Three tiers, two views per tier. Left = grid view (Fitzgerald Key colored noun grid). Right = scene view (AI-illustrated scene with tappable word hotspots). Same breakfast scene shown in three art styles to demonstrate tier progression and visual direction.
Mockup tiles at display scale. Actual iPad app tiles are 60pt minimum — see Screen & Device Capacity below for real hardware math. Bathroom and playtime scene illustrations pending generation.
Marcus is 14 months old. He has no spoken words yet. His SLP has set Meadow to Tier 1. Here's what breakfast looks like.
At this stage, multi-step sequences add cognitive load without benefit. The goal is cause-and-effect. Drager et al. (2003): children under 24 months succeed with scene displays but fail with symbolic grids.
Expectant pausing is a core prelinguistic technique (Yoder & Warren, 1998). 7 seconds (vs. 5 at higher tiers) because motor planning takes longer at this stage. If no tap, the companion gently repeats and advances — no child is ever stuck.
Emotional expression must be low-friction. When a child is upset, multi-step interaction is the last thing they need. The device says it, the caregiver hears it, the child is acknowledged.
Amara is 20 months old. She understands that pictures mean things and can reliably tap a target. Her SLP has moved her to Tier 2. The speech bubble is now active.
The speech bubble is an offer, not a gate. "Pancakes" already spoke on the first tap. The combination is available for children ready to take that step — mirroring how caregivers naturally expand a child's utterance: child says "milk," parent says "want milk?"
Contextual suggestions reduce cognitive load — the child doesn't search through irrelevant options. Real conversations are situated in physical context (Beukelman & Light, 2020).
Zoe is 3 years old. She uses dozens of words reliably and has started combining them on her own. Her SLP has moved her to Tier 3. The sentence engine is active.
This is generative language — Zoe assembled a novel sentence that no one programmed. The sentence engine helped by offering predictions, but Zoe chose her own path.
At Tier 3, the engine shifts from templates to grammar-aware predictions. It understands word order (verb → adjective → noun) and suggests accordingly. The child constructs novel utterances with scaffolding — the hallmark of the 24-48 month language explosion (Bates & Goodman, 1997).
Person + action + object combinations (agent-action-object) are a key milestone at MLU 2.5+, emerging 24-36 months. The sentence engine scaffolds word order so the child can focus on meaning, not grammar.
How words and scenes share the iPad screen as vocabulary grows. Each diagram below shows the proportion of screen used for word tiles (teal edges) versus the illustrated scene (gold center).
| T1 · 72pt · 1 row | T2 · 60pt · 2 rows | T3 · 60pt · 2 rows | |
|---|---|---|---|
| Top edge | 12 | 30 | 30 |
| Bottom edge | 13 | 30 | 30 |
| Left edge | 7 | 9 | 9 |
| Right edge | 9 | 11 | 11 |
| Total available | 41 | 80 | 80 |
| Assigned | 23 | 38 | 56 |
| Empty | 18 (44%) | 42 (53%) | 24 (30%) |
| Scene | 986 × 632pt (64%) | 1010 × 524pt (55%) | 1010 × 524pt (55%) |
| T1 · 60pt · 1 row | |
|---|---|
| Top edge | 14 |
| Bottom edge | 14 |
| Left edge | 7 |
| Right edge | 10 |
| Total available | 45 |
| Assigned | 23 |
| Empty | 22 (49%) |
| Scene | 963 × 580pt (66%) |
Edge padding 8pt, tile gap 6pt, corner anchors 66pt. T1 on iPad 11 uses 72pt tiles in 1 row. T2/T3 use 60pt tiles in 2 rows on top/bottom. Left and right edges are always single columns.