Instructional Design Models (LTD200x)

Welcome to IDT200x: Learning Modules, a course designed to deepen your understanding of how effective instructional design is structured, delivered, and evaluated. In this course, you’ll explore the building blocks of instructional modules—from defining learning objectives and selecting appropriate content, to designing engaging activities and assessments that promote measurable outcomes. Whether you’re developing training for a classroom, workplace, or online environment, this course equips you with the tools to design impactful learning experiences, one module at a time.

Design Models Topics

Introduction, History, Ethics, Accessibility, & AI

ADDIE Design Model

Dick & Carey Design Model

Understanding by Design (UbD) Model

Rapid Instructional Design

Successive Approximation Model (SAM)

Learning Objectives, Bloom's Taxonomy, & SMEs

Sequencing, Assessment, & Alignment

Click on above titles for additional information

Introduction, History, Ethics, Accessibility, & Artificial Intelligence

Artificial Intelligence (AI) transforms instructional design by automating tasks, personalizing learning, and streamlining decision-making. Tools like ChatGPT, intelligent tutoring systems, and adaptive learning platforms provide instructional designers with technologies that enhance course development and the learning experience. AI is crucial for creating engaging educational journeys using machine learning algorithms, natural language processing, and generative tools. These resources help learning designers as co-designers to improve content creation and support positive learning outcomes. Notable AI-driven content tools include ChatGPT (OpenAI), Articulate 360 AI, and Canva Magic Studio.

Key Concepts

Utilizing Artificial Intelligence (AI) in Instructor Design

Content Generation and Curation

AI supports you in creating engaging learning objectives, developing microlearning content, crafting quizzes, and designing lesson plans. It tailors resources based on your preferences and goals, helping reduce your development time by 40- 60%!

Learner Persona Development

Analyzing demographics and skills creates learner personas that represent our audience. These personas guide targeted content and strategies. I use AI to align five supply chain personas with their work processes, enhancing content relevance. I developed these personas for my course using ChatGPT and Articulate prompts.

AI platforms adjust content difficulty, pacing, and sequence in real-time, creating personalized pathways aligned with learners' performance data. For instance, ATD Mini Courses offer adaptive courses that redirect as needed, ensuring optimal learning outcomes for all.

Adaptive Learning Systems

a man riding a skateboard down a street next to tall buildings

Assessment and Feedback

Instructional Design Automation

With AI, creating quizzes has never been easier! It also allows us to explore open-ended responses and provide immediate feedback. Thanks to AI-driven learning analytics, we can identify knowledge gaps and recommend helpful interventions to support everyone's learning journey.

Images Source: created by ChatGPT from OpenAI

Best Practices

Utilizing Artificial Intelligence (AI) in Instructor Design

Embrace the power of AI

Enhance your work with AI! Use it for drafting, brainstorming, or quiz creation, but always have an expert review the final product for the best outcomes.

Foster Inclusivity through Persona Modeling

Embrace AI to effectively simulate diverse learner backgrounds, enabling us to thoughtfully plan for accessibility and cultural responsiveness.

Begin with a thoughtful approach

Ensure that AI implementation is aligned with the learning goals and adheres to established instructional models, such as ADDIE or SAM.

Review our work for accuracy and potential bias

Confirm all AI outputs with Subject Matter Experts (SMEs) to ensure factual accuracy and ethical tone.

Review our work for accuracy and potential bias

Share how and when AI has contributed to the creation of the course, particularly in relation to assessments or the personalized feedback you receive.

Image created by ChatCPT from OpenAI

Risk and Considerations

Bias in Outputs

AI can reflect and amplify societal biases found in its training data, including those related to gender, race, or language proficiency.

Relying too much on automation

Can lead designers to depend on AI suggestions, which may result in generic or superficial content.

Accuracy and Misinformation

AI can generate incorrect or misleading information without grasping the context or nuances.

Accuracy and Misinformation

Storing and analyzing learner data with AI systems raises compliance risks associated with GDPR, FERPA, and HIPAA.

Intellectual Property Ambiguity

The ownership of AI-generated content can be unclear, especially when proprietary tools are involved.

Additional Resources

DHL – AI for Predictive Logistics and Supply Chain Optimization (Case Study - AI-powered predictive analytics)
DHL Supply Chain
Unilever – AI in Demand Forecasting and Inventory Planning (Case Study), Unilever Supply Chain
AI-Focused Webinar: “Talking Supply Chain: Putting Gen AI into Action, Tomorrow Raftery.
Lentz, M. (2025, February), Partner with AI for instructional design (Issue 2502). TD at Work, Association for Talent Development.

