Why simulation-based learning is becoming AI-resistant by design
In times of rapid advances in artificial intelligence, a fundamental shift is taking place in higher education. As AI systems become increasingly capable of generating analyses, solving structured problems, and supporting managerial decision-making, the question is no longer whether students can arrive at the right answers. Instead, the question is whether they are engaging in forms of learning that cannot be easily replicated or outsourced.
Simulation-based learning is gaining renewed relevance in this context.
Unlike traditional assignments, simulations introduce a layer of interdependence that is inherently difficult for AI to resolve. Outcomes are not only determined by models or data, but by the decisions of multiple teams acting simultaneously. Each decision influences the environment in which others operate, creating feedback loops that evolve over time.
While AI can optimize within a predefined system, it struggles to anticipate how that system will change when multiple actors interact dynamically. This makes simulations one of the few educational formats where learning remains fundamentally human: shaped by judgment, timing, interaction, and uncertainty.
A closer look at widely used simulations such as Glo-Bus, Capsim, Marketplace Simulations, Cesim and Markstrat reveals that they differ significantly in what they actually model and therefore in what they teach.
Comparative overview of widely used simulations
| Simulation | Organizational perspective | Thematic focus | Primary decision domain | Role of innovation | Learning orientation | Level of abstraction | Type of complexity |
|---|---|---|---|---|---|---|---|
| Glo-Bus | Product & market positioning | Competitive strategy in technology markets | Pricing, production, product features, market positioning | Implicit (via R&D and product design) | Competitive performance and market positioning | Medium | Financial and market-driven |
| Capsim | Functional business system | Integrated firm management | Cross-functional decisions (R&D, finance, marketing, operations) | Partial | General management and business acumen | Medium | Analytical and data-intensive |
| Marketplace Simulations | Customer & market interface | Marketing strategy and customer behavior | Product launch, branding, segmentation, pricing | Limited | Customer insight and market strategy | Medium | Market dynamics and positioning |
| Cesim (Global Challenge) | Strategy & external environment | Global strategy and sustainability trade-offs | Global expansion, strategy, sustainability decisions | Emerging | Strategic decision-making in global contexts | Medium–high | Strategic and financial |
| Markstrat | Product portfolio management | Marketing strategy and product lifecycle management | Product portfolio, segmentation, R&D | Moderate | Market strategy and lifecycle management | Medium | Analytical and market-based |
| Innovation Management Game (Innovative Dutch) | Innovation system & organizational design | Managing exploration vs exploitation and innovation portfolios | Portfolio balance, resource allocation, long-term alignment | Central and explicit | Innovation capability and strategic alignment | High | Systemic and strategic |
Two fundamentally different logics
At first glance, these simulations appear structurally similar. Teams make decisions in iterative rounds, receive feedback, and compete for performance. Yet beneath this shared format lie two fundamentally different logics.
Most simulations, including Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat, model the organization as a firm operating within a competitive market. The task of the participant is to improve performance within that environment. Success is typically measured in terms of profit, market share, or strategic positioning.
In contrast, the Innovation Management Game operates at a different level. Rather than simulating the market itself, it focuses on how organizations design and manage their innovation system over time.
Most simulations model the outcomes of innovation: products, markets, and performance.
The Innovation Management Game models the system that produces those outcomes.
What becomes visible in practice is that simulation-based learning is not only about making decisions, but about experiencing how those decisions interact over time. Participants are confronted with uncertainty, conflicting priorities, and the need to align short-term actions with long-term strategy.
This is where the difference between market-based simulations and innovation-oriented simulations becomes tangible. Rather than optimizing isolated decisions, participants must develop coherence across their choices and understand how their actions shape the system as a whole.
The Innovation Management Game explicitly focuses on this dynamic, placing participants in a setting where innovation is not a single decision, but an evolving system of trade-offs.
Market-based simulations: optimizing within a given system
Simulations such as Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat are rooted in a paradigm of optimization. The organization is treated as a system that already exists, and the role of the participant is to make better decisions than competitors within that system.
