Automating Instructional Design

From Learning Objectives to Interactive MicroSimulations

Course Duration: 12 Modules (Self-Paced or 12-Week Instructor-Led)
Level: Intermediate
Prerequisites: Basic computer literacy; no programming experience required

Course Overview

This course teaches educators and training professionals how to leverage AI-assisted tools to transform learning objectives into interactive educational simulations called MicroSims. Participants will master the complete lifecycle of MicroSim development—from analyzing pedagogical goals through deployment and learner assessment—using Claude Code skills and a comprehensive library of visualization tools.

The central challenge of instructional design automation is bridging the semantic gap between abstract learning outcomes and concrete interactive experiences. A well-crafted learning objective like "Students will understand the relationship between supply and demand" contains implicit assumptions about visualization, interaction, pacing, and assessment that must be made explicit before any simulation can be built. This course provides systematic methods for that translation.

Target Audience

This course is designed for working professionals who create educational content:

• K-12 Teachers seeking to supplement curriculum with interactive demonstrations
• Corporate Training Specialists developing employee onboarding and skill development programs
• Higher Education Faculty creating simulations for undergraduate and graduate courses
• Instructional Designers at educational technology companies
• Curriculum Developers building standards-aligned educational materials
• Subject Matter Experts who want to make their knowledge more accessible

No programming background is required. The course teaches participants to work with AI tools that generate code, focusing on specification, evaluation, and iteration rather than implementation details.

Learning Objectives

Upon completing this course, participants will be able to:

1. Analyze learning objectives to identify simulation-ready components and prerequisite concepts
2. Select appropriate visualization paradigms (animation, network, timeline, chart, map) based on concept characteristics
3. Write detailed MicroSim specifications that preserve pedagogical intent through AI generation
4. Evaluate generated simulations against educational effectiveness criteria
5. Adapt MicroSim complexity for audiences ranging from kindergarten to graduate school
6. Test simulations with target learners and interpret feedback systematically
7. Iterate on designs using structured refinement protocols
8. Build a personal library of reusable MicroSim patterns and templates

The MicroSim Design Challenge

Why This Is Hard

Converting a learning objective to a working simulation requires decisions at multiple levels:

Level	Question	Example
Conceptual	What is the core insight?	Gravity accelerates all objects equally
Visual	How should it look?	Two balls of different sizes falling side by side
Interactive	What can the learner control?	Drop height, ball mass, air resistance toggle
Temporal	How does it unfold?	Real-time vs. slow motion vs. step-by-step
Feedback	How does the learner know they understand?	Prediction prompt before reveal
Scaffolding	What support is needed?	Labels, hints, worked examples

Each decision affects learning outcomes. A simulation that is technically accurate but visually overwhelming fails its pedagogical purpose. A simulation that is beautifully designed but doesn't target the right misconception wastes learner time.

The Automation Opportunity

AI tools can generate working code from natural language descriptions, but they cannot automatically determine what should be built. The instructional designer's role shifts from implementation to specification and quality assurance. This course teaches both.

Course Modules

Module 1: Foundations of Learning Objective Analysis

Topics:

• Bloom's Taxonomy and cognitive complexity levels
• Decomposing compound objectives into atomic concepts
• Identifying implicit prerequisites and assumed knowledge
• The "simulation readiness" test: which objectives benefit from interaction?

Hands-On Activity: Analyze 10 learning objectives from your own teaching context. Classify each by Bloom's level and identify which are candidates for MicroSim development.

MicroSim for This Module: Bloom's Taxonomy Classifier — An interactive tool where users drag learning objectives to the appropriate taxonomy level, receiving immediate feedback on their classification with explanations of why objectives belong at particular levels.

Module 2: The MicroSim Pattern Library

Topics:

• Overview of visualization paradigms and their pedagogical affordances
• p5.js animations: Motion, physics, dynamic systems, cause-and-effect
• Network graphs (vis-network): Relationships, hierarchies, dependencies, influence
• Timelines (vis-timeline): Sequences, history, processes, scheduling
• Charts (Chart.js, Plotly): Comparisons, trends, distributions, correlations
• Maps (Leaflet): Geography, spatial relationships, location-based data
• Diagrams (Mermaid): Flowcharts, state machines, organizational structures
• Set visualizations (Venn.js): Classification, overlap, logical relationships

Hands-On Activity: Match 20 learning objectives to appropriate visualization types. Justify each selection based on concept characteristics.