Gilmore, D., Nottingham, A., & Zerwes, M. (2023, February 10). ChatGPT and learning design: What online content creation opportunities does it offer? Times Higher Education. https://www.timeshighereducation.com

Rouhiainen, L. (2019, October 14). How AI and data could personalize higher education. Harvard Business Review. https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education

Tipping Point Media. (2024, March 21). The role of AI in instructional design and learning experience [LinkedIn article]. https://www.linkedin.com/company/tippingpointmedia

How does AI affect how we approach Instructional Design?

AI is really changing the game for instructional designers (IDs), especially when it comes to understanding learners and creating effective formative assessments. Let’s take a look at some important highlights:

Instructional Design (ID) is becoming more exciting, driven by data, and centered around real-time applications that really make a difference!

The role of the ID is evolving from just being a creator to becoming an orchestrator. This means they are now setting up vibrant ecosystems that allow AI to personalize, adapt, and optimize learning experiences in exciting ways!

IDs must also serve as ethical guardians, data interpreters, and adaptive framework designers, ensuring that AI enhances learning without compromising pedagogical integrity or inclusivity.

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ADDIE Design Model

The ADDIE Model is a systematic five-phase framework utilized in instructional design to create effective and efficient learning experiences. Each phase builds on the previous one, ensuring clarity, alignment, and relevance.

Analysis: This phase identifies the learning problem, audience, environment, and performance gaps. Activities include audience analysis, needs assessment, and learning objective setting.

Design: Designers outline strategies to achieve learning objectives, selecting instructional methods, assessment tools, and media formats, and developing prototypes.

Development: Content and materials, including eLearning modules, assessments, facilitator guides, and assets, are created per the design plan.

Implementation: The course is delivered to learners, handling logistics, instructor training, technology setup, and accessibility of materials.

Evaluation: Effectiveness is assessed through formative and summative assessments. Feedback refines the course and confirms that learning outcomes are achieved.

Image Source: ChatGPT from OpenAI

Learner-Centered: It begins with learner needs and concludes with measurable outcomes.

Goal-Oriented: Learning objectives direct the design and development process.

Structured and systematic: Its clear phases promote consistency and decrease design errors.

Flexible and Iterative: While traditionally linear, modern usage embraces iteration, allowing for the revisiting of earlier phases as new data emerges, especially in digital learning environments.

Data-Driven: It utilizes feedback and performance metrics to foster continuous improvement.

This model promotes alignment among goals, content, delivery, and assessment, leading to high-impact learning that fosters performance change.

Real-World Supply Chain Example

Write your text A mid-sized consumer packaged goods (CPG) company is struggling with inaccurate forecasts and excess inventory in regional warehouses. The VP of Supply Chain partners with L&D to create a short course that upskills demand planners in AI-driven forecasting and inventory optimization. The team applies the ADDIE Model:

Analyze: Interview planners and audit ERP data to identify knowledge gaps in AI, demand variability, and lead-time buffers.

Design: Map out modules covering predictive analytics, machine learning basics, and application of AI tools like Llamasoft or Netstock.

Develop: Use real SKU-level forecast data in simulations and build interactive decision points that compare traditional vs. AI-augmented forecasts.

Implement: Launch via LMS with a 3-week blended rollout including facilitator-led debriefs and follow-up coaching.

Evaluate: Monitor post-training improvements in forecast accuracy, and capture learner feedback to iterate on modules.

Result: After course completion, forecast bias is reduced by 12%, and safety stock levels are adjusted more confidently using AI-generated insights—unlocking $750K in working capital....

Source: McKinsey & Company, “The state of AI in 2023: Generative AI’s breakout year”[Published: December 2023], https://www.mckinsey.com

Systematic structure for instructional design
Flexible and adaptable across contexts
Aligns objectives, content, and assessments
Encourages iterative improvement
Learner-centered approach
Supports robust evaluation strategies
Widely recognized and used

Can be time-consuming in large-scale projects
May be overly rigid if used linearly
Limited guidance on integrating modern technologies
Depends heavily on accurate initial analysis
Evaluation phase can be resource intensive
May not support rapid or agile development cycles
Assumes a one-size-fits-all framework without adaptatio

Strengths and Limitations of the ADDIE Model

Strengths and Limitations of the ADDIE Model for my Mini Course -

Utilizing Artificial Intelligence for Data-Driven Decision-Making in Supply Chain Operations

How ADDIE Supports This Design:

