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The Future of Learning With AI: Part 1

Artificial Intelligence (AI) will be a critical element to help Corporate L&D teams provide their companies’ with the relevant support needed in the post-pandemic “new normal” of virtual and self-paced learning. AI can help to ensure that L&D programs and their associated processes are objective, bias-free and data driven. AI and Machine Learning (ML) technologies are game-changers for the corporate L&D function, and they promise to transform learning for years to come.  

What Are AI and ML? 

AI refers to intelligence demonstrated by machines. In eLearning, AI is incorporated into machines and systems to perform tasks otherwise completed by humans. In doing so, an eLearning consultant can use a machine or system equipped with AI to quickly resolve problems and speed up and improve their everyday operations.   

ML is an AI application in which machines and systems use algorithms to automatically learn. Over time, a machine or system equipped with ML capabilities will pick up on patterns and trends and learn from its experiences. This ensures that the machine or system is continuously learning and using insights to find ways to optimize its performance.  

Common Algorithm Classifications of ML 

Algorithm classification plays a pivotal role in the use of ML in eLearning. An algorithm is classified based on the predictive calculations used to assign data to various categories by analyzing specific datasets. In doing so, algorithm classification ensures that an ML machine or system can use the right data, to perform the right task, at the right time, every time.  

Common AI and ML algorithm classifications include:  

  • Logistic Regression: Predicts a binary outcome (pass/fail, something happens/does not happen, etc.) 
  • Naïve Byers Classifier: Determines if a data point belongs to a category; to do so, it uses words or phrases to categorize words to a specific tag (classification) 
  • K-Nearest Neighbors: Uses training datasets to determine the value of “k” relative to its nearest neighbor; for instance, if k=1, it would be placed in a classification as close to 1 as possible 
  • Decision Tree: Separates data points into two separate categories simultaneously that become similar as they expand; decision trees often replicate flow charts used by an eLearning consultant and other eLearning professionals 

Proper ML algorithm classifications are key. But, for an eLearning consultant who understands the value of AI and ML, he or she can establish appropriate ML algorithm classifications  and get the most value out of their AI and ML investments.  

Benefits of AI and ML for L&D Organizations 

L&D professionals should consider AI and ML for a variety of reasons, including: 

  • Personalization: Can evaluate a learner’s accomplishments and goals and provide personalized learning content  
  • Resource Allocation: Can provide exceptional learning content faster and more efficiently than ever before 
  • Automation: Can automatically generate unique learning course maps for learners and adjust learning course materials on the fly 
  • Learner Motivation: Can deliver individualized eLearning materials that keep learners engaged and motivated 

Clearly, there is a lot to like about AI and ML in learning  so how can you incorporate AI and ML into your learning strategy? Stay tuned to part of our blog series to find out more.  

In the meantime, we encourage you to keep in mind that LTS provides instructional design and L&D services to help you incorporate AI and ML into your eLearning materials. For more information, please contact us today.   

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