MicroSim for This Module: Visualization Selector — Users input a learning objective description and the system suggests appropriate visualization types with example thumbnails. Users can explore why certain visualizations work better for certain concept types.

Module 3: Writing Effective MicroSim Specifications

Topics:

• The anatomy of a MicroSim specification document
• Describing visual elements without writing code
• Specifying interaction behaviors and constraints
• Defining success criteria and edge cases
• Common specification ambiguities and how to avoid them
• The "telephone game" problem: preserving intent through AI interpretation

Hands-On Activity: Write specifications for three MicroSims. Exchange specifications with a partner and attempt to build from each other's specs. Identify gaps and ambiguities.

MicroSim for This Module: Specification Completeness Checker — An interactive checklist that guides users through specification writing, highlighting missing elements and common pitfalls. Shows examples of vague vs. precise specifications side by side.

Module 4: Adapting for Audience Levels

Topics:

• Cognitive development stages and their implications for simulation design
• Early Childhood (K-2): Large touch targets, simple cause-effect, minimal text, bright colors
• Elementary (3-5): Guided exploration, scaffolded complexity, reading support
• Middle School (6-8): Abstract concepts, multiple variables, hypothesis testing
• High School (9-12): Real-world applications, data interpretation, edge cases
• Undergraduate: Theoretical foundations, mathematical relationships, professional contexts
• Graduate: Research applications, limitations, advanced parameter spaces
• Corporate: Job-relevant scenarios, time-efficient, immediately applicable

Hands-On Activity: Take a single concept (e.g., "probability") and design MicroSim specifications for three different audience levels. Document how complexity, vocabulary, and interaction patterns change.

MicroSim for This Module: Audience Adaptation Slider — A demonstration showing the same core concept (like fractions) rendered at different complexity levels. A slider moves between kindergarten and graduate school versions, highlighting what changes at each level.

Module 5: Cognitive Load and Visual Design

Topics:

• Cognitive load theory: intrinsic, extraneous, and germane load
• The "split attention" effect and how simulations can cause it
• Progressive disclosure: revealing complexity gradually
• Color, contrast, and accessibility considerations
• Animation speed and learner control
• When less is more: the case for simplicity

Hands-On Activity: Evaluate five existing MicroSims for cognitive load issues. Redesign one to reduce extraneous load while preserving learning effectiveness.

MicroSim for This Module: Cognitive Load Visualizer — An interactive demonstration where users can toggle various visual elements (labels, colors, animations, data points) on and off while a "cognitive load meter" shows estimated mental effort. Helps develop intuition for design tradeoffs.

Module 6: Anticipating Misconceptions

Topics:

• Common misconceptions by subject area
• How simulations can reinforce vs. correct misconceptions
• The "productive failure" approach: letting learners discover errors
• Building prediction prompts into simulations
• Edge cases that reveal conceptual boundaries

Hands-On Activity: For a concept in your teaching area, list five common misconceptions. Design a MicroSim that specifically targets one misconception.

MicroSim for This Module: Misconception Reveal — A simulation about a commonly misunderstood concept (like seasons being caused by Earth's distance from the sun) that first lets users explore their intuition, then reveals the correct model with explicit comparison.

Module 7: Generating MicroSims with AI Tools

Topics:

• Overview of Claude Code skills and the MicroSim generation toolkit
• Invoking the microsim-generator skill effectively
• Interpreting generated output and identifying issues
• The iterative refinement conversation: prompting for changes
• When to regenerate vs. when to manually adjust
• Version control and managing multiple iterations

Hands-On Activity: Generate your first complete MicroSim from a specification you wrote in Module 3. Document the generation process including any refinement prompts needed.

MicroSim for This Module: Generation Workflow Simulator — An interactive flowchart showing the MicroSim generation process. Users can click through different decision points and see example prompts and outputs for each path.

Module 8: Quality Evaluation Frameworks

Topics:

• The three-lens evaluation model:
  • Technical: Does it work? Is it responsive? Are there bugs?
  • Pedagogical: Does it target the learning objective? Is the cognitive level appropriate?
  • User Experience: Is it intuitive? Accessible? Engaging without being distracting?
• Creating evaluation rubrics for different MicroSim types
• Automated vs. human evaluation methods
• Documentation requirements for reusability

Hands-On Activity: Develop an evaluation rubric for your subject area. Apply it to three MicroSims (one you created, two from the library).

MicroSim for This Module: Evaluation Rubric Builder — An interactive tool where users select criteria from categorized lists to build custom evaluation rubrics. Can export rubrics as checklists for practical use.

Module 9: User Testing Methods

Topics:

• Think-aloud protocols for simulation testing
• A/B testing design for educational interventions
• Gathering feedback from learners of different ages and abilities
• Observational techniques: what to watch for
• Interpreting test results and prioritizing changes
• Ethical considerations in educational research

Hands-On Activity: Conduct a think-aloud test with 2-3 learners using a MicroSim you created. Document observations and identify three potential improvements.

MicroSim for This Module: Test Session Planner — An interactive guide that helps users design user testing sessions. Inputs include learner age, concept complexity, and available time. Outputs include suggested protocols, question templates, and observation checklists.

Module 10: Iteration and Refinement

Topics:

• The design-test-refine cycle
• Prioritizing changes: critical vs. nice-to-have
• When a MicroSim needs fundamental redesign vs. incremental improvement
• Building on feedback without scope creep
• Knowing when a MicroSim is "done enough"
• Maintaining a change log and design rationale

Hands-On Activity: Take a MicroSim through two complete iteration cycles. Document each change and its rationale.

MicroSim for This Module: Iteration Tracker — A visual tool showing a MicroSim's evolution through versions. Users can see what changed between iterations, why changes were made, and how evaluation scores improved.

Module 11: Building for Diverse Learners

Topics:

• Universal Design for Learning (UDL) principles applied to simulations
• Accessibility requirements: screen readers, keyboard navigation, color blindness
• Language considerations: vocabulary level, multilingual support
• Cultural sensitivity in examples and contexts
• Supporting learners with different prior knowledge levels
• Differentiation strategies within a single MicroSim

Hands-On Activity: Audit a MicroSim for accessibility issues. Implement at least three accessibility improvements.

MicroSim for This Module: Accessibility Simulator — A tool that lets users experience a MicroSim under various simulated constraints: grayscale mode (color blindness), keyboard-only navigation, screen reader text output, and reduced motion settings.

Module 12: Deployment and Integration

Topics:

• Embedding MicroSims in learning management systems (LMS)
• Integration with intelligent textbooks and course materials
• Analytics: tracking learner interactions
• Maintenance and updates over time
• Building a personal MicroSim library
• Sharing and collaboration with other educators

Hands-On Activity: Deploy a completed MicroSim to your teaching context. Collect initial analytics and plan for ongoing maintenance.

MicroSim for This Module: Deployment Checklist — An interactive checklist that guides users through deployment steps, from final testing through LMS integration. Tracks completion and provides troubleshooting tips for common issues.

Assessment Methods

Formative Assessment (Throughout Course)

• Module reflection journals documenting design decisions
• Peer feedback on specifications and generated MicroSims
• Self-evaluation using course rubrics

Summative Assessment (Course Completion)

Portfolio Project: Create a complete MicroSim package including:

1. Learning objective analysis document
2. Detailed specification
3. Working MicroSim (generated and refined)
4. Evaluation rubric with self-assessment
5. User testing report (minimum 3 participants)
6. Iteration log showing refinement process
7. Accessibility audit and improvements
8. Deployment documentation

Portfolio Evaluation Criteria:

Criterion	Weight
Learning objective alignment	20%
Specification quality and completeness	15%
Technical functionality	15%
Pedagogical effectiveness	20%
User testing rigor	15%
Iteration quality	10%
Documentation clarity	5%

MicroSims Teaching This Course (Meta-Level)

The following MicroSims are developed specifically to teach the concepts in this course, demonstrating the principles being taught:

MicroSim	Type	Module	Concept Taught
Bloom's Taxonomy Classifier	Drag-and-drop classification	1	Cognitive levels of learning objectives
Visualization Selector	Decision tree with examples	2	Matching concepts to visualization types
Specification Completeness Checker	Interactive checklist	3	Components of effective specifications
Audience Adaptation Slider	Comparison slider	4	How complexity scales with audience
Cognitive Load Visualizer	Toggle demonstration	5	Visual design impact on mental effort
Misconception Reveal	Prediction-reveal sequence	6	Designing for conceptual change
Generation Workflow Simulator	Interactive flowchart	7	The AI-assisted creation process
Evaluation Rubric Builder	Configurable checklist	8	Quality assessment frameworks
Test Session Planner	Guided planning tool	9	User testing methodology
Iteration Tracker	Version comparison	10	The refinement cycle
Accessibility Simulator	Constraint simulation	11	Universal design principles
Deployment Checklist	Progress tracker	12	Launch and integration steps

