Artificial intelligence (AI) is used across industries and transforming them rapidly. Corporate learning and development (L&D) is no exception; AI is increasingly being used to improve the learning ecosystem. Personalization is a key requirement for L&D, and AI has the potential to make a significant positive impact on it.

Challenges in Enabling Personalized Learning

Personalization is not new to L&D; adaptive learning management systems and content frameworks have been around for a while. However, the nature of work and the way employees learn has rapidly changed in the last few years, and these systems have failed to keep up with their needs. Learning resources are no longer limited to one or two systems, and learners are looking at several other options, many of which are outside the enterprise environment.

Recognizing these gaps, some organizations have acquired a number of different systems and libraries. The average large enterprise uses over 20 different learning tools or platforms, according to Josh Bersin. The ever-increasing number and type of learning resources spread across disparate sources make it difficult for learners to find relevant content and for the L&D to make learning work for the learners.

Many tech-savvy L&D teams have attempted to solve this problem by implementing a centralized content aggregation or indexing system. The L&D team defines a common taxonomy (set of metadata) and tags content from disparate systems uniformly to facilitate personalized content. These systems, however, have two important limitations: They need considerable human effort to curate content, and they lack context beyond the demographic information.

Large enterprises with high amounts of diverse content, job roles and learners are constrained by current solutions in managing and delivering truly personalized learning experiences. Following are the key parts of this puzzle and how L&D can leverage AI to create modern and personalized content, programs and experiences. It is important to note that there are no one-size-fits-all AI solutions. L&D must carefully evaluate the new initiatives around these solutions to avoid overkill.

Content Curation

Content curation is a big undertaking for L&D teams in large organizations due to the scale involved. AI and machine learning are helpful for solving this problem and reducing the human effort involved. There are reliable AI-based solutions now that can help scan content (be it text, images or videos), identify relevant assets, and automatically tag them. Such solutions will need data and assistance at the initial stages but, once trained, will offer tremendous scale and value.

Context-specific, Individualized Learning

Serving context-specific content to learners at their moment of need is a complex problem to solve with traditional systems. Apart from curation, there are two key areas that we need to address:

Analytics: We need to go beyond demographic data and learning history to truly individualize learning experiences. Collecting additional data points is crucial, and L&D teams can capture some of them through current systems and some through AI. AI can help you better understand aspects like where and how learners are engaging with the content, their cognitive and behavioral preferences, possible gaps and learning needs, and the impact of learning on retention and performance.

Recommendations: L&D can effectively use individual and collective (social) information about learners to deliver personalized recommendations. You can then add additional data points like organizational process shifts, regulatory changes and important yearly work events to this mix. Leverage AI to analyze behavior, identify patterns and predict potential needs of the learners by using the collected data sets (the way Netflix suggests content or Amazon recommends products).

Learning Experience Design

We need to think beyond building Netflix-like systems for learning and offer a more seamless learning experience to employees in the flow of their work activities. L&D can integrate AI-powered systems with enterprise tools (like Email, IM and collaboration platforms) to better understand gaps and offer specific learning recommendations within those systems.

AI is also enabling new learning experiences through chatbots and voice-based personal assistants. Organizations can integrate these conversational interfaces into enterprise systems to offer personalized coaching and performance support as and where they are needed. They can also help establish a better context where there are gaps in the collected data; learners are often happy to answer a couple of quick questions knowing that they will improve relevance.

The future of work is changing rapidly, and recent reports emphasize the urgency of reskilling and lifelong learning. The L&D function will play an important role in enabling this continuous and independent learning to meet business and performance objectives. There is no better time than now to critically look at the current learning ecosystem and transform it by leveraging the latest digital technologies, including artificial intelligence.

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