The day we are born is the day we begin learning—unless, of course, you're a nativist, and hold that we are born with certain pieces of innate knowledge. But let us entertain the perspective of the experimentalist, who sees humans as experiential learners at their core—we observe first, then rationalize.

Learning is embedded in our nature as sapient beings. It is how we adapt and survive. We avoid repeating the same mistakes that introduce chaos into our systems—and that, in itself, is learning. Now, welcome to the 21st century—where we no longer learn just to survive nature, but to survive modernity. Instead of hunting or foraging, we navigate bureaucracies, pass through artificial processes, and collect credentials to stay afloat in a hyper-industrialized world.

And yet, beyond formal schooling and institutional instruction, we find something older—something more primal. Play remains one of our most natural forms of learning. It predates classrooms, lectures, and standardized tests. We see it not only in humans, but across the animal kingdom: kittens stalk shadows to rehearse hunting, young wolves play-fight to prepare for survival, and children build imaginary worlds to explore roles, rules, and consequences. Play is instinctual. It is ritual. It is how we first learn—through curiosity, mimicry, and experimentation. A social process where the experienced guide the new, where knowledge is not transferred, but discovered. In games, this continues: players onboard newcomers into systems the designers have crafted—mechanics, roles, goals—forming an unspoken tradition of learning through doing.

I remember visiting my sister’s home during Christmas. One evening, we decided to play board games. Naturally, my brother-in-law took on the familiar role of guide—walking new players like me through the ritual of learning a card game. He introduced the mechanics, explained the rules, and outlined the systems necessary to play what the designers had crafted. We played a few tutorial rounds, complete with gentle guidance, tips on what to do, and what mistakes to avoid. It was, unmistakably, a ritual—a social act of onboarding that happens across nearly every game. And through it, we see how learning in games is not just instructional—it is cultural, shared, and deeply human.

Learning is an essential process in games. In fact, designers should build systems within systems that naturally onboard players—guiding them not just into how to play, but into how to belong in the game’s world. Just as my brother-in-law taught me to play that card game through gentle instruction and tutorial rounds, game designers can craft intelligent learning systems—ones that teach mechanics, rules, and even lore—either explicitly, through bold UI labeled “Tutorial,” or implicitly, through immersive world design and narrative cues.

Some systems go beyond onboarding and become spaces for lifelong skill development. Games like Overwatch and CS:GO are prime examples, where mastery is not handed to the player, but earned through time, practice, and reflection. These games are built around skill-based systems—timing, precision, map awareness, and team synergy—all governed by structured systems like ranking roles and skill-based matchmaking which were intentionally crafted to enable this mastery system.

Naturally, this leads us to consider the many scientific models of learning—each offering insight into how players engage with game systems. These models help us understand not only how gamers learn, but also how we can design better systems—be it shops, combat, dialogue, or narrative structure—that function as onboarding interfaces, gently guiding players into the experience. Many of these frameworks were engineered through cognitive psychology, rooted in how humans process, absorb, and apply knowledge over time.

By demystifying learning through models, we can begin to see learning itself as a kind of progression system—a metaphorical leveling-up from beginner to expert. And once this lens is in place, we gain the ability to turn theoretical insights into practical design strategies—guidelines for crafting systems, mechanics, and level design that align with how humans actually learn through play.

First, understanding the connection between humans, play, and games is foundational. As humans, we possess the unique ability to interact with abstract systems—and play is one of the purest forms of that interaction. It is not limited to leisure; play is a paradigm of engagement, a way of thinking and doing. We play not just with toys or games, but with ideas, with tools, and even with our work—transforming the mundane into something meaningful or joyful. This mindset, often called playful thinking, reveals itself in how we explore, experiment, and make sense of the world.

Games, in turn, are formalized systems that call us to play. They attract us through incentives—mechanics, rules, aesthetics, characters, roles, or even deeper messages that some players interpret on a symbolic or emotional level. This layered engagement reflects what some theorists call the “dimensions of fun.” But fundamentally, when we play games, we are interacting with complex systems—ones that contain players, rules, boundaries, goals, and feedback loops.

