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Four critical ways AI is impacting learning and development

Artificial intelligence (AI) initiatives are of growing importance for many business functions, including learning and development. Availability of both internal resources and external services that leverage large data streams have grown, particularly for enterprise. And some learning teams are using these tailwinds to hit audacious impact goals.

AI initiatives related to knowledge work generally tend to support two types of activities.

  • Automatically surfacing insights or recommendations from vast real-time data sources via machine learning, natural language processing, or machine vision (among others)
  • Accelerating the creation of media outputs through generative AI

While not all use cases for AI in L&D have reached full maturity, the step change in productivity and bandwidth provided by AI provides a real first-mover advantage to teams that can conceive a solid use case.

In an effort to support agility, scalability, and personalization in learning, we’re pleased to present the ways in which we’re seeing learning teams leverage AI here at Arist.

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Understanding learning and skill gaps

For many learning teams the days of relying solely on quarterly or annual needs assessment are long past. But with greater velocity of learner data comes additional challenges. Namely, many data sources surface the need to be able to monitor and extract meaning from data at scale. Machine learning-based processes provide the ability to take in multivariate input across business function, employee engagement, and learning tools to surface insights and needs.

The construction of learner profiles can reduce redundancy and more efficiently route learning that is needed to the right learner. Arist supports the needs of individual learners by supporting agile learning cadences through near live-time feedback from learners and the ability of learning teams to rapidly produce new and pertinent content.

Adapting to individual learning styles

This cluster of AI support is all about providing the right content, at the right place, at the right time.  From our internal benchmarking, many corporate learning teams have up to 50 learning tools. A variety of delivery methods and content types can be a strength, but can quickly become an overwhelming task for learning teams (or worse yet, learners) to tackle.

Arist supports effective behavior change for a wide variety of learning styles by removing barriers to learning and injecting learning into tools already used by 91% of the planet. Intelligent automations ensure that learning is spaced in delivery for retention while nudges and reminders ensure that actual actions are taken.

Utilizing qualitative learner input

Qualitative application of knowledge is a great fill-in for assessing behavior change. If you have the tech stack to wrangle it at scale.

Natural language processing is a subset of artificial intelligence that can take in unstructured data such as written sentences and extract insights at scale. Think of this as a capability set that could turn a large collection of email messages into a spreadsheet filled with meaningful fields.

The use of qualitative input greatly expands the ways learning teams can interact with learners. At Arist we support qualitative feedback by delivering in messaging apps where learners are enabled to quickly and informally respond to prompts. Qualitative feedback enables a new set of L&D metrics and is part of a cohort of metric types we suggest for learning teams to track more closely with business outcomes.

Creating learning content more quickly

Messaging app-based learning delivery is particularly ripe for levelling up content creation with AI

Generative AI is trained on large corpuses of text to predict what the “perfect” next word in written body of work should be. You may have seen videos of tools like Chat GPT, Jasper, or others. And some learning teams are utilizing these tools to boost the speed of their curriculum content creation. With greater speed, learning content gains relevancy to rapidly changing conditions. Additionally, