How to Create Successful Simulations for Higher Education

Universities drive intellectual progress through invention, discovery, and the communication of ideas. But how the ideas are communicated shapes how well they’re understood.

Higher education has historically relied on static or fixed formats–lectures, slides, textbooks, and discussion-based analysis–to explain increasingly complex systems. That approach works well for transferring information. It’s far less effective for helping students understand how real-world systems behave when decisions, uncertainty, and tradeoffs come into play.

That’s where simulations excel.

Every communication technology–be it the printing press or the lecture–encourages the expression of some ideas while constraining others. When students read or listen, they learn by consuming static representations of information. Simulations extend learning by making the concepts more tangible, allowing ideas to be explored dynamically.

For more than two decades, Forio has worked with educators and institutions to use simulations to teach subjects where outcomes emerge from interconnected choices, including business strategy, economics, leadership, operations, and policy. While the technology has evolved, the core strengths of simulations have remained the same.

At their core, effective learning simulations share three defining characteristics: they are computational, responsive, and connected.

Simulations are computational. They can emulate systems that exist in the real world, including environmental, political, economic, and business systems. Instead of explaining these complex processes through words alone, simulations provide a model that students can explore themselves.

Simulations are responsive. Unlike a book, simulations can respond to outside stimuli and adapt to student decisions. When faculty or students interact with a simulation, they create scenarios and can analyze a range of decisions and the outcomes linked to those decisions.

Simulations are connected. Unlike traditional media, simulations support information sharing. If students want to examine the effect of a policy change, they can model it in the simulation and share the results. This natural “show and tell” dynamic places depicting and describing on equal footing. Faculty can also aggregate results from many student runs to surface broader insights for the entire class.

The Real Design Constraint: Students and Faculty Have Different Needs

There are two primary users of a higher education simulation: students and faculty. Each has different needs, and a simulation must address both simultaneously to be successful.

What Students Want in a Simulation

• An enjoyable, engaging experience
• Learning that’s relevant, important, and meaningful
• Limited preparation time before playing 
• Familiar controls
• Control over their experiences

What Faculty Want in a Simulation

• Students gain relevant, meaningful learning
• Students are successful while enjoying themselves
• Simple preparation in advance of facilitating
• Ability to answer questions posed by the simulation
• Easy summarization of the class’s results
• Scaffolding to debrief the simulation

Simulation Complexity Does Not Always Lead to Success

Because students want simulations with clear goals and simple instructions, and faculty need experiences that are straightforward to explain and debrief, complexity is often a liability rather than an advantage in simulation design.

When faculty design simulation concepts, they are often drawn to sophistication. Since the real world is complex, realism is frequently treated as an indicator of quality, leading projects to build on more complexity in an effort to build a better learning experience.

In practice, simulations with a clear learning objective consistently perform better than those that attempt to replicate every aspect of real-world complexity. Simpler simulations are easier to learn, easier to facilitate, and more effective at focusing attention on the decisions that matter most.

Design Simulations for Clarity Before Realism

Real-world systems are complex, but successful simulations rarely attempt to reproduce that complexity in full. Instead, they focus attention on a small number of meaningful decisions and the relationships between them.

Clarity is a design choice. A simulation that tries to model everything risks obscuring the very dynamics it aims to teach.

When students are unsure what matters–or why outcomes change–the learning objective is lost. Help students understand what they are experimenting with by using: 

• Clear goals
• Simple rules
• A limited set of variables

For faculty, clarity also makes simulations easier to facilitate and debrief. When the logic of the system is transparent, instructors can spend less time explaining mechanics and more time helping students interpret results. In practice, simulations designed for clarity are more likely to:

• Be implemented
• Reused
• Make an impact
• Taught well

Make the Simulation Learning Objective Visible Through Play

Successful simulations do not hide the learning objective behind instructions or background readings. Instead, the objective is revealed through interaction.

Rather than telling students what they are supposed to learn, simulations allow students to discover it by making decisions and observing outcomes.

• Patterns emerge through play.
• Tradeoffs become apparent.
• Students begin to understand why certain strategies succeed while others fail.

This does not mean eliminating preparation entirely. Students still need enough context to engage meaningfully. But preparation should support exploration, not replace it. The goal is to create conditions where the learning objective becomes evident as students interact with the system, not after they are told what it was supposed to be.

When the learning objective is visible through play, students remain engaged and faculty gain confidence that the experience is aligned with their course goals.

Let Decisions Drive Outcomes

At their core, simulations are computational and responsive. This means outcomes change based on student input, not pre-scripted paths.

A successful simulation makes the relationship between decisions and consequences clear. Students should be able to see how their choices shape results over time. When outcomes feel arbitrary or disconnected from actions, simulations lose their educational value.

Responsive systems also allow students to test ideas. By running scenarios multiple times, adjusting inputs, and comparing results, students move beyond passive observation to active inquiry. They are no longer just learning about a system, they are actively experimenting with it.

This decision-driven structure is what differentiates simulations from static cases or examples. It allows students to explore uncertainty, complexity, and unintended consequences in a controlled and “safe” environment.

Utilize Interaction in Simulations Intentionally (Not Excessively)

Interaction is a powerful design lever, but more interaction does not automatically lead to better learning.

Some simulations benefit from collaboration or competition, where students compare results, negotiate decisions, or respond to one another’s strategies. Others work better as single-player experiences, allowing students to focus deeply on cause and effect without social complexity.

Successful simulation design treats interaction as a purposeful choice.

• Collaboration should support the learning objective, not distract from it.
• Competition can motivate engagement, but only when it reinforces the underlying concepts rather than shifting focus to winning.

Similarly, replay and scenario variation often matter more than the number of participants. Running the same simulation under different conditions and comparing outcomes can be more instructive than adding additional layers of interaction.

The key question is not whether students interact, but how that interaction advances learning.

Simulation Debrief is Where Learning Crystallizes

No simulation is complete without the debrief. It’s not an add-on, it’s a core design consideration. Simulations that lack structure for reflection often feel engaging in the moment but fail to produce lasting understanding. 

Research from the AERA on simulation-based learning consistently shows that instructional scaffolding–particularly during and after simulation play–is critical to helping learners translate experience into durable understanding. Without guided reflection, students may recognize what happened in a simulation without fully understanding why it happened or how those lessons apply beyond the exercise.

The experience itself generates data–decisions made, outcomes observed, patterns–but learning happens when students reflect on what those results mean. Debriefing connects experience to theory, helping students articulate insights and generalize lessons beyond the simulation.

For faculty, effective debriefing depends on access to results at both the individual and class level. Being able to compare runs, highlight trends, and surface unexpected outcomes allows instructors to guide discussion toward the learning objective.

When designed well, debriefing turns simulation play into insight–and insight into learning that transfers beyond the classroom.