In "The Matrix," Keanu Reeves' character, Neo, is able to plug himself into a virtual world, complete with a digital representation of himself. This digital version can get "upgrades" such as downloading the skills needed to be an expert at kung fu or to fly a helicopter. While we can't promise that acquiring skills will ever be that easy, the increasing use of simulations and digital twins highlights how being able to train in realistic ways can help power improved learning outcomes.
Digital twins are virtual representations of physical systems, modeled in ways that can be updated as new information comes in. For example, a digital twin of a manufacturing plant doesn't just show a static 3D model—it reflects real-time data about machine performance, inventory levels, and production rates. In learning environments, these twins can represent everything from complex mechanical systems to human interactions, creating rich, dynamic environments for practice and experimentation.
Let's focus on two of the areas where digital twins seem poised to have their biggest impact on education: simulating training scenarios and combining with learning analytics to create more adaptive systems.
Training simulations: learning while doing
As a simulation, digital twins can provide a safe way to try out new skills and activities that might be hard to replicate in the real world. Medical students, for example, can practice procedures on digital patients that produce responses based on actual physiological data. Project managers can run complex scenarios where virtual team members, resources, and timelines interact in realistic ways. Sales professionals can practice negotiations with AI-powered clients that exhibit different personality types and buying styles. HR specialists can work through difficult conversations with virtual employees, practicing everything from performance reviews to conflict resolution. While not necessary to make these kinds of experiences effective, new technologies like virtual reality can make simulations like these more immersive and engaging.
These environments create safe spaces for the kind of "productive failure" that learning sciences have shown can be beneficial for developing a deep understanding. This research finds that carefully constructed training sequences can help people start to pick out the most critical elements needed to build a richer conceptual understanding. For example, by asking learners to work through a series of exercises that help them isolate important versus surface features of a problem, they begin to develop the kind of knowledge that separates novices from those on the path to expertise.
Powering these kinds of simulations are, generally, three different "models." These are data-driven engines, sometimes powered by AI, that are used to track current states, predict future ones, and make recommendations:
- The environment model: This tracks the "world" of the scenario—the patient's vital signs, the factory’s processes and inventory, the project's resources and timelines, or the market conditions in a business simulation.
- The learner model: This monitors how the person interacts with the system, tracking such things as decision patterns, common mistakes, and successful strategies.
- The intervention engine: This combines data from both models to generate personalized learning experiences, adjusting difficulty and introducing new challenges based on the learner's progress.
Learning analytics: from monitoring to prediction
This is where the element of learning analytics comes into play. With these complex models, more powerful and interesting forms of interventions can be created. By analyzing patterns across thousands of learners, systems can identify common challenges and test different interventions to address them. If data show that many learners struggle with a particular concept, the system can choose different explanatory approaches until it determines which is the most effective.
Think about a financial analyst in a course on spotting market risks. The system might notice that they consistently miss certain types of warning signs in market data. It could then generate new scenarios that specifically highlight these patterns, gradually increasing the subtlety until the analyst develops stronger recognition skills for these kinds of situations. The system might also track that this particular learner has, in the past, done better with graphs rather than numerical tables. It could assist the learner by presenting a graph or up the challenge by giving the analyst the numbers and asking them to try to detect the pattern there.
Early-warning systems represent another powerful application of learning analytics. By monitoring how learners interact with digital twins—their problem-solving approaches, time spent on different tasks, error patterns—the system can identify struggling students before traditional assessments would catch issues and provide a customized learning path.
A welcome addition for learning
There will always be a place for traditional educational experiences, focused on conveying information in useful, applicable, digestible ways. That said, simulations that provide rich, interactive experiences that adapt to each learner's needs can be a welcome addition, particularly when they provide practice in ways that might otherwise be difficult to generate. While we may not be able to download kung fu skills like Neo, we're moving toward a future where learning complex skills becomes more engaging, effective, and personalized than ever before. And, like with a lot of technologies powered by vast data sets, there is a possibility for a positive feedback loop to emerge: As more people use these systems, they become better at predicting challenges and generating effective learning experiences, making complex learning more accessible and efficient for everyone.
