Bridging the gap between learning and application has been a long-standing challenge for business leaders, especially in high-risk or high-stakes roles with dire consequences. Employers must enable their teams to develop market-ready skills through experiential learning methods, and today’s tech puts the advanced tools needed to do so at their fingertips.
Skills Practice learning is pioneering the transition from theoretical learning to active learning. Recreating real-world scenarios instead of simply hypothesizing about them allows for training initiatives that are more informed, more immersive, and more effective. Using simulations for skill-building isn’t a new concept, but recent breakthroughs and widespread adoption of AI have unlocked new potential for this type of learning solution.
AI’s Role in Skills Practice Learning
With its unprecedented ability to collect, analyze, and interpret data, AI can customize the design and development of learning experiences with little to no lift compared to traditional learning models. This not only lessens the workload on human staff when developing these initiatives but also maximizes speed-to-performance because participants learn at a faster pace.
What is “AI-supported, human-centered” design?
Human behavior is at the center of learning. Leaders must first prioritize creating and fostering an environment in which their workforce can perform their best, then optimize those elements using modern technology. Determine what success means to your team, and how you can help them achieve it. Then, you can identify what tools (namely, specific AI models) will best align with those requirements to further enhance training initiatives. The most effective and efficient learning models put people first, then strategically leverage technological capabilities like AI to enhance human efforts and fill the gaps.
In AI-supported, human-centered learning design, the designer trains the AI model on how to teach learners through cyclical feedback. As the participant learns from their mistakes and adjusts their responses accordingly, the AI model learns from the participant how to best relay new information for maximum understanding and retention.
3 Benefits of Skills Practice Learning Design for L&D
Scenario-based learning simulations mimic real-life circumstances without the risk of making real-life mistakes. Plus, they make learning more immersive and thus more engaging by “crafting gamified journeys where learners solve puzzles and level up their skills,” according to our own Mike Vaughan, CEO of the Regis Company. Pairing this type of safe, receptive, and sometimes even fun environment with help from AI means participants can practice learning by doing through training initiatives that:
1. Limit stress and institutional pressure.
If you’ve ever participated in a performance evaluation or taken a standardized test, you’ve likely felt what’s commonly referred to as “exam anxiety.” For learners, one advantage of using AI simulations for corporate training is that it minimizes the stress that tends to accompany exercising an unfamiliar skill, particularly in the presence of an authority figure. Learners today are on the cusp of needing to reskill and upskill at a rapid rate, so being able to engage in skills practice is essential.
Learning led by an AI model as opposed to, say, a member of the company’s leadership team effectively removes the fear of human judgment and lessens pressure on the learner. Because they can freely make mistakes and learn from them, learners have more confidence when they apply their skills in the field. Some more advanced AI learning models can even classify a learner’s emotions with up to 70 percent accuracy. This enables the AI to determine if the learner feels particularly frustrated or discouraged and adjust lesson plans accordingly.
2. Provide personalized feedback.
Another advantage of AI-supported skills practice learning is its ability to provide personalized feedback through adaptive assessments. Think of these models as personal learning assistants—they analyze participant responses to identify strengths and pinpoint shortcomings, tailoring the program to align with certain focus areas and difficulty levels.
Personalized feedback also gives the learner more control over their learning journey. Adaptive assessments allow participants to learn at their own pace, more easily track their progress, and focus on specific areas of interest.
3. Report and interpret assessment results with ease.
No longer must leaders subject learners to one-size-fits-all evaluations. Instead, leadership learns how to use and interpret the unique results of AI assessments, resulting in more informed decision-making.
Properly integrating AI tools into training practices means leadership teams can better understand the ideal learning environment for their participants. This visibility allows learning leaders to support effective professional development and boost a team’s speed-to-performance.
Relevant Applications of AI-Supported Skills Practice Learning Design
AI-powered training simulations can be used across industries to prepare learners for what could happen in the field and help them build market-ready skills. These in-demand proficiencies include both the hard and soft skills necessary to succeed in the workplace and keep up with the latest trends.
Some real-world examples already in use include:
- Conversation simulations: Contact centers employ human agents to answer and respond to customer inquiries. Recently, many contact centers have implemented AI-powered conversation simulations to train their agents in a hyper-realistic setting. This allows inexperienced agents to practice and refine their communication skills before they even pick up the phone, better preparing them for customer-facing situations.
- Leadership development simulations: Leadership skills (e.g., conflict management, team-building, and decision-making) are some of the most highly sought-after capabilities by modern employers. However, such skills are often considered hard to teach as they typically require diverse field experience. AI-supported learning simulations enable leaders to instill this invaluable experience in their learners through situational programming that adapts to learner aptitude and preferences.
- Sales and negotiation simulations: Sales is another area in which team members usually need a robust foundation of real-world experience and practice perfecting their craft. With human-centered, AI-supported simulation learning, salespeople can freely practice their techniques and selling points with a digitized learning assistant, refining these skills without risking business opportunities.
How to Design Skills Practice Learning for Maximum Effectiveness
High-level learning and corporate training initiatives were formerly reserved for large corporations and those with the capital to invest in costly technology. However, recent advancements in AI have made these avenues for learning more accessible to a wider range of organizations, both in terms of cost and implementation.
The Regis Company’s Design Co-Author is one example of this type of technological breakthrough. This powerful tool works within the SimGate™ Skills Practice Platform to develop skill practice experiences for any audience, industry, or challenge in days instead of weeks. Plus, the design co-author is human-centered, so you have complete control over the content.
