AI Integration and the SAMR Framework: A Practical Lens for Instructional Design
Authors: Yerin Seung; info@ciddl.org
As educators become more interested in using artificial intelligence (AI) tools in their K–12 classrooms, they are being asked to clarify how and why to integrate them into instruction. With new tools emerging faster than formal research or professional learning can keep pace, many teachers and teacher educators are turning to instructional frameworks to help guide decision-making. There are multiple frameworks that can offer useful lenses for thinking about AI integration. One commonly used framework is SAMR (Substitution, Augmentation, Modification, Redefinition), which categorizes how technology changes learning tasks. Although SAMR was not originally designed with AI in mind, it continues to feature prominently in discussions of AI integration due to its familiarity and accessibility. In this post, I draw on examples from two recent AI guidance documents of Maine and New Mexico to synthesize how SAMR is being applied to AI integration in practice.
Substitution: AI as Ethical Automation and Tool Replacement
At the substitution level, technology directly replaces traditional tools without changing the underlying learning task. In the context of AI integration, substitution is often associated with automation and efficiency. Common examples include using AI-powered grammar tools instead of basic spell checkers, generating lesson drafts, translating materials, or assisting with grading. At this level, AI primarily supports teachers rather than transforming student learning experiences. An important theme of substitution is ethical automation. Automating a task does not entail the removal of professional judgment. Instead, AI is positioned as a support that handles routine or time-consuming work while educators retain responsibility for instructional decisions and student learning. Substitution can be valuable, especially when it frees up time and cognitive energy. But on its own, it rarely changes how students engage with content. It is best understood as an entry point rather than a destination for AI integration.
Augmentation: Functional Improvement Through Feedback and Personalization
Augmentation occurs when technology substitutes for traditional tools while also providing functional improvements. In AI-supported instruction, this level is commonly associated with feedback, personalization, and responsiveness. Examples include AI tools that provide real-time writing feedback, adaptive problem-solving supports in math, interactive visualizations in science, or chatbots that help students revise work before submission. Teachers may also use AI to analyze student data or generate personalized learning recommendations. These uses can meaningfully improve instructional efficiency and access. Students may receive more immediate feedback, and teachers may gain insights more quickly. However, the learning task itself often remains largely the same. A key consideration at this level is that improved tools do not automatically lead to deeper learning. Augmentation enhances the learning environment, but whether it strengthens understanding depends on how the task is designed and how students are expected to use the feedback they receive.
Modification: AI-Supported Task Redesign
Modification represents a shift from enhancement to task redesign. At this level, AI enables the structuring of learning activities in ways that were not easily possible with traditional tools. Examples include AI-powered simulations, gamified learning environments, customized learning pathways, and AI-supported approaches to accessibility. A particularly notable practice at this level is asking students to analyze, critique, or improve AI-generated content, rather than simply consume it. Here, AI begins to shape how students interact with ideas, representations, and problems. Instructional design choices become central: teachers are not just deciding which tool to use, but how AI changes the nature of the task and the kind of thinking students must engage in. Modification highlights the importance of intentional design. Without clear learning goals, redesigned tasks can still remain surface-level, even if they look more complex or novel.
Redefinition: New Learning Possibilities with AI
At the redefinition level, AI enables learning tasks that would otherwise be difficult or impractical. This is where AI integration is often described as transformative. Examples include students co-creating virtual experiments, developing predictive models, designing AI-assisted solutions to real-world problems, building chatbots to explain content, or creating and refining multimedia products in collaboration with AI. These tasks tend to emphasize creativity, interdisciplinarity, and authentic problem solving. Importantly, redefinition is not about using the most advanced tools—it is about what students are able to do and think about as a result. AI functions as a collaborator or thinking partner, supporting exploration, iteration, and decision-making. At the same time, novelty alone does not guarantee meaningful learning. Redefinition is most powerful when tasks require students to reason, justify, and internalize understanding rather than simply produce outputs with AI assistance.
Implications for Teachers and Teacher Educators
Using SAMR as a lens for AI integration can be helpful, particularly for educators who are new to AI or seeking a structured framework for reflecting on instructional choices. SAMR offers a shared language and concrete examples that make AI integration feel more approachable. However, SAMR also has limitations. It focuses on how tasks change, not on how students think while performing them. Higher levels are often assumed to be better, even though learning quality depends far more on cognitive engagement, instructional goals, and opportunities for reflection. For teachers, this means effective AI integration starts with questions about learning, not tools: What kinds of thinking do I want students to engage in? How does AI support or hinder that thinking? What skills should students internalize over time? SAMR can serve as a useful planning and reflection tool, but it should be paired with frameworks that foreground cognition, learning processes, and learner variability for those with and without disabilities.
Using SAMR as a Starting Point, Not a Scorecard
SAMR remains a popular framework because it offers clarity in a rapidly changing technological landscape. When applied thoughtfully, it can help educators recognize that AI integration spans a continuum from automation to collaboration. But meaningful AI integration cannot be reduced to a category. The most important questions are not “Where does this fit in SAMR?” but “What does this ask students to think about, practice, and ultimately learn?” When SAMR is used as a starting point rather than a scorecard, it can support more intentional, reflective, and human-centered approaches to AI integration—ones that keep learning, not technology, at the center.
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