How do we make sense of these systems? Through constructivism—a theory of learning which holds that knowledge is actively constructed, not passively absorbed. We learn by doing, by testing, by making mistakes, and by reflecting on outcomes. And this is where experiential learning comes in: we understand a system not by being told how it works, but by interacting with it, responding to its feedback, and gradually internalizing its logic through play.

Games are experiential engines by design—they create systems that players actively engage with, adapt to, and learn from. It’s only natural, then, that they function as experiential learning systems, where doing, failing, and reflecting are central to player growth. This aligns perfectly with the playful mindset—a state defined by low stakes, intrinsic motivation, freedom to experiment, and the safety to fail without real-world consequences.

Through this lens, we begin to see games not just as entertainment, but as structured elegant loop of learning. Players apply constructivist thinking the moment they press play. With curiosity and experimentation, they enter feedback loops that teach and reinforce rules, systems, and strategies. These loops—mechanical, visual, narrative, or social—make learning feel seamless, embedded in the very act of play. In this way, we can begin to think of games as cybernetic systems—self-regulating environments where the player and the game are in constant feedback-driven dialogue of learning and doing.

One exemplar model, that, scholars of learning often learn, when learning how to learn, is Bloom's Taxonomy. Its a model for how learners grow from recognizing patterns to inventing new ones. In fact, when players learn a game, they essentially go over Bloom's Learning progression, from remembering the facts and definition of the game's rules, understanding the causation between systems, applying mechancis into their own game state, analyzing choices emergent to the system, evaluating choices, and creating strategies/theorycrafting serves a foundatiuonal to how we can deconstruct the gamers mind. This progression is foundational to how we can deconstruct the gamer’s mind—not just as a player of games, but as an active learner within systems.

Thus, we have a langauge to how we can design game systems that naturally onboards new players into our magic circle, and seeing the long-term to how gamers evolutionize from remembering elemental rules to creating their own strategies, playstyles, and theorycrafting within your own games!

Learning systems within game aligns with Raph Koster’s central claim in *A Theory of Fun for Game Design*: that **fun is the experience of learning in disguise**. When a game presents new patterns to recognize, systems to master, and challenges to overcome, the brain rewards that engagement with enjoyment. What we call “fun” can be described as the **neurochemical joy of understanding, of adapting, of leveling up in both knowledge and reflex**. A well-designed game sustains this fun by continuously offering just enough complexity to stretch the player’s mind and body, without overwhelming them. Thus, when we design systems and pacing for skill progression and cognitive evolution Engineering deep fun— where fun is repurpose to learning that sticks, transforms, and empowers.

Not only so, but there exist MANY models of learning, to each of their own objective. Cognitive Load Theory teaches us how to manage complexity, and if your game is naturally complex (which is a bad thing - some gamers love complexity! hence a flavor of fun - e.g. paradox studios!), we can ground our systems to Cognitive Load Theory, where we design systems/levels where we gradually introduce mechancis. For example, Designing jump + dash + enemy + timer not an ideal humanely design, we may introduce jump in the first level, then jump and dash for several, then introduce them to a challenge - enemie! And for the final trial, peraphs a time-based mechanic, to see their efforts of learning to go in vain - this itself is how we can structure narrative and learning together - mastery learning, where we introduce pacing, systems, and level that trains the player for that mastery challenge of defeating a boss.

Take, for example, a 100-player medieval RTS war simulation. Every player starts as a lowly peasant, performing basic grunt work like gathering resources and hauling them back to the kingdom. It’s a simple, accessible role. But as the game unfolds, they begin to witness something profound: other players, further along the progression curve, engaged in vastly different roles—tacticians, commanders, diplomats, siege engineers. They observe new playstyles, behaviors, and systems that feel distant yet intriguing. Alas, gathering wheat for the kingdom is not a primitive function, but an initiation into the intricate systems that govern the world by the designers. What seems like a simple task—harvesting, hauling, delivering—quietly teaches players about logistics, roles, and contribution to a larger economy. But the real revelation comes not from the task itself, but from what the player sees while performing it. As they traverse the kingdom, wheat sack on their back, they pass by others—archers drilling on the barracks field, engineers fortifying the outer wall, merchants bartering at the caravan gates.

