Reinventing Model Communication with Gen AI
By G. Sawatzky, embedded-commerce.com
Sept 2025
Why Diagrams Fall Short
- Static snapshots miss causality and constraints.
- Ambiguity creates misalignment and rework.
- We need dynamic, interactive explanations.
The Conceptual Model: The Real Thing
- Structured understanding of concepts and rules.
- Defines behavior, constraints, and relationships.
- Best communicated with stories, Q&A, and feedback.
Story-Based Narrative
- Explain flows with short animated sequences.
- Use causality and temporal order to show behavior.
- Pair narrative with visual and textual cues.
Conversational Model
- Ask natural-language questions about the model.
- Explore “what if” scenarios and see implications.
- Ground answers in rules, constraints, citations.
Lessons from Inclusive Design
- One size does not fit all: allow modality preferences.
- Reduce cognitive load; highlight what matters now.
- Personalize outputs for comprehension speed.
Multi-Modal Communication
- Visual + text + sonic + haptic, weighted per user.
- Use summaries and progressive disclosure.
- Synchronize cues across modalities.
Focal Point Guided Exploration
- Center attention on intent (“Show Customer Returns”).
- Dim noise; emphasize relevant rules and objects.
- Maintain context as the user drills in.
Progressive Disclosure
- Reveal detail on demand; keep the overview clean.
- Drill-down to sub-processes with local narratives.
- Use animation to show state changes and rule fires.
Summaries and Synthesis
- Generate concise explanations for complex areas.
- Tailor tone and modality to the receiver.
- Provide citations to model elements and rules.
Practical Paths for Tools
- Animate narrative steps from formal logic.
- Enable direct Q&A against the model.
- Add modality preferences and saved profiles.
Example Mockups
- Storyboard panel with narrator captions.
- Chat panel wired to model queries.
- Multi-modal control with sliders (visual/text/sonic/haptic).
Tackling Information Overload
- Focal Point Exploration: AI highlights relevant elements across modalities while dimming noise based on user intent.
- Progressive Disclosure: Drill down through abstraction layers; reveal detail contextually on demand.
- Summarization & Synthesis: AI generates concise insights tailored to user's preferred modality.
- Noise Reduction: Predictive highlighting based on user behavior; de-emphasize less relevant information.
Gen AI dynamically adjusts presentation to reduce cognitive load and guide attention to what matters.
Sensory and Haptic Models
- Haptic feedback: Physical objects represent concepts; vibration or resistance signals rule violations.
- Sonic models: Each concept has a unique sound; harmonious chords for valid states, dissonance for errors.
- Spatial reasoning: Tangible interfaces tap into kinesthetic understanding of abstract concepts.
Lessons from accessibility reveal universal principles: multi-sensory approaches enhance comprehension for everyone.
ORM Tool Enhancements: Phase 1
- Dynamic Story Narratives: Real-time animation engine translates FOL processes into visual "movies" showing state changes.
- Direct Model Querying: Natural language questions against ORM model; system parses, queries FOL, returns precise answers.
- User Preference Profiles: Capture and weight modality preferences (visual, text, auditory, haptic).
Build on existing verbalizations and narratives; leverage FOL representation to drive new experiences.
ORM Tool Enhancements: Phase 2
- User-Driven Flow: Fast-forward, rewind, pause narrative animations; interactive breakpoints for queries.
- "What If" Scenarios: Propose hypothetical model changes; AI responds with logical implications and conflicts.
- Adaptive Output: Dynamically adjust presentation based on user profiles (sound signatures, haptic emphasis, visual focus).
- Interactive Playground: Virtual sandbox to manipulate conceptual entities; AI provides immediate feedback on consistency.
Outcomes
- Faster alignment and fewer misinterpretations.
- Explainable, testable conceptual understanding.
- Accessible communication for diverse audiences.
- Reduced cognitive load through intelligent filtering.
- Personalized learning paths based on modality preferences.
References & Link
- UDL guidelines • Cognitive load theory (Sweller, 1994)
- Multi-sensory research (Obrist et al., 2016; Zanella et al., 2023)
- Adaptive interfaces and personalization literature