Learning and development professionals are likely to see descriptions of eLearning apps, platforms, and content touting their built-in AI. But sorting out the truth from the hype and understanding how AI is affecting eLearning is a daunting task.
This article offers an overview of the ways that AI intersects with and impacts eLearning. For a deeper look, download Neovation’s new white paper, AI in eLearning 2020: Demystifying Artificial Intelligence and Its Impact on Digital Learning.
AI can loosely be understood as tasks performed by technology that seem to use human-like intelligence or skills. These can range from automated scheduling and reminders to tools that create content.
AI is built on algorithms — rules or processes — that a machine follows to perform a task. When AI is enhanced with “machine learning,” the algorithms use feedback from performing the tasks and input from humans to improve their ability to do the task — they learn how to improve.
L&D professionals who develop or use eLearning platforms and authoring tools do not have to create their own AI-based tools. AI-based technologies can be licensed for use within a software platform and are built into some eLearning authoring tools and platforms. AI technologies based on Amazon (AWS), Google (GCP), IBM (Watson), and Microsoft (Azure) are commonplace in eLearning and other online tools.
AI-based tools change the way people use technology, the way they work, shop, find information, and learn. It also changes the tools that L&D professionals use and how they create and deliver eLearning content. Key areas where AI is making a difference include:
AI-based tools might generate alt text or closed captions and transcripts of video content. Spelling and grammar checking tools might improve the readability of content. Automated translation enables L&D teams to more easily, quickly, and inexpensively create content for multilingual learner populations.
AI-based tools might automatically tag content and power recommendation engines that suggest content to learners based on their interests, performance, job roles, or prior training. Adaptive training uses algorithms to deliver specific content to individual learners based on their mastery goals and performance.
Predictive analytics that use learner data can help L&D teams improve both the quality and the effectiveness of training. This could entail mapping data on training history and performance to job performance data to identify skills or knowledge gaps. Or it could use analysis of customer and employee communications to identify common areas of confusion and create or improve training in those areas.
AI excels at pattern detection. An AI tool can analyze enormous amounts of data very quickly and find patterns and correlations that human analysts would likely overlook — or simply would not process enough data to identify.
AI tools can also automate routine tasks and free up human L&D experts to focus on the more creative aspects of their work. Rather than spend hours each week enrolling learners, reminding them to complete training, and looking up who is and is not progressing, those tasks can be automated to produce daily or weekly reports. The L&D team can focus on writing content or study the reports to figure out which assessment questions need to be improved or what topic areas need additional or improved content.
Download the white paper today for a deeper look at how AI and eLearning intersect.
Simple games layered on top of content
Scenario-based games that use the content
Fan excessive competition among employees or teams by offering large prizes for top performers and/or shaming those with lower scores
Challenge employees to beat their own past performance, or design a leaderboard that shows each employee only the four scorers above and below them
Points, rewards, badges
Award points or levels for completing sections of training or playing for a set number of minutes
Award levels, badges or points for recalling or applying content correctly, demonstrating mastery
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