Analysis: The model helps identify the real-world problem—excess inventory tying up capital—and the audience’s prior knowledge of inventory basics.
- Example: I can assess learners’ current use of safety stock buffers or economic order quantities to tailor course difficulty.
Design: Ensures alignment between supply chain KPIs (like inventory turns, fill rate) and course outcomes.
- Example: The course can incorporate interactive simulations where learners adjust reorder points and see the impact on working capital.
Development: Encourages production of engaging, modular eLearning—matching my development workflow using ChatGPT + Articulate 360.
- Example: Quizzes, short videos, and scenario-based exercises aligned with each learning objective can be easily developed.
Implementation: Supports launching the minicourse via LMS with a structured rollout and usability testing.
- Example: I can test accessibility and functionality across mobile and desktop to ensure all users have a smooth experience.
Evaluation: Built-in feedback mechanisms ensure I can track learner performance and iteratively improve.
- Example: Using pre/post assessments and surveys to determine knowledge gains and confidence in applying inventory optimization techniques.

Limitations and Challenges:

Time-Intensive: Following every ADDIE phase thoroughly may slow down development for fast-turnaround projects.
Tech Adaptation Needs: ADDIE does not inherently account for modern digital learning tools or rapid prototyping methods like Agile or SAM .
Resource Constraints: Smaller instructional teams (e.g., freelance or solo ID professionals) may struggle with the workload of full-scale evaluation or multiple iterations.

In conclusion, ADDIE provides a strong foundational structure for my minicourse. It ensures alignment with learning goals and supports quality outcomes. However, applying it iteratively and flexibly—especially with rapid content authoring and digital feedback tools—is key to overcoming its traditional limitations and ensuring a smooth, scalable development process.

Additional Resources

TD at Work Guide – Design Thinking Meets ADDIE
Sugrue, B. (2022). Design thinking meets ADDIE: Templates and tools. TD at Work. Association for Talent Development.
TD Magazine Article – Integrating ADDIE with Digital Learning
Salas, A. (2018, November). Integrating ADDIE with digital learning. TD Magazine, 72(11), 57–64.
Retrieved from https://www.td.org
An Introduction to the ADDIE Model for Instructional Designers – Articulate Community
Community Team. (n.d.). An introduction to the ADDIE model for instructional designers. E-Learning Heroes.
Retrieved from https://community.articulate.com
Foundations of Instructional Design – ADDIE (Implied title)
edX / University System of Maryland. (2025). Foundations of instructional design: ADDIE model overview.
Course material from Instructional Design Models, USMx LDT200x, edX.
Retrieved from https://learning.edx.org

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Dick and Cary Design Model

Overview of the Dick and Carey Model

The Dick and Carey Systems Approach Model is a thoughtful and structured way to design instruction. This model is both systematic and flexible, helping us ensure that every piece—like goals, learners, content, assessments, and delivery—works together harmoniously. It encourages us to see instruction as a cohesive system, where everything influences each other. Let's explore a summary of each step in simple, everyday language:

Identify Instructional Goals - Take a moment to think about what you want your learners to achieve after the instruction. This will become your guiding principle for every design decision.

Break Down Instructional Analysis - Let's split that big goal into smaller, manageable tasks and subskills so we can achieve mastery together!

Let's take a closer look at our learners and their contexts! Understanding their current knowledge, attitudes, preferences, and the environment in which they are learning is essential.

When writing Performance Objectives, consider it a way to turn your goals into clear and measurable learning outcomes. These should specify the desired behavior, the conditions under which it will occur, and the performance criteria that will guide success.

Develop Assessment Instruments - Design assessments that align with objectives and help gauge how well learners are meeting their performance expectations.

Create a thoughtful instructional strategy by choosing the right instructional methods, delivery modes, and engagement tactics that are perfectly suited to your learners' unique needs and the type of content you're working with.

Select or Develop Instructional Materials—Gather or create exciting content, such as videos, case studies, or simulations, that perfectly aligns with our strategy and performance goals.

Design and conduct formative evaluations by testing early versions with learners, gathering valuable feedback, and joyfully identifying areas for improvement together.

Revise the instruction to make it even more effective by incorporating feedback from formative evaluation data. This way, we can ensure it aligns closely with our objectives and supports everyone's learning journey!

Conduct a comprehensive evaluation to assess the instructional strategy post-implementation, to measure its effectiveness and identify areas for potential enhancement in the future. text here...

Implications for Instructional (Learning) Design

The model enforces a front-loaded, analysis-heavy process that minimizes guesswork and ensures that each component is data-driven. It is beneficial for complex, performance-based instruction, such as in supply chain training, because it:

Supports alignment of content, assessments, and objectives.

Encourages iterative refinement through formative evaluation.

