Smaller creators build richer games, and teachers and trainers gain new ways to let people learn by doing.
The unlock
AI assists with code, art, dialogue, testing, and interactive environments, shrinking the work required to turn a playable idea into a prototype.
The hard part
Generating an environment is not the same as shipping a good game. Persistent rules, fun, performance, accessibility, and meaningful design remain demanding.
How to read these scenarios
These are positive possibilities, not promises or timeline forecasts. Lists put the most intriguing ideas first: an editorial judgment about interest and transformative potential, not likelihood or arrival date. The labels describe what must happen next, not numerical probabilities.
Already emerging: early versions or important components exist; the full scenario may still need work.
Plausible next step: an extension of demonstrated capabilities, with meaningful engineering and adoption work remaining.
Breakthrough-dependent: needs a scientific or technical advance that is not assured.
Products are useful tools and services; cures include potential treatments and prevention, not claims of proven cures; insights are discoveries that help us understand the world.
Individuals and small teams prototype games that once required several specialist disciplines.
Why AI helps
Code and asset generation can accelerate early iteration and help creators test mechanics.
What still has to happen
Production-quality games still require debugging, design, art direction, and integration.
Read details ↗Full details: A personal game-making workshop
Making games · Product
A personal game-making workshop
Already emerging
Individuals and small teams prototype games that once required several specialist disciplines.
AI-assisted tools can shorten the distance between a game idea and something playable by helping with scripts, placeholder art, dialogue, and routine debugging. A creator can test whether a mechanic is enjoyable before investing heavily in production.
The advantage is rapid iteration, not guaranteed quality. Game systems interact in surprising ways, and generated code or assets still need integration, review, and testing on the devices players will actually use.
What this could look like
A first-time creator builds a small puzzle game, watches a few people play, and revises the confusing rules. AI helps implement those revisions; the creator decides what makes the game worth playing.
Who benefits, and how
More people can test an original interactive idea.
Small teams can spend more attention on design rather than repetitive setup.
What would make it real
Ship a stable, responsive build on the intended devices.
Test the actual player experience and establish appropriate rights for the assets.
Production-quality games still require debugging, design, art direction, and integration.
A child practices fractions, a language, or scientific reasoning through a game that changes with their progress.
Why AI helps
AI can generate variations and feedback around a teacher-defined learning objective.
What still has to happen
Learning gains must be measured; engagement alone does not establish educational value.
Read details ↗Full details: Learning adventures that adapt to the player
Making games · Product
Learning adventures that adapt to the player
Plausible next step
A child practices fractions, a language, or scientific reasoning through a game that changes with their progress.
An adaptive game could vary the next challenge based on what a learner appears to understand. A good system would distinguish a conceptual mistake from a slip and offer an explanation or easier intermediate step when appropriate.
Adaptation should serve a learning goal that educators can inspect. Making the game more absorbing is not the same as making it more educational, and learners should not be trapped in an endless cycle of easier rewards.
What this could look like
A fractions game notices that a learner confuses the size of a piece with the number of pieces. It introduces a visual comparison, then checks the idea again in a different context.
Who benefits, and how
Practice tailored to a learner's current misunderstanding.
Useful feedback for teachers about where learners need support.
What would make it real
Evaluate learning and retention against credible alternatives.
Give educators control over objectives, difficulty, and the information collected about learners.
Learning gains must be measured; engagement alone does not establish educational value.
Technicians and emergency teams rehearse uncommon scenarios before encountering them in real life.
Why AI helps
AI can generate varied training situations and responsive characters inside validated simulations.
What still has to happen
The simulator's behavior and scoring must match real procedures; realistic images do not guarantee realistic physics.
Read details ↗Full details: Practice worlds for difficult jobs
Making games · Product
Practice worlds for difficult jobs
Plausible next step
Technicians and emergency teams rehearse uncommon scenarios before encountering them in real life.
Training simulations let people rehearse rare or difficult situations without exposing a real system to their mistakes. AI could make those situations more varied and the simulated people more responsive.
The training value depends on the decisions and consequences being correct. A photorealistic emergency scene can be a poor simulator if it rewards behavior that would fail in the real setting.
What this could look like
A maintenance team rehearses diagnosing a fault while a simulated colleague supplies incomplete information. The exercise rewards correct checks and communication, not merely reaching an answer quickly.
Who benefits, and how
More opportunities to practice unusual situations.
Repeatable scenarios for comparing procedures and discussing decisions.
What would make it real
Validate scenario logic, physical behavior, and scoring with domain experts.
Demonstrate transfer to real tasks rather than relying on in-simulator scores.
The simulator's behavior and scoring must match real procedures; realistic images do not guarantee realistic physics.
Players get better-supported control alternatives, adjustable pacing, and explanations that suit their needs.
Why AI helps
AI can help adapt interfaces and generate accessible variants while developers preserve the core experience.
What still has to happen
Changes need testing with disabled players and must remain predictable and controllable.
Read details ↗Full details: Games that fit more bodies and abilities
Making games · Product
Games that fit more bodies and abilities
Plausible next step
Players get better-supported control alternatives, adjustable pacing, and explanations that suit their needs.
Accessibility needs vary widely. Some players need alternative controls; others need clearer audio cues, larger text, less time pressure, or an explanation of a confusing objective. AI could help developers offer and maintain more of these adaptations.
Players should remain in control of the changes. A system that unpredictably takes over or silently changes the rules can undermine accessibility, trust, and the satisfaction of playing.
What this could look like
A player uses switch controls and chooses slower timing windows. The game offers concise explanations when requested while preserving its core puzzles and allowing the player to adjust the assistance.
Who benefits, and how
A wider range of players can participate in the same game.
More practical ways for smaller studios to support varied access needs.
What would make it real
Co-design and test with people who use the relevant accessibility features.
Keep adaptations predictable, reversible, and compatible with the game's mechanics.
Changes need testing with disabled players and must remain predictable and controllable.
Researchers explore how people coordinate under different rules and resource constraints.
Why AI helps
AI-assisted simulation can make alternative scenarios cheap to construct and test.
What still has to happen
Behavior in a game does not automatically generalize to society; findings need independent evidence.
Read details ↗Full details: Playable laboratories for cooperation
Making games · Insight
Playable laboratories for cooperation
Plausible next step
Researchers explore how people coordinate under different rules and resource constraints.
Interactive scenarios can help researchers compare how different rules affect coordination: what information people receive, how they share resources, or how they resolve competing goals. AI can reduce the cost of constructing and varying these environments.
A simulated society is not a substitute for evidence about actual people. Results depend on participants, incentives, and assumptions; AI agents may behave very differently from humans even when their dialogue sounds natural.
What this could look like
A research team compares two ways of sharing a limited resource in an experimental game, measures participants' decisions, and checks whether the result survives changes to the rules and participant pool.
Who benefits, and how
Faster exploration of competing explanations for collective behavior.
Concrete teaching tools for discussing incentives and institutional design.
What would make it real
State assumptions and distinguish human observations from simulated-agent behavior.
Replicate findings and test whether they extend beyond the specific game.
Behavior in a game does not automatically generalize to society; findings need independent evidence.
Individuals and small teams prototype games that once required several specialist disciplines.
AI-assisted tools can shorten the distance between a game idea and something playable by helping with scripts, placeholder art, dialogue, and routine debugging. A creator can test whether a mechanic is enjoyable before investing heavily in production.
The advantage is rapid iteration, not guaranteed quality. Game systems interact in surprising ways, and generated code or assets still need integration, review, and testing on the devices players will actually use.
What this could look like
A first-time creator builds a small puzzle game, watches a few people play, and revises the confusing rules. AI helps implement those revisions; the creator decides what makes the game worth playing.
Who benefits, and how
More people can test an original interactive idea.
Small teams can spend more attention on design rather than repetitive setup.
What would make it real
Ship a stable, responsive build on the intended devices.
Test the actual player experience and establish appropriate rights for the assets.
Production-quality games still require debugging, design, art direction, and integration.
A child practices fractions, a language, or scientific reasoning through a game that changes with their progress.
An adaptive game could vary the next challenge based on what a learner appears to understand. A good system would distinguish a conceptual mistake from a slip and offer an explanation or easier intermediate step when appropriate.
Adaptation should serve a learning goal that educators can inspect. Making the game more absorbing is not the same as making it more educational, and learners should not be trapped in an endless cycle of easier rewards.
What this could look like
A fractions game notices that a learner confuses the size of a piece with the number of pieces. It introduces a visual comparison, then checks the idea again in a different context.
Who benefits, and how
Practice tailored to a learner's current misunderstanding.
Useful feedback for teachers about where learners need support.
What would make it real
Evaluate learning and retention against credible alternatives.
Give educators control over objectives, difficulty, and the information collected about learners.
Learning gains must be measured; engagement alone does not establish educational value.
Technicians and emergency teams rehearse uncommon scenarios before encountering them in real life.
Training simulations let people rehearse rare or difficult situations without exposing a real system to their mistakes. AI could make those situations more varied and the simulated people more responsive.
The training value depends on the decisions and consequences being correct. A photorealistic emergency scene can be a poor simulator if it rewards behavior that would fail in the real setting.
What this could look like
A maintenance team rehearses diagnosing a fault while a simulated colleague supplies incomplete information. The exercise rewards correct checks and communication, not merely reaching an answer quickly.
Who benefits, and how
More opportunities to practice unusual situations.
Repeatable scenarios for comparing procedures and discussing decisions.
What would make it real
Validate scenario logic, physical behavior, and scoring with domain experts.
Demonstrate transfer to real tasks rather than relying on in-simulator scores.
The simulator's behavior and scoring must match real procedures; realistic images do not guarantee realistic physics.
Players get better-supported control alternatives, adjustable pacing, and explanations that suit their needs.
Accessibility needs vary widely. Some players need alternative controls; others need clearer audio cues, larger text, less time pressure, or an explanation of a confusing objective. AI could help developers offer and maintain more of these adaptations.
Players should remain in control of the changes. A system that unpredictably takes over or silently changes the rules can undermine accessibility, trust, and the satisfaction of playing.
What this could look like
A player uses switch controls and chooses slower timing windows. The game offers concise explanations when requested while preserving its core puzzles and allowing the player to adjust the assistance.
Who benefits, and how
A wider range of players can participate in the same game.
More practical ways for smaller studios to support varied access needs.
What would make it real
Co-design and test with people who use the relevant accessibility features.
Keep adaptations predictable, reversible, and compatible with the game's mechanics.
Changes need testing with disabled players and must remain predictable and controllable.
Researchers explore how people coordinate under different rules and resource constraints.
Interactive scenarios can help researchers compare how different rules affect coordination: what information people receive, how they share resources, or how they resolve competing goals. AI can reduce the cost of constructing and varying these environments.
A simulated society is not a substitute for evidence about actual people. Results depend on participants, incentives, and assumptions; AI agents may behave very differently from humans even when their dialogue sounds natural.
What this could look like
A research team compares two ways of sharing a limited resource in an experimental game, measures participants' decisions, and checks whether the result survives changes to the rules and participant pool.
Who benefits, and how
Faster exploration of competing explanations for collective behavior.
Concrete teaching tools for discussing incentives and institutional design.
What would make it real
State assumptions and distinguish human observations from simulated-agent behavior.
Replicate findings and test whether they extend beyond the specific game.
Behavior in a game does not automatically generalize to society; findings need independent evidence.