Hence, The player doesn’t need a tooltip explaining “you can become a commander”—they see it. And in that moment, the world silently tells the new players - there is more, and it’s within reach. Thus, Cognitive Apprentenchip elegantly introduces players the world that thematically aligns with the story.

Now, let’s shift over to Gagné’s 9 Events of Instruction—a model originally designed during wartime. Tasked with rapidly training soldiers, Robert Gagné needed a system to deconstruct learning progression into concrete, actionable steps. The most pragmatic of the leraning models, as it was ission-critical. In a context where lives depended on competence, the model had to be efficient, repeatable, and performance-oriented. Naturally, we can use this model to our own advantage - in games!

Gagné’s 9 Events of Instruction is a step-by-step model. It begins with gaining the learner’s attention, using stimuli like visual cues, narrative structures, or dramatic tension to capture focus. Next, it informs the learner of the objective, clearly stating what they are about to learn or achieve. Then, it stimulates recall of prior knowledge, helping the learner connect new information to something familiar. The fourth step is to present the content, introducing the new concept or mechanic in a clear and accessible way. This is followed by providing guidance, such as hints, demonstrations, or supportive cues to help the learner succeed. After that, learners are encouraged to perform the task themselves, putting the concept into practice through direct interaction. The model then emphasizes the importance of feedback, offering immediate and informative responses to the learner’s actions. Once performance is demonstrated, the system should assess the learner, ensuring the concept has been understood and applied correctly. Finally, it focuses on enhancing retention and transfer, allowing the learner to apply the knowledge in new or future situations—ensuring the learning sticks and becomes part of their toolkit.

In BRAINROT, we apply Gagné’s 9 Events of Instruction to structure the onboarding experience. The game begins with a cinematic cutscene—a chaotic, overstimulated montage that grabs the player’s attention (Event 1). As it ends, a cryptic narrator informs the player of their objective: “Escape the noise. Find the signal.” (Event 2). Once control is given, the player recalls basic movement and interaction through environmental cues (Event 3), and is introduced to the gameplay loop via their first corrupted microtask (Event 4). Subtle prompts, NPC behavior, and visual indicators offer guidance (Event 5), leading the player to perform the task themselves (Event 6). Immediate feedback—visual glitch effects, brainrot meters, or voiceovers—responds to their performance (Event 7), followed by a slightly more challenging version of the task to assess understanding (Event 8). The skills learned here carry forward, forming the foundation for surviving and progressing through the game’s escalating stimuli (Event 9).

In skill-based competitive games like *Overwatch*, *CS:GO*, and *Marvel Rivals*, the Dreyfus Model of Skill Acquisition offers a powerful framework for understanding how players evolve from rigid, rule-following novices to fluid, intuitive experts. These games are designed to support this progression by introducing mechanical systems—such as aim precision, movement tech, ability timing, and map awareness—in ways that first rely on explicit tutorials and visual cues, then gradually fade scaffolding to allow for emergent, experience-driven mastery. As players move through stages of competence, they shift from consciously executing learned techniques to instinctively reacting within dynamic, high-pressure scenarios. Game systems such as ranked ladders, replay tools, challenge modes, and open-ended ability design foster this growth, allowing players internalize skill through repetition, reflection, and real-time adaptation—ultimately embodying the kind of deep, flexible mastery that defines high-level competitive play.