Ensures the learning experience is contextualized to real-world performance needs.

Offers a roadmap ideal for structured eLearning development, such as your Articulate 360-based workflow.

Strengths and Limitations in the Context of Your Mini-Course

Mini-Course Title: Using AI to obtain Supply Chain Operational Excellence

Course Type: Interactive eLearning Mini-Course

Modality: Self-paced, online, asynchronous

Features: Scenario-based learning, practical exercises, interactive case studies, knowledge checks/quizzes, and reflective prompts (Take-a-Ways for implementation).

Strengths in Support of Your Course Design:

Alignment Focus: Ensure that AI content, such as predictive analytics and machine learning for logistics, connects seamlessly with real-world learning outcomes. For example, consider how to “evaluate AI tools for inventory optimization.”

Learner-centered: It encourages a thorough analysis of learners, allowing for tailored experiences catering to mid-level supply chain professionals regardless of digital fluency.

Assessment Integration: Encourages the development of engaging, performance-based assessments such as dashboards, case evaluations, or simulations that closely align with our objectives.

Structured Design: Complements the current design workflow with ChatGPT to Articulate 360, bringing clarity and consistency from the very start of your ideas to the final implementation.

Limitations and Considerations:

Complexity and Time Investment: The 10-step model is methodical and can be burdensome for smaller teams or needs with fast turnaround.

Limited Agility: The model does not prioritize rapid prototyping or modular content updates, making it less suitable for frequent revisions.

Not Optimized for Quick Iteration: Given my intention to update the mini-course once or twice a year to reflect evolving AI use cases, the Dick and Carey model may not be the most efficient choice. While it provides intense upfront rigor, each cycle of revision (especially beyond step 8: formative evaluation) can be resource- and time-intensive.

Potential Adaptation of Dick and Cary Model

Potential adaptation if more time allows and a more detailed analysis is desired: - for example, if a client wants greater input and more control over the development.

Utilize the Dick and Carey model for initial instructional architecture and core course design. Then:

For eLearning: Organize content into modular blocks that can be easily swapped or updated independently each cycle (e.g., quarterly AI tool spotlight).
For Instructor-Led Training: Maintain a base instructor guide and use supplemental update inserts for emerging topics or revised case studies.

Complement with a lightweight, agile revision method (e.g., SAM) to streamline updates without full model reapplication. More details will be provided in one of the upcoming weekly sections.

Continuation of my comments ... An Interesting Alternative from previous section

There are several common reasons for considering a switch between Dick, Carey, and SAM. Here’s a brief list of examples that I would foresee:

When early analysis uncovers significant unknowns or uncertainties, testing them quickly is essential.
It can be challenging when stakeholders or SMEs share last-minute updates to content or tools that impact learning objectives.
When pilot modules or test runs show that learners are feeling confused, losing interest, or not reaching their goals, it's essential to take notice and make adjustments.
When project timelines get a bit tight, they call for quicker action than what Dick and Carey’s complete linear cycle allows us to take.
When audience analysis uncovers essential differences, it’s wonderful to create tailored content variations that resonate with each group.

Boucher, C. (2022, August 3). The Dick and Carey System Approach Model of Instructional Design. HowToo Blog. https://www.howtoo.co
Pappas, C. (2024, February 28). An in-depth analysis of the Dick and Carey model. eLearning Industry. https://elearningindustry.com
Kurt, S. (2025, November 23). Dick and Carey instructional model. Educational Technology. https://educationaltechnology.net
Digital Learning Institute. (n.d.). What are the core instructional design models? Digital Learning Institute Blog. https://digitallearninginstitute.com
Pappas, C. (2023, November 20). 9 steps to apply the Dick and Carey model in eLearning. eLearning Industry. https://elearningindustry.com

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Understanding by Design Model (UbD)

Overview of the Understanding by Design (UbD) Model

Understanding by Design (UbD), created by Wiggins and McTighe, offers a thoughtful approach to curriculum development by starting with the desired results. Rather than merely emphasizing content at the outset, UbD encourages educators to clarify the learning outcomes they wish to achieve. Next, they identify how to measure that learning and develop engaging instructional activities that align with those goals. This effective, three-stage process is often called “backward design.”

Stage 1: Identify Desired Results—We aim to emphasize the key concepts and memorable insights our learners will hold onto long after the course concludes. For my minicourse, one important takeaway is: “AI technologies, when strategically implemented, can significantly improve forecasting accuracy, inventory optimization, and operational efficiency in supply chains.”

Stage 2: Determine Acceptable Evidence - Next, explore how learners will showcase their understanding. This could involve authentic assessments, such as creating an AI-driven forecasting improvement plan or thoughtfully critiquing an AI implementation case study with a GRASPS-aligned task.