Required Tools and Resources

Software (Skill Libraries Provided Free)

• Claude Code with MicroSim generation skills ($20/month Claude Pro required)
• Access to the Claude Code Skill library on GitHub
• Web browser with developer tools

Recommended Background Reading

• Mayer, R. E. (2009). Multimedia Learning (2nd ed.)
• Clark, R. C., & Mayer, R. E. (2016). E-Learning and the Science of Instruction
• Sweller, J. (2011). Cognitive Load Theory

Time Commitment

• Self-paced: Approximately 60-80 hours total
• Instructor-led: 3-4 hours per week for 12 weeks

Expected Outcomes

Graduates of this course will be able to:

• Produce MicroSims that effectively support specific learning objectives
• Evaluate educational simulations systematically using research-based criteria
• Adapt simulation complexity for any audience from kindergarten to graduate school
• Collaborate with AI tools effectively in the instructional design process
• Build and maintain a personal library of reusable educational simulations
• Apply universal design principles to create accessible learning experiences
• Conduct basic user testing and iterate based on learner feedback

Who Should Not Take This Course

This course may not be the right fit if you:

• Want to learn programming (this course uses AI tools to generate code)
• Need simulations for entertainment rather than education
• Are looking for a library of pre-made simulations (this teaches creation)
• Expect fully automated simulation generation without human judgment

Concepts Covered in This Course

The following concepts are explicitly taught and practiced:

Instructional Design Foundations

• Learning objective analysis and decomposition
• Bloom's Taxonomy (2001 revision) and cognitive complexity levels
• Prerequisite concept identification
• Simulation readiness assessment
• Pedagogical alignment

Visualization and Interaction Design

• Visualization paradigm selection (animation, network, timeline, chart, map, diagram)
• Interaction pattern design (sliders, toggles, drag-and-drop, click events)
• Progressive disclosure techniques
• Feedback loop design
• Scaffolding strategies

Cognitive Science Principles

• Cognitive load theory (intrinsic, extraneous, germane load)
• Split attention effect
• Multimedia learning principles
• Misconception identification and remediation
• Mental model formation

Specification and Communication

• MicroSim specification document writing
• Visual element description without code
• Behavior specification and constraints
• Success criteria definition
• Edge case documentation

Audience Adaptation

• Cognitive development stages (Piaget, Vygotsky)
• Age-appropriate complexity calibration
• Vocabulary level adjustment
• Cultural sensitivity in design
• Prior knowledge assessment

Quality Assurance

• Technical evaluation (functionality, responsiveness, bugs)
• Pedagogical evaluation (alignment, cognitive level, effectiveness)
• User experience evaluation (intuitiveness, accessibility, engagement)
• Rubric development and application
• Automated vs. human evaluation methods

User Research

• Think-aloud protocol design and facilitation
• Observational techniques
• Feedback interpretation
• A/B testing fundamentals
• Ethical considerations in educational research

Iteration and Refinement

• Design-test-refine cycle management
• Change prioritization frameworks
• Version control for educational content
• Design rationale documentation
• Scope management

Accessibility and Inclusion

• Universal Design for Learning (UDL) principles
• Screen reader compatibility
• Keyboard navigation requirements
• Color blindness considerations
• Reduced motion preferences
• Multilingual support strategies

AI-Assisted Development

• Prompt engineering for MicroSim generation
• Iterative refinement conversations
• Output interpretation and issue identification
• When to regenerate vs. manually adjust
• Human-AI collaboration patterns

Deployment and Maintenance

• LMS integration techniques
• Analytics implementation
• Long-term maintenance planning
• Library organization and reusability
• Collaboration and sharing practices

Concepts NOT Covered in This Course

The following topics are outside the scope of this course:

Programming and Technical Implementation

• JavaScript, HTML, or CSS coding
• p5.js, Chart.js, or other library APIs
• Debugging code errors
• Performance optimization
• Database design or backend development
• Version control systems (Git)

Advanced Research Methods

• Statistical analysis of learning outcomes
• Randomized controlled trial design
• Psychometric validation
• Learning analytics at scale
• Machine learning for adaptive learning

Graphic Design

• Visual design principles (typography, color theory, composition)
• Icon and illustration creation
• Animation principles (beyond pedagogical considerations)
• Brand identity development
• Professional graphic design software (Photoshop, Illustrator, Figma)

Learning Management System Administration

• LMS configuration and setup
• User management and permissions
• Gradebook configuration
• Course structure beyond MicroSim embedding
• SCORM/xAPI technical implementation