Naturally, we can design systems in that transitions the gamers within the dreyfus stage of skill progression. Designing systems that emphasize the Dreyfus Model of Skill Acquisition, we must build experiences that support each stage of player expertise—from novices needing rule-based guidance to experts relying on intuition and pattern recognition. Early stages require structured tutorials, visual cues, and limited complexity to foster foundational understanding. As players progress, systems should gradually remove scaffolding, introduce meaningful variation, and encourage experimentation, failure, and reflection. Competent players benefit from feedback-rich tools like replays or heatmaps, while proficient players thrive in open-ended, emergent systems where mastery comes from adapting fluidly to dynamic situations. At the expert level, systems emphasize creative freedom and high-skill expression, removing constraints and letting mastery speak through action. When done effectively, game mechanics become instruments for skill embodiment, turning systems of control and feedback into pathways for deep mastery. As players progress from rule-based execution to fluid, intuitive action, the game itself transforms—from a surface-level experience into a medium for expressing learned expertise. This transformation taps into something primal: the innate human drive to master one’s environment, to overcome challenge through growth, and to act with seamless fluency in the face of chaos, thus unlocks the true meaning of play.

In the end, designing games is fundamentally about designing for human learning. Players, by definition, are not passive consumers of content—they are active learners navigating, testing, and mastering content that are complex systems. Whether through constructivist engagement, experiential feedback loops, or carefully sequenced instruction like Gagné’s 9 Events, players learn by doing. They reflect, adapt, and grow—not just in skill, but in understanding. We’ve seen how models like Bloom’s Taxonomy help us structure the progression from novice to expert. How Cognitive Load Theory helps manage complexity across levels. How Cognitive Apprenticeship allows us to design emergent, socially situated learning moments without breaking immersion. And how Gagné’s model provides a practical blueprint for structured onboarding and tutorialization. Lastly, how systems can be designed to guide gamers into Dreyfus Skill Model Tools, scaffolding them into mastery of our design. Thus, we can wield these learning models to make games more accessible, more meaningful, and more human-centered.

Because play is learning. Every jump, every menu, every strategy is part of a greater feedback system in which the player learns not only how to play, but who they are within the world you’ve created. When we acknowledge this, we stop thinking of tutorials as some mundane boring phase in a game, and see it as a ritual, where players are invited into the magic circle of the game. It is in this moment—not just through explanation, but through experience—that the player becomes part of the system. They are being initiated into a world of meaning, mechanics, and mastery.

Exercises

1. [🟢 Remember] Recall a Learning Moment
Think back to a time when you learned something entirely through experience (not through formal instruction). What activity was it? What triggered the learning? Write down what you observed, how you reacted, and what you ultimately learned.

2. [🟢 Understand] Learning The Magic Circle
In your own words, describe what the "magic circle" of a game is. Why is this concept important when thinking about onboarding, immersion, and learning through play?

3. [🔵 Apply] Identify a Learning System in a Game
Choose a game you’ve played and describe a system within it that teaches the player something—either explicitly (through tutorials) or implicitly (through design or feedback). What cognitive learning model does it align with (e.g., Bloom, Gagné, Dreyfus)?

4. [🔵 Analyze] Compare Two Onboarding Systems
Pick two games with different tutorial styles (e.g., *Celeste* vs. *Elden Ring*). How do they approach onboarding differently? Which learning model(s) do they reflect? How does each one help the player construct understanding?

5. [🟠 Evaluate] Critique a Game’s Learning Curve
Pick a game where the learning curve either felt perfect or frustrating. What made it feel that way? Was it the pacing, complexity, scaffolding, or feedback system? Use a model like Cognitive Load Theory or Gagné to frame your critique.

6. [🔴 Create] Design a Gagné-Based Onboarding Sequence
Choose a fictional mechanic (e.g., time rewinding, emotion sensing) and write a 9-step onboarding sequence based on Gagné’s Events of Instruction. Make sure to design it in a way that feels immersive, not instructional.

7. [🔴 Create] Craft a Dreyfus-Inspired Skill Progression
Design a small game or minigame where the player moves from novice to expert in a single mechanic. How does the system change to support each stage of the Dreyfus model? What scaffolding is given—and when is it removed?

8. [🔴 Create] Build a System That Teaches Through Play
Invent a game concept that is secretly a learning engine. The player believes they are playing—but in reality, they are being guided through a layered system of skill acquisition, emotional understanding, or cognitive development. Describe the core loop and how it uses feedback to teach.