Stage 3: Planning Learning Experiences and Instruction - Once we have clearly defined our outcomes and assessments, we can plan our instruction. In my minicourse, I'm excited to include scenario-based learning featuring engaging AI use cases, fun short explainer videos, and interactive decision-making simulations that keep everyone involved and learning together!

Implications of UbD for Instructional Design

UbD shifts the instructional designer’s mindset from simply delivering content to ensuring transferable learning. This model fosters a deeper alignment between course goals, assessments, and learning activities in instructional design, especially for self-paced online minicourses like mine.

For global, mid-level supply chain professionals, the backward design model ensures that learners do not simply memorize AI terms—they apply them. For example, instead of merely knowing what a machine learning algorithm is, learners will use it to evaluate supplier risk or optimize safety stock. This emphasis on application is crucial in a short eLearning course where engagement and relevance must be immediate.

Additionally, UbD encourages the use of essential questions that provoke engaging thought and inspire inquiry. In my course, one essential question is: “How can AI-driven insights transform day-to-day supply chain decisions?” These thought-provoking questions effectively anchor learners in real-world relevance, which is a crucial factor for meaningful adult learning.

Evaluation of Strengths and Limitations for My Minicourse

Strengths:

The Understanding by Design (UbD) model offers significant support in designing my minicourse, Using AI to Obtain Supply Chain Operational Excellence, particularly in its emphasis on aligning learning outcomes with real-world applications and business impact. The backward design process ensures that each module builds toward transferable skills, such as evaluating AI technologies or developing a business case for ROI, directly relevant to mid-level supply chain professionals. Focusing on enduring understandings and authentic assessments also encourages scenario-based learning that mirrors these professionals' daily decision-making challenges.

Clarity and Focus: Beginning with our key understandings, I ensure that each module and activity connects beautifully with the central theme—using AI to achieve operational excellence.

Authentic Learning: The GRASPS framework simplifies the creation of engaging and realistic performance tasks. For instance, learners might have the exciting opportunity to step into the shoes of supply chain analysts as they recommend AI tools to help reduce excess inventory.

Transferability: Learners are encouraged to apply their knowledge across various supply chain functions, including demand planning, logistics, and procurement.

Evaluation of Strengths and Limitations for My Minicourse (Continuation)

Limitations and Challenges:

The model also presents specific challenges. The level of planning required to fully align transfer goals, essential questions, assessments, and activities can be time-intensive, especially for a short-format, self-paced eLearning course. Additionally, UbD is conceptually strong but less prescriptive regarding delivery strategies, requiring the integration of other frameworks (e.g., Merrill’s or Gagné’s) to support digital engagement, interactivity, and learner motivation. Despite these limitations, UbD provides a solid foundation for ensuring the learning experience is outcome-focused, strategically relevant, and instructionally coherent.

Thoughtful Design Takes Time: The careful planning involved in Understanding by Design (UbD) can be quite resource-intensive. Significant effort is needed to align transfer goals with competencies and ensure that each quiz, activity, and case study is well-coordinated, particularly in microlearning formats.

Limited Strategy Guidance: While UbD is strong in structure, it offers less guidance on the “how” of instruction. For eLearning, I still rely on other frameworks (like Merrill’s Principles or Gagné’s Nine Events) to create engaging and interactive content.

Assessment Complexity: Developing valid, performance-based assessments that evaluate AI applications in supply chains requires both domain knowledge and creativity. Basic quizzes may be inadequate.

McTighe, J. (2023).
UbD in a Nutshell.
Jay McTighe & Associates Educational Consulting.
Retrieved from: https://jaymctighe.com/resources/ubd-in-a-nutshell/
Edutopia. (2022).
Backward Design: The Basics.
Retrieved from: https://www.edutopia.org/blog/backward-design-basics-grant-wiggins
Vanderbilt University Center for Teaching.
Understanding by Design Template and Guide.
Retrieved from: https://cft.vanderbilt.edu/guides-sub-pages/understanding-by-design/

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Rapid Instructional Design

Overview of the Rapid Instructional Design Approach

Rapid Instructional Design (RID), or Rapid eLearning or Rapid Prototyping, is an agile approach to creating instructional content that prioritizes speed, efficiency, and responsiveness over extensive front-end planning. Unlike traditional linear models like ADDIE, RID emphasizes quick iterations, streamlined collaboration with SMEs, and rapid authoring tools such as Articulate 360.

Typical stages of RID include:

Needs Analysis: Quickly identify core learning goals and performance gaps, often based on the client's or learners' prior knowledge.