Glo-Bus illustrates this clearly. Although positioned as a technology design simulation, participants primarily configure products and compete on price, features, and market positioning. Innovation is present, but mainly as a function of product development.
Capsim extends this by integrating multiple business functions. Participants must align decisions across departments, interpret financial data, and manage trade-offs between growth and profitability. The complexity increases, but remains analytical in nature.
Marketplace Simulations emphasize customer-driven decision-making, focusing on segmentation, branding, and product launches. Cesim adds layers of international strategy and sustainability, while Markstrat deepens understanding of portfolio management and product life cycles.
Across these simulations, a consistent pattern emerges: complexity arises from the number of variables and the need to interpret data. The challenge is to optimize performance within a relatively well-defined system.
Designing the system: a different level of decision-making
The Innovation Management Game starts from a different premise. Instead of assuming a fixed system, it focuses on how that system is designed and evolves.
Participants are not primarily concerned with pricing or production decisions. Instead, they must address questions such as how resources should be allocated between exploration and exploitation, how balanced the innovation portfolio is, and how short-term results relate to long-term renewal.
These decisions are inherently systemic. Their impact is often indirect, delayed, and dependent on how different elements of the organization interact over time.
As a result, the nature of decision-making changes. There is no single optimal solution. Instead, participants must develop coherence in their choices and understand how those choices shape the organization’s ability to innovate.
Analytical versus systemic complexity
This difference is most clearly visible in the type of complexity each simulation introduces.
Market-based simulations are analytically complex. Participants are presented with large amounts of data and must identify patterns, calculate trade-offs, and make decisions that optimize performance indicators. The challenge is primarily cognitive and quantitative.
The Innovation Management Game introduces systemic complexity. Outcomes depend on interactions between decisions, timing, and alignment. Cause-and-effect relationships are less direct, and success depends on the ability to understand and manage interdependencies.
This distinction is increasingly relevant. While analytical complexity can often be supported, or even partially automated, by AI, systemic complexity remains difficult to externalize.
Abstraction and learning outcomes
Another key difference lies in the level of abstraction.
Most simulations operate at a relatively concrete level, focusing on products, markets, and financial results. This makes them highly effective for developing business acumen and functional knowledge.
The Innovation Management Game operates at a higher level of abstraction. It focuses on capabilities, processes, and strategic alignment. This allows participants to explore how organizations create value over time, rather than how they optimize performance in a given moment.
The learning outcomes therefore differ. Market-based simulations are effective in developing business acumen and functional knowledge. Simulations focused on innovation systems contribute to a broader understanding of strategy, organizational design, and long-term value creation.
Implications for education
These differences are not merely technical. They reflect broader shifts in how organizations are understood.
Traditional simulations align with a view of organizations as entities competing in relatively stable environments. Success depends on efficiency, optimization, and informed decision-making.
More recent approaches reflect a view of organizations as adaptive systems operating under uncertainty. Here, success depends on the ability to innovate, experiment, and continuously rebalance competing priorities.
In this context, simulation-based learning is not only a way to teach business fundamentals, but also a way to engage with the limits of those fundamentals.
Conclusion
Business simulations are often treated as a single category, but a closer analysis reveals that they operate at fundamentally different levels.
Simulations such as Glo-Bus, Capsim, Marketplace, Cesim, and Markstrat provide valuable insights into how organizations perform within markets. They strengthen analytical thinking, financial reasoning, and competitive decision-making.
The Innovation Management Game operates at a different level. It focuses on how innovation systems are designed, balanced, and evolved over time, shifting attention from outcomes to the mechanisms that produce them.
In an AI-driven world, this distinction becomes increasingly important. As analytical tasks become easier to automate, the ability to think in systems, navigate uncertainty, and design for long-term innovation may well become the defining capability for future professionals.
The question for educators is therefore not which simulation is best, but what kind of thinking they want to develop.
