1. Game Overview
Game Name (Internal)
Cell Differentiation Builder
Subject
IB Biology / Cell Biology
Core Concept
Turn stem cell differentiation into a fast, decision-based cognitive game that reinforces classification, lineage logic, and misconception correction through user actions.
What this is NOT
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Not an animation
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Not a video
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Not a passive explanation
What this IS
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A decision loop requiring user actions
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A system that measures accuracy, speed, and misconception patterns
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A repeatable cognitive training game
2. Learning Objectives
By playing this game, students should be able to:
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Distinguish between pluripotent, multipotent, unipotent cells
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Identify valid differentiation outcomes for a given stem cell
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Recognize and correct common misconceptions
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Understand cell lineage constraints (what is NOT possible)
3. Core Gameplay Loop
Round Length
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Target: 20–40 seconds per round
Screen Elements
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Starting Cell Card (center/top)
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Candidate Target Cells (6–10 cards)
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Drop Zone / Selection Area
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Optional: Timer bar / streak indicator
User Actions (MANDATORY)
At least one of the following per round:
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Drag & drop cards into the correct area
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Tap/select correct targets (mobile compatible)
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Drag cards into correct sequence (advanced rounds)
If a round can be completed without clicking or dragging, it fails the design requirement.
4. Game Modes (Progressive Difficulty)
Mode A – Recognition
Instruction
“Select all cells that this stem cell can differentiate into.”
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1 start cell
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6 target options
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Clear correct answers
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No distractors from distant lineages
Goal: confidence + basic mapping
Mode B – Discrimination (Misconception Traps)
Instruction
“Select only the valid differentiation outcomes.”
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Add distractors that are close but wrong
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Examples:
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Hematopoietic stem cell → neuron
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Mesenchymal stem cell → RBC
Goal: eliminate false positives
Mode C – Sequencing
Instruction
“Arrange the differentiation pathway correctly.”
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Cards:
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Pluripotent → multipotent → progenitor → specialized
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User must drag into correct order
Goal: reinforce process logic
Mode D – Error Detection
Instruction
“Tap the incorrect step in this pathway.”
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Show mostly correct diagram with one wrong arrow
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User identifies the mistake
Goal: diagnose understanding
5. Feedback Rules (Critical)
Correct Action
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Immediate visual confirmation
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Small positive animation or highlight
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Optional micro-animation (cell transforms briefly)
Incorrect Action
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Card returns to original position
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Short misconception label appears:
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“Wrong lineage”
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“Too specialized”
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“Not derived from this germ layer”
Do NOT show long explanations mid-round.
6. Scoring & Metrics
Per Round Metrics
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Accuracy (% correct)
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Time to first action
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Total completion time
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Number of incorrect attempts
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Misconception types selected
Scoring Logic (Example)
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+10 per correct card
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−3 per incorrect card
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Speed bonus for completing under target time
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Streak bonus (3+ correct rounds)
7. Data Structure (Required)
Each row = one playable round.
Required Fields
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round_id
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mode (A / B / C / D)
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Mode A = multi-select / click correct tiles
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Mode B = single-select MCQ / “pick outcomes”
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Mode C = sequencing / missing-step (drag-and-drop)
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Mode D = error spotting (tap incorrect step/claim)
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start_cell_name
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start_cell_type (pluripotent / multipotent / unipotent)
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valid_targets (list)
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distractors (list)
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misconception_tags (map distractor → tag)
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difficulty_level
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hint_if_wrong (short string)
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time_limit_seconds
8. Example Round (Plain Text)
start_cell_name: Hematopoietic stem cell
start_cell_type: multipotent
valid_targets:
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Red blood cell
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Neutrophil
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Macrophage
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B lymphocyte
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T lymphocyte
distractors:
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Neuron
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Hepatocyte
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Skeletal muscle
misconception_tags:
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Neuron → “wrong lineage”
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Hepatocyte → “wrong tissue origin”
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Skeletal muscle → “mesoderm mismatch”
mode: B
difficulty: medium
9. UX Constraints
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One clear question per round
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No text explanations longer than 8 words mid-round
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Touch-friendly layout (tablet first)
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One round must feel finishable in under 30 seconds
10. Success Criteria (Prototype Acceptance)
This prototype is considered successful if:
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Average round completion ≤ 40s
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Players voluntarily replay incorrect rounds
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Misconception frequency decreases over sessions
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Game feels distinct from flashcards or videos
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Intern can add new rounds without changing code
11. Future Scalability (Non-Blocking)
This structure must later support:
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Other Biology topics (immune system, mitosis)
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Other subjects (Chemistry reactions, Math steps)
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AI-generated rounds using same schema
12. Key Principle (Non-Negotiable)
If learning occurs because the student makes a decision, it’s a cognitive game.
If learning occurs because they watched something, it’s content.