Design and Planning: Create a high-level structure that includes essential content flow, an assessment strategy, and a delivery format.

Content Development: Quick creation and repurposing of content using authoring tools.

Prototyping and Testing: Initial development of modules to collect feedback and enhance.

Deployment and Iteration: Rapid implementation of content with continuous feedback loops.

Evaluation and Maintenance: Ongoing enhancement through learner feedback and performance data

Implications of RID for Instructional (Learning) Design

Faster Time-to-Delivery: Great for on-demand or “just-in-time” learning scenarios.

Agile Design Process: Supports quick adaptation to changing organizational needs.

Lean Development: Focuses on core content without unnecessary embellishment.

Collaborative Workflow: Requires continuous input from SMEs and stakeholders.

Tool-Driven Efficiency: Relies heavily on tools like Rise 360 for modular, mobile-friendly design.

This approach aligns with modern organizational learning needs but may compromise depth and interactivity if not carefully balanced.

Recap of Instructional Design Methods

Strengths and Limitations for Your Minicourse

Strengths:

Speed and Flexibility: Ideal for addressing the rapid evolution of AI tools in supply chain contexts.

Cost-Effectiveness: Efficient for your 3–4 hour course model using existing AI case templates and graphics.

Just-in-Time Learning: Supports fast rollout of practical techniques and real-time tools used in optimization.

Limitations:

Reduced Interactivity: Might limit deeper engagement with complex AI algorithms or simulation-based learning.

Assessment Depth: Risk of relying on simple quizzes over performance-based evaluations.

Over-reliance on templates: This could lead to a generic learner experience unless supplemented with authentic supply chain scenarios.

Support or Challenge for Designing Your Minicourse

Support:

Modularization fits your 3–6 module design, enabling faster development and quicker learner onboarding.

Prototyping helps test AI use-case modules early, gather feedback from mid-level supply chain professionals, and refine accordingly.

The reuse of Existing AI Content supports scalability across multiple client industries.

Challenges:

Creating interactive simulations of AI decision-making in supply chains may exceed the time constraints of RID.

Ensuring scenario realism and engagement for a global, mid-level professional audience within tight development cycles can be difficult....

Kyle Pearce. “Learning by Doing: 7 Principles of Rapid Instructional Design.” Social Creators, March 2025.
YingYu Chen. ”#12 Learning Design: Rapid Instructional Design.” Medium, April 2024.
https://medium.com/@yingyuchen/12-learning-design-rapid-instructional-design
Instructional Design Blog. “What Am I Supposed to Do with All of This Information?” The Rapid E-Learning Blog, (n.d.).
https://blogs.articulate.com/rapid-elearning/what-am-i-supposed-to-do-with-all-of-this-information/

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Successive Approximation Model (SAM)

Overview of SAM: Description of the Three Phases

The Successive Approximation Model (SAM) is a highly agile and iterative instructional design approach skillfully developed by Dr. Michael Allen. Unlike traditional linear models like ADDIE, it promotes collaboration, encourages prototyping, and emphasizes frequent feedback. This way, learning experiences are created, genuinely adapted, and refined throughout the design and development journey.

Preparation Phase:

This represents the core of the project, during which I collaborate with stakeholders and subject matter experts (SMEs) to elucidate our project objectives, comprehend any limitations, and gain insights into the profiles of our esteemed learners.
I would engage in various activities, such as conducting a thorough needs assessment, defining clear learning objectives, and performing insightful background research. The result will be a thoughtfully crafted design blueprint with prioritized learning outcomes that truly reflect our goals.
Example (minicourse): In my AI-in-supply-chain minicourse, this phase is all about connecting with supply chain leaders to uncover their most pressing challenges, such as AI for demand sensing and transportation route optimization. Together, we can explore these exciting opportunities!

Overview of SAM: Description of the Three Phases (Continuation)

Iterative Design Phase:

I would develop a rough prototype (often called the “Alpha”) that includes initial content outlines, wireframes, and instructional strategies.
A “Savvy Start” session gathers the design team (i.e. Me) and key stakeholders to brainstorm creative solutions, establish expectations, and explore ideas.
Feedback from SMEs and learners is collected to enhance structure and interactivity.
I would create a mock scenario where a supply chain planner uses ChatGPT to analyze historical order data and then gathers feedback on whether learners found the scenario realistic and useful.

Iterative Development Phase:

Based on Alpha feedback, the content is progressively refined through the Beta and Gold stages.
Multimedia, assessments, and user interactions are incorporated and evaluated.
Learner usability testing is performed, and enhancements are implemented until the final product is ready for launch.
Example: I would improve the beta version of a warehouse case study with narrated AI tool walkthroughs, based on feedback regarding clarity and flow.

