AI and Learning Innovation

November 3, 2025

AI 90-Day Implementation Roadmap

A lightbulb representing AI that reads 90-day Implementation Roadmap

The Problem

Organizations and individuals want to adopt AI but don’t know where to start, what sequence to follow, or how to measure success—leading to scattered adoption, wasted investment, and failure to achieve ROI.

Description

This structured 90-day plan guides systematic AI integration through three phases: Foundation (Days 1-30: assessment, first tool, first project, 20-30% savings), Integration (Days 31-60: workflow integration, second tool, team training, 30-40% savings), and Optimization (Days 61-90: process refinement, organizational scaling, 40-50% sustained savings). Each week includes specific action items with checkboxes, success criteria, ROI tracking framework, and common challenge solutions.

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November 3, 2025

Integrating AI in the ADDIE Model

The Problem

Instructional designers struggle to integrate AI tools efficiently into their traditional ADDIE workflow, unsure of which tools to use at each phase and how much time they can realistically save.

Description

This guide maps AI tools and applications to each phase of the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). It provides specific tool recommendations, time savings data showing 50-90% efficiency gains, and decision frameworks for when to use AI versus traditional methods. The document includes detailed tables comparing traditional timelines with AI-assisted timelines for every major instructional design task.

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November 3, 2025

Integrating AI in SAM

The Problem

Instructional designers need faster, more agile project delivery but don’t know how to leverage AI to accelerate SAM’s iterative cycles without sacrificing quality.

Description

This guide demonstrates how AI transforms the three-phase SAM model (Preparation, Iterative Design, Iterative Development) from a weeks-long process into a days-long workflow. It shows how to create working prototypes in 1-2 hours using tools like Articulate AI. The document provides side-by-side comparisons of SAM versus ADDIE with AI integration and recommends when to use each approach.

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November 3, 2025

Ethical Use of AI in Instructional Design

The Problem

Instructional designers using AI may lack clear ethical frameworks, risking accuracy errors, bias perpetuation, privacy violations, and copyright infringement that could harm learners and create legal liability.

Description

This principles-based framework establishes five core ethical pillars for responsible AI use: Transparency, Accuracy, Bias Mitigation, Privacy, and Intellectual Property protection. Each principle includes practical “Poor Practice vs. Best Practice” scenario comparisons showing real-world applications, plus implementation guidelines and a comprehensive 15-point ethics checklist. The document is grounded in 2024 U.S. Department of Education guidelines on algorithmic discrimination and provides actionable steps for building ethics into your AI workflow.

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AI and Learning Innovation

November 3, 2025

AI in ID: Decision Trees and Matrices

A lightbulb representing AI that reads AI Decision Trees

The Problem

Instructional designers face daily decisions about which AI tool to use, when to use AI vs traditional methods, and whether their organization is ready for AI adoption—without clear frameworks these decisions are inconsistent and suboptimal.

Description

This document provides five practical decision-making frameworks: Tool Selection Decision Tree (guiding users from their primary need to the right tool), Methodology Selection Tree (ADDIE vs SAM), AI vs Human Decision Tree (safety, accuracy, volume considerations), AI Readiness Assessment Matrix (8 organizational factors with scoring), and Task-Specific Tool Recommendations (15 common ID tasks mapped to best tools with time savings). Each framework includes step-by-step guidance and is designed as a reference tool for daily use.

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