconceivable become possible — learners co-create with a generative model in ways that have no non-AI analogue). The model is widely used in educational technology research and teacher professional development as a vocabulary for planning and evaluating technology integration, and it is a standard companion to TPACK and to technology-adoption frameworks — though it answers a different question than either.

What the four levels mean

  • Enhancement (lower half): Substitution and Augmentation improve an existing task without changing its nature. Most routine AI use — question generation, quick text generation, summarization — sits here: it is faster and more convenient but does not alter what the learner is asked to do.
  • Transformation (upper half): Modification and Redefinition change the task itself. Vibe coding, real-time co-construction with a model, and adaptive interactive dialogue are tasks that were not possible before generative AI, and represent the transformative end of the scale.

SAMR is frequently paired with the ICAP framework because the two align: the four cooking-to-learning scenarios in Rummel, Nachtigall & Panadero (2026) map ICAP engagement modes onto SAMR levels — ICAP Passive with SAMR Substitution, Active with Augmentation, Constructive with Modification, and Interactive with Redefinition — illustrating a progression from passive delegation to interactive co-construction.

Evidence from the knowledge base

  • AI integration in higher education is largely incremental, not transformative. AlSheikh et al. (2026), in a PRISMA systematic review of 22 intervention studies screened from 959 records, graded AI integration with the SAMR model and found most studies clustered at the Substitution or Augmentation level, with fewer at Modification and only one approaching Redefinition. AI was typically introduced to improve existing practice — dominated by assessment automation and personalized-learning support — rather than to reconceptualize curricula or learning outcomes.
  • SAMR is a common analytical lens for GenAI-driven curriculum change. Sabani et al. (2026) map five curricular shifts (static to dynamic, transmission to capability, local to institutional) onto established lenses including SAMR and constructive alignment, using the model to clarify the pedagogical mechanisms by which GenAI reshapes curriculum.
  • SAMR and TPACK anchor teacher-technology integration standards. Crompton et al. (2026) situate their six faculty technology standards against existing frameworks — TPACK, RAT, SAMR, SETI — and standards (ISTE, UNESCO, DigCompEdu), most of which target K 12 educators or only the teaching portion of faculty roles, leaving a gap in higher-education faculty development.
  • SAMR is a target of the posthumanist critique. Elsayed (2026) critiques TPACK, SAMR, and AI Literacy models for sharing a humanist ontology that presumes a bounded learner whose cognition is fundamentally unchanged by technological mediation, arguing these instrumentalist frameworks cannot address AI's constitutive role in cognition.
  • A framework for integration, not a framework for power. Poudyal (2026) evaluates SAMR alongside TPACK and other integration frameworks and finds none address equitable power, data ownership, or accountability, motivating an alternative ecological co-agency framework.

SAMR, TPACK, and technology adoption: how they differ

Three frameworks are frequently conflated but answer distinct questions:

  • Technology adoption models (TAM, UTAUT) explain why an individual or institution accepts and continues using a technology — perceived usefulness, ease of use, and social influence.
  • TPACK describes the knowledge a teacher needs to integrate technology effectively — the interplay of technological, pedagogical, and content knowledge, extended in the AI era to AI-TPACK/GenAI-TPACK.
  • SAMR classifies how deeply a technology transforms a learning task, from enhancement to redefinition.

SAMR is best understood as an integration-depth lens used in planning and evaluation, complementing adoption theory (which explains uptake) and TPACK (which explains teacher capability). In the AI era, it is most productively used to ask whether generative AI is being applied to enhance existing tasks or to enable genuinely new ones — a distinction that runs throughout the knowledge base's assessment and curriculum research.

Implications for practice

  • Use SAMR to ask the depth question, not to prescribe transformation. Enhancement is not inherently inferior; many routine AI uses are legitimate Substitutions. The model is diagnostic, clarifying what a given use actually changes.
  • Pair it with ICAP. SAMR describes what the task becomes; ICAP describes how the learner engages. Used together they distinguish surface substitution from deep interactive co-construction.
  • Complement it with adoption and equity lenses. SAMR says nothing about why a tool is adopted or who benefits. Pair it with adoption models and equity analysis to avoid a depth label substituting for a critical evaluation.
  • Treat evidence of incremental integration as a finding, not a failure. If most AI integration clusters at Substitution/Augmentation, the design task is not to force Redefinition but to recognize that transformative use requires different task designs, not just better tools.

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