Implications of SAM for Instructional Design

SAM changes how instructional designers think about and approach course development. It emphasizes:

Agility over rigidity: Designers are not confined to a fixed sequence. Design decisions can be modified and retested as understanding evolves.
Stakeholder engagement from day one: Through Savvy Start and iterative reviews, SMEs and learners shape the experience, not merely validate it at the end.
Performance-centered learning: The model facilitates the swift evaluation of learning interventions to ensure they promote the desired behaviors and outcomes.
Risk reduction: Catching errors or misalignments early in prototypes minimizes wasted development effort.

This indicates a more collaborative, responsive, and learner-focused process in which content evolves alongside a deeper understanding of learners’ needs and performance goals.

Write your text, Strengths and Limitations of SAM (Applied to My Minicourse)

Strengths:

Iterative testing ensures relevance: By developing and testing scenarios early, I can create content that resonates with the daily challenges faced by supply chain professionals.
Learner-centered: Frequent feedback loops provide learners the opportunity to influence what’s created, enhancing engagement and effectiveness.
Supports innovation: The model is well-suited for integrating rapidly evolving tools like AI, which may require frequent updates of examples.
Aligns with modern tech tools: SAM enhances eLearning tools such as Articulate Rise and Storyline, which are designed for rapid prototyping and agile development.

Limitations:

Resource-intensive: It requires continuous engagement with SMEs and iterative builds, which may pose challenges for individual instructional designers or small teams.
Scope Creep: If feedback cycles are not carefully managed or prioritized, iterative revisions can expand the project scope.
Steeper Learning Curve: For individuals new to agile ID or lacking experience with prototyping tools, SAM may initially seem overwhelming.

How SAM Supports or Challenges My Specific Learning Experience

Supports:

SAM perfectly aligns with my ambition to create a scenario-rich minicourse that invites learners to explore practical AI applications in various supply chain functions, like demand forecasting, routing, and inventory optimization.
The Savvy Start allows me to team up with talented SMEs who truly grasp the subtle differences between practical and theoretical AI tools, making our content even more impactful!
The prototype-first mindset empowers me to create sample modules, such as an AI-powered forecasting dashboard, test them quickly with learners, and fine-tune them based on usability, effectiveness, and engagement.

Challenges:

Handling several review cycles can be a bit tricky because the availability of our subject matter experts (SMEs) can be limited.
As the topic continues to evolve quickly (AI capabilities shift monthly), it can be a bit tough to keep the content steady during the iterative development process.
I might consider scaling down the complete SAM approach and opting for a more streamlined model to better fit my time and solo development needs.

Here’s how I would apply SAM in my minicourse, “Using AI to Drive Operational Excellence in the Supply Chain”:

Preparation: I hold a stakeholder meeting with a supply chain director and a data analyst to identify the most significant AI pain points in forecasting and logistics. I also survey mid-level managers to identify current tools they use (or misunderstand).

Iterative Design: I built a rough prototype of a decision-making activity in which a learner chooses between AI tools for a warehouse routing problem. I conducted a feedback session with a beta group and made changes based on confusion with terminology and a lack of context.

Iterative Development: I develop polished multimedia versions of my scenarios using Articulate Rise, record voiceovers with AI narration, and refine based on beta feedback about pacing and interactivity. I also build quizzes to reinforce decision-making and test logic and understanding.

Rimmer, T. (2016). An introduction to SAM for instructional designers. Articulate E-Learning Heroes. https://community.articulate.com
Boucher, C. (2022, July). The complete guide to the successive approximation model (SAM) of instructional design. HowToo Blog. https://www.howtoo.co
Bean, C. (2014). The accidental instructional designer: Learning design for the digital age. ASTD Press.

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Learning Objectives, Bloom's Taxonomy, & SMEs

Examples of Course Learning Outcomes (CLOs):

Learners will assess internal and external supply chain risks and design targeted mitigation strategies to improve operational resilience across sourcing, manufacturing, and distribution processes.

Learners will design and present a comprehensive digital marketing plan for a product launch, including audience segmentation, campaign channels, KPIs, and success measurement criteria.

Examples of Learning Objectives:

By the end of the module, learners will analyze and prioritize three significant sources of supply chain disruption based on their impact on operational performance.
Given a product scenario, learners will design and justify a customized digital marketing funnel to optimize lead conversion.

Note: The above Learning Objectives are “stretch versions” of my original objectives, but they have been changed since my learners are “Mid-Level” Supply Chain Professionals.

