“I'm trying to build a prototype cognitive learning game for an intern to follow. The prototype can serve as an example. I guess I'm best with biology so am thinking along the line of cell studies. Maybe pluripotent or multipotent cell differentiation into specialised cell types. Cell theory showing how bigger prokaryotes eating smaller prokaryotes led to the rise of eukaryotes. Maybe how lymphocytes are fighting against pathogens with options to eat or shoot antibodies...
Our cognitive game designer says all I need to give is a well documented requirements. For games, there must be user clickage induced actions. My biggest concern is on what the difference is with animated video or gif images.”
You’re thinking in exactly the right direction: a prototype cognitive game is not “content” (like an animation) — it’s a measurement + decision loop that forces retrieval, discrimination, sequencing, and error-detection through user actions.
Below is (1) the clearest way to draw the line between “game” vs “animation”, and (2) a ready-to-hand-to-an-intern prototype spec using your biology ideas, with concrete click/drag actions and dataset requirements.
1) “Cognitive game” vs “animated video/gif” — the real difference
Animated video / gif
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User is passive
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Learning = exposure / explanation
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No reliable proof of understanding
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No adaptive difficulty
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No memory reinforcement loop
Cognitive game
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User must act (click/drag/type/select/order)
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Learning = retrieval + discrimination + correction
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System measures accuracy + speed + error type
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Enables spaced repetition + adaptive difficulty
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Produces diagnostic data (what they confuse, where they hesitate)
One-sentence rule:
If the user can watch it and “feel like they learned” without making decisions, it’s not a cognitive game — it’s content.
2) Prototype you can build: “Cell Differentiation Builder” (Pluripotent → Specialized)
This one is great because it naturally supports:
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classification
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prerequisite logic
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misconception traps
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drag-and-drop actions
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fast rounds
Game name (internal)
Differentiation Builder: From Stem Cell to Tissue
Learning goal (for spec)
Students can:
1.
distinguish pluripotent vs multipotent vs unipotent
2.
match stem cell type → possible differentiated outcomes
3.
identify incorrect differentiation pathways (misconceptions)
3) Core gameplay loop (must-have interactions)
Round structure (20–40 seconds)
Screen shows:
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A “starting cell” card (e.g., pluripotent stem cell / hematopoietic stem cell)
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6–10 possible target cell cards (neurons, hepatocytes, RBC, B cell, muscle cell, etc.)
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A “Differentiate” area (drop zone)
User action
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Drag and drop ONLY the valid outcomes into the drop zone within a time limit
or
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Tap-select outcomes (mobile-friendly)
System feedback (instant)
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Correct cards “lock in” with a pleasant success feedback
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Incorrect cards bounce back with a short hint tag like:
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“Too specialized”
◦
“Wrong lineage”
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“Not possible from this stem cell type”
Scoring
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◦
points for correct
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− points for wrong
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bonus for speed
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track common wrong picks (misconceptions)
4) Difficulty modes (so it feels game-like, not quiz-like)
Mode A — Warm-up (recognition)
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Show the “gap” clearly: “Pick ALL that can differentiate from this cell.”
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Fewer options (6)
Mode B — Discrimination (misconception traps)
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Add distractors from nearby lineages
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Example: hematopoietic stem cell → distractor “neurons” or “hepatocytes”
Mode C — Sequencing (procedural)
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Give 4 cards: pluripotent → multipotent → progenitor → specialized
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User must drag order correctly
Mode D — “One wrong step”
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Show a pathway with one incorrect arrow
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User taps the wrong arrow (error spotting)
5) Dataset requirements (what the intern needs to prepare)
You want your designer to be able to scale this across topics later, so make the dataset structured.
Minimal dataset fields (one row = one round)
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round_id
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start_cell (name)
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start_cell_level (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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hint_if_wrong (short hint rules or per distractor)
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difficulty (A/B/C/D)
Example round (plain English)
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start_cell: “Hematopoietic stem cell (multipotent)”
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valid: RBC, neutrophil, macrophage, B cell, T cell
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distractors: neuron, hepatocyte, skeletal muscle
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misconception tags:
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neuron → “wrong lineage”
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hepatocyte → “wrong tissue origin”
6) UI requirements (so it doesn’t become “just flashcards”)
Must-have “game feel” elements (simple but effective)
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Timer bar (even optional)
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Combo streak (3 correct in a row)
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Tiny celebratory feedback (sound optional)
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“Retry wrong ones” micro-loop (spaced repetition inside the round)
Must-have analytics
Per round:
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time_to_first_action
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total_time
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accuracy
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wrong_choice_ids
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hesitation (time before choosing each card)
7) How this differs from “a cool animation”
You can still use animations, but only as feedback, not as the main learning delivery.
Example:
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If user correctly builds “Hematopoietic → RBC”, show a tiny 1-second “cell transforms” animation.
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If wrong, show a “blocked” effect + misconception tag.
So animation becomes a reward/clarifier, while the core learning comes from the choices.
8) Alternative biology prototypes (if you prefer “immune battle”)
Your lymphocyte idea can work, but it risks turning into “game mechanics first, learning second.”
If you still want it, constrain it to cognitive actions:
“Immune Response Decision”
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Each turn: pathogen appears with 2–4 labeled traits
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User must choose:
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phagocytosis vs antibodies vs cytotoxic kill
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Wrong choice explains why (e.g., “viruses inside cells → cytotoxic T cells”)
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Tracks misconception patterns
It’s still a cognitive game if:
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decisions are tied to biology rules
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wrong answers are categorized + repeated later (spaced repetition)
9) What to hand to the intern (super practical)
Give them this checklist as the “prototype package”:
1.
30 rounds for Mode A (recognition)
2.
30 rounds for Mode B (distractor discrimination)
3.
15 rounds for Mode C (sequencing)
4.
UI wireframe of one screen
5.
Data schema (fields above)
6.
Success criteria:
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average round ≤ 35 seconds
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return rate to “retry wrong ones” ≥ X
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measurable reduction in repeat mistakes over 3 sessionss