Content Creation Beyond MicroSims

• Video production and editing
• Podcast or audio content creation
• Written curriculum development
• Assessment item writing (beyond simulation-embedded)
• Gamification and game-based learning design

Organizational Change Management

• Institutional adoption strategies
• Faculty development programs
• Budget planning and resource allocation
• Stakeholder management
• Policy development

Advanced Accessibility

• WCAG 2.1 AA/AAA compliance auditing
• Assistive technology testing
• Legal accessibility requirements by jurisdiction
• Accessibility remediation for legacy content
• Sign language interpretation

Specialized Domains

• Simulation for high-stakes training (medical, aviation, military)
• Virtual reality or augmented reality development
• Hardware-integrated simulations
• Real-time multiplayer educational experiences
• Adaptive learning system design

Learning Objectives by Bloom's Taxonomy (2001 Revision)

The 2001 revision of Bloom's Taxonomy by Anderson and Krathwohl reorganized cognitive processes into six levels, using verbs rather than nouns, with "Create" as the highest level. Below are this course's learning objectives organized by taxonomy level.

Level 1: Remember

Retrieving relevant knowledge from long-term memory

• List the six cognitive levels of Bloom's Taxonomy in order
• Identify the seven primary visualization paradigms used in MicroSim development
• Recall the three components of cognitive load theory
• Name the three lenses of MicroSim evaluation (technical, pedagogical, user experience)
• Define key terms: learning objective, MicroSim, specification, scaffolding, progressive disclosure
• Recognize common misconception patterns in educational simulations

Level 2: Understand

Constructing meaning from instructional messages

• Explain why converting learning objectives to simulations is challenging
• Describe how cognitive development stages affect MicroSim design choices
• Summarize the relationship between cognitive load and visual complexity
• Interpret user testing feedback to identify design issues
• Classify learning objectives by Bloom's Taxonomy level
• Compare different visualization paradigms and their pedagogical affordances
• Exemplify how the same concept can be adapted for different audience levels
• Paraphrase specification requirements into natural language descriptions

Level 3: Apply

Carrying out or using a procedure in a given situation

• Implement a complete MicroSim specification document
• Execute a think-aloud user testing session
• Use the microsim-generator skill to produce working simulations
• Apply cognitive load principles to evaluate an existing MicroSim
• Demonstrate audience adaptation by modifying a MicroSim for a different age group
• Employ an evaluation rubric to assess simulation quality
• Carry out an accessibility audit on a MicroSim

Level 4: Analyze

Breaking material into constituent parts and detecting relationships

• Differentiate between intrinsic, extraneous, and germane cognitive load in a simulation
• Organize learning objectives by prerequisite dependencies
• Attribute MicroSim failures to specific design decisions
• Deconstruct a complex learning objective into simulation-ready components
• Distinguish between misconception-reinforcing and misconception-correcting designs
• Examine user testing data to identify patterns across participants
• Compare multiple MicroSim approaches for the same learning objective
• Outline the decision points in the MicroSim generation workflow

Level 5: Evaluate

Making judgments based on criteria and standards

• Assess whether a MicroSim effectively targets its stated learning objective
• Critique specification documents for completeness and clarity
• Judge the appropriateness of a visualization paradigm for a given concept
• Prioritize iteration changes based on impact and effort
• Justify design decisions using pedagogical principles
• Appraise the accessibility of a MicroSim against UDL guidelines
• Defend audience adaptation choices with cognitive development theory
• Recommend whether to regenerate or manually refine a MicroSim

Level 6: Create

Putting elements together to form a novel, coherent whole

• Design a complete MicroSim specification from a learning objective
• Construct an evaluation rubric tailored to a specific subject area
• Develop a user testing protocol for a target learner population
• Compose iterative refinement prompts that preserve pedagogical intent
• Produce a MicroSim that addresses a documented misconception
• Generate multiple MicroSim variations for different audience levels
• Formulate a maintenance and update plan for deployed simulations
• Assemble a personal MicroSim library with consistent documentation
• Synthesize feedback from multiple sources into prioritized design changes
• Author a complete portfolio demonstrating mastery of the MicroSim development lifecycle

Getting Started

To begin this course:

1. Ensure you have access to Claude Code with MicroSim skills enabled
2. Identify 5-10 learning objectives from your teaching context to use as practice material
3. Complete the Module 1 readings on Bloom's Taxonomy
4. Begin the learning objective analysis activity

Welcome to the future of instructional design! - Dan McCreary