(“Identify” → “Analyze and Prioritize”: From basic recognition to deeper evaluation and judgment, requiring critical thinking.)

(“Outline” → “Design and Justify”: Moves from simply describing existing structures to creating a new structure and defending their choices, which fits the “Create” and “Evaluate” levels of Bloom’s.)

Overview of Bloom’s Taxonomy

Bloom’s Taxonomy is a framework for categorizing learning objectives into levels of cognitive complexity, initially developed by Benjamin Bloom and later revised by Anderson and Krathwohl. It is widely used in instructional design to ensure a progression from foundational knowledge to higher-order thinking.

The Six Levels of Bloom’s Taxonomy - Instructional Designers use this taxonomy to scaffold instruction from fundamental to complex learning, align assessments, and select appropriate action verbs when writing objectives

Remember – Recall facts and basic concepts.

Example verbs: define, list, name, identify

Understand – Explain ideas or concepts in your own words.

Example verbs: describe, explain, summarize, interpret

Apply – Use information in new situations.

Example verbs: demonstrate, implement, solve, use

Analyze – Break information into parts to explore relationships.

Example verbs: compare, contrast, differentiate, examine

Evaluate – Justify a decision or course of action.

Example verbs: assess, critique, judge, support

Create – Produce new or original work.

Example verbs: design, construct, formulate, compose

Shabatura, J. (2022).
Using Bloom’s Taxonomy to Write Effective Learning Objectives.
Teaching Innovation & Pedagogical Support, University of Arkansas.
Retrieved from: https://tips.uark.edu/using-blooms-taxonomy/
Rimmer, T. (2021).
How to Write Measurable Learning Objectives.
Articulate E-Learning Heroes.
Retrieved from: https://community.articulate.com/articles/how-to-write-measurable-learning-objectives
Armstrong, P. (2023).
Bloom’s Taxonomy.
Vanderbilt University Center for Teaching.
Retrieved from: https://cft.vanderbilt.edu/guides-sub-pages/blooms-taxonomy/
Association for Talent Development (ATD). (n.d.).
Writing Performance-Based Learning Objectives [Micro Course].
Retrieved from https://www.td.org/education-courses/writing-performance-based-learning-objectives
Hofmann, J. (2013, May).
Applying Bloom’s Taxonomy to Learning Technology [Blog post].
Association for Talent Development.
Retrieved from https://www.td.org/content/atd-blog/applying-blooms-taxonomy-to-learning-technologies

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Sequencing, Assessment, & Alignment

Sequencing

Definition: Sequencing is all about arranging content, skills, and learning activities in a logical way that helps everyone learn and master new concepts step by step.

Key Concepts:

Start with the basics and gradually move on to more advanced applications. This way, you'll build a solid understanding before diving into the complexities!
It's important to make sure that learners have the necessary prior knowledge before we dive into new material. This helps set them up for success!
Chronological or procedural order: Whenever it makes sense, try to arrange the content in the order it would naturally occur in real life.
Spiral sequencing: Let's take a moment to review key concepts together, gradually exploring them at more complex levels for a deeper understanding and reinforcement!

Purpose:

Improves learner comprehension
Supports skill scaffolding
Increases knowledge retention

Assessments

Definition: Assessments are tools or methods used to evaluate learners’ understanding, skills, or performance relative to course objectives.

Types:

Formative assessments are carried out during instruction, such as quizzes, knowledge checks, and polls. They serve a wonderful purpose by providing helpful feedback and guiding learning along the way.
Summative assessments play a crucial role at the end of a unit or course, like final exams and capstone projects, as they help us understand how well knowledge and skills have been mastered.
Diagnostic assessments are a wonderful way to gauge what students already know before instruction begins. They help us understand prior knowledge and tailor our teaching to better meet individual needs!
Authentic assessments invite learners to engage with knowledge in real-world or simulated scenarios, such as case studies and role plays. This approach not only makes learning more relatable but also encourages practical application and deeper understanding!

Purpose:

Measures learning outcomes
Identifies knowledge gaps

Alignment

Definition: Alignment connects learning objectives, instructional content, learning activities, and assessments in a way that supports one another and helps everyone grow.

Dimensions of Alignment:

Learning Objectives ↔ Content: It’s super important that the content connects nicely with our objectives. This way, everything feels cohesive and purposeful!
Learning Objectives ↔ Activities: Activities should provide learners with the practice necessary to meet the objectives.
Learning Objectives ↔ Assessments: It’s vital that assessments truly reflect what we expect learners to accomplish based on the objectives.

Purpose: