Introduction
The Successive Approximation Model (SAM) is an iterative framework for instructional design that emphasizes early prototyping, frequent feedback, and continuous improvement. Developed by Michael Allen, SAM was introduced as a practical response to the limitations of linear models like ADDIE. Instead of assuming that instructional solutions can be fully designed upfront, SAM treats learning design as a process of informed trial and refinement. It begins with quick, rough approximations of the final product and improves them through successive cycles based on stakeholder and learner feedback.
SAM has proven especially useful in environments where conditions change quickly, requirements are uncertain, or stakeholder expectations evolve. It allows teams to adapt rapidly while maintaining focus on instructional quality and learning outcomes. Though sometimes presented as a replacement for traditional models, SAM is more accurately viewed as a complementary alternative—one that retains the rigor of structured design but redistributes it across an iterative process.
What is the Successive Approximation Model?
SAM is a process model for managing instructional design through cycles of approximation. Its primary objective is to reduce risk and improve quality by building and testing early versions of a solution before committing to full-scale development. Rather than move linearly from analysis to design to development, SAM encourages teams to move quickly into prototyping, use feedback to refine ideas, and iterate toward a finished product.
This iterative strategy supports deeper collaboration, faster alignment, and earlier identification of flaws. The goal is not to shortcut the design process, but to frontload insight. By surfacing misunderstandings or design weaknesses early, SAM helps avoid late-stage rework and increases the likelihood that the final product meets both learning needs and business goals.
SAM is available in two main forms: a simplified three-phase process (SAM1) and an expanded model (SAM2) used for more complex projects. Both emphasize the same logic: design by doing, learn by testing, and improve by building better versions over time.
How does it work in practice?
In practice, SAM unfolds as a structured series of design and development cycles. The version used depends on the complexity of the project.
In SAM1, the process includes three main phases:
- Preparation Phase – The team defines goals, identifies constraints, reviews background information, and assembles key participants.
- Iterative Design Phase – Designers create low-fidelity prototypes, test ideas, and gather feedback. These early versions focus on structure, concept, and flow—not polish.
- Iterative Development Phase – The instructional product is developed in stages. Each stage is reviewed, revised, and improved before continuing.
For more complex projects, SAM2 introduces a more structured variant:
- Savvy Start – A collaborative kickoff session involving the full team: instructional designers, developers, stakeholders, and often SMEs. The group works together to brainstorm solutions, sketch prototypes, and agree on a shared direction. The goal is alignment, not documentation.
- Iterative Design – A series of short design-review cycles where prototypes are presented, critiqued, and revised. Each round brings the solution closer to its intended instructional and functional form.
- Iterative Development – As the product is built, it continues to undergo testing and refinement. Functional versions are reviewed with the same feedback loop as earlier design iterations.
Across both versions, the central principle is approximation. Designers begin with an incomplete or imperfect representation of the final solution and improve it incrementally. This makes weaknesses visible early and enables course corrections before substantial resources are committed.
When is it most useful?
SAM is particularly well-suited to projects where the anticipated learning solution is complex, novel, or not easily communicated through traditional design documentation. Its primary advantage lies in the way it delivers immediate, tangible representations of the learning experience—such as clickable prototypes or rough visual mockups—rather than abstract deliverables like outlines, scripts, or storyboards. This makes it especially valuable for instructional solutions that are difficult to envision or evaluate without seeing them in action.
When a learning intervention depends on interactivity, narrative flow, user experience design, or nuanced behavior modeling, conventional specifications often fail to convey the full learner experience. In these situations, SAM allows stakeholders to engage with something real early in the process, identify mismatches or missed opportunities, and guide improvements before substantial resources are committed.
SAM is also useful when the success of the solution hinges on deep stakeholder alignment. Early prototypes help surface assumptions and promote shared understanding in ways that static documents cannot. The model excels in situations where visual, structural, or experiential aspects of the learning design are central to its effectiveness—and where feedback on those elements must be gathered before moving into full production.
When is it not useful?
SAM faces serious practical constraints that can make it difficult to implement in many real-world contexts. One of the core challenges is logistical: SAM requires that stakeholders, SMEs, and other client-side personnel participate in concentrated working sessions—often lasting several hours—to co-create prototypes and provide iterative feedback. In theory, this ensures that instructional solutions are built collaboratively and informed by real needs. In practice, it demands a level of time, access, and sustained engagement that few clients are able or willing to provide. Over the past several decades, it has proven exceptionally rare for client teams to consistently show up for multiple, collaborative design sessions at the intensity SAM presumes.
A second limitation is expertise. SAM’s co-design approach means that instructional decisions are frequently made in real time, in collaborative workshops, with heavy input from people who may not have formal training in learning design. While stakeholder perspectives are essential, determining the most appropriate learning strategy requires deep knowledge of instructional theory, sequencing, and learner psychology. In facilitated group sessions, these factors are easily overshadowed by intuitive opinions about how people learn or enthusiastic brainstorming that leads to superficially engaging but instructionally weak ideas. This does not reflect a lack of intelligence or value on the part of client participants—it reflects a mismatch between what is being asked of them and the expertise required to make those decisions well.
As a result, SAM can introduce risks in contexts where strong instructional judgment is critical, but where the design process is distributed among non-experts with limited time. Without careful facilitation and backstopping by experienced designers, the model can produce compromised solutions under the guise of collaborative efficiency.
Theoretical Foundations
SAM is not grounded in a single theory of learning but borrows extensively from adjacent fields and design philosophies. It draws its conceptual foundation from:
- Agile methodology – Emphasizes iterative development, early feedback, and team collaboration over rigid planning.
- User-centered design – Prioritizes designing with and for the user, using prototypes and testing to ensure usability and relevance.
- Prototyping theory – Treats incomplete representations as tools for conversation, alignment, and insight—not just precursors to finished products.
- Action research – Incorporates cycles of inquiry, feedback, and revision to improve practice through evidence.
Although SAM is sometimes associated with constructivist pedagogy—particularly due to its emphasis on learner relevance and engagement—it is fundamentally a project model, not a learning theory. It can accommodate many instructional approaches, from behaviorally driven skills training to problem-based and experiential learning designs. Its contribution is not to prescribe what to teach, but to improve how instructional products are developed.
Design Considerations
Implementing SAM requires attention to both team dynamics and project constraints. Its iterative nature offers speed and flexibility but demands structured collaboration and a willingness to embrace ambiguity.
Teams using SAM should be prepared for:
- Prototyping as process – Rough versions are not failures; they are tools. Early designs should provoke discussion, reveal assumptions, and clarify expectations. High fidelity is not required early—but clear intent is.
- Time for reviews – SAM depends on rapid and regular review cycles. Teams must secure time from SMEs and stakeholders at multiple points in the process. Without this engagement, the model breaks down.
- Disciplined iteration – Iteration must serve a purpose. Each round should be anchored in clear questions: What are we testing? What decision does this feedback inform?
- Structured collaboration – Agile does not mean chaotic. Effective SAM teams establish norms around who decides what, how feedback is gathered, and when work moves forward.
- Continued instructional rigor – Speed must not come at the cost of instructional integrity. Strong design principles still apply. SAM accelerates exposure of design thinking—it does not replace the need for it.
The model assumes that instructional designers will lead the process, guiding SMEs and stakeholders through unfamiliar territory. Facilitation, communication, and design judgment are critical to its success.
Cautions and Critiques
SAM’s primary risks stem not from the model itself, but from how it is applied. Several common critiques focus on execution challenges.
First, it assumes access. Many organizations struggle to provide frequent stakeholder feedback. When reviewers are unavailable, iterations stall and decision-making slows. The resulting gaps can lead to rushed approvals or incomplete designs.
Second, SAM may feel unpredictable to sponsors. Traditional project managers often prefer defined milestones and signed deliverables. Without upfront documentation, SAM can appear unstructured—even when it is not. Successful teams mitigate this by showing early progress and framing iteration as risk reduction.
Third, iteration is sometimes mistaken for improvisation. Inexperienced teams may skip analysis or rush into development, calling it “agile.” But iteration without design rigor results in circular loops and unfocused output. SAM requires clear objectives, criteria, and disciplined inquiry to work effectively.
Finally, not every project justifies the overhead. For short-term compliance modules or static reference materials, the cost of iteration may outweigh the benefit. Designers should assess the context before applying the model.
Notable Contributors
Michael Allen, the model’s originator, is the most prominent voice behind SAM. His work emphasizes rapid, meaningful learning experiences, often using scenarios and interactivity. Allen’s books and workshops have been influential in promoting iterative design within corporate L&D and have helped position SAM as a practical evolution of the traditional instructional design toolkit.
Conclusion
The Successive Approximation Model offers a flexible, pragmatic framework for instructional design in dynamic environments. It does not abandon the goals of clarity, alignment, and effectiveness—it simply redistributes how those goals are achieved. By encouraging early prototyping, ongoing feedback, and structured iteration, SAM helps teams build better learning experiences through collaboration rather than prediction.
Its value lies not in its novelty but in its adaptability. For teams working in high-stakes, fast-moving, or ambiguous contexts, SAM provides a disciplined method for discovering what works—before it’s too late to change course. When applied well, it helps instructional designers lead with insight, deliver with confidence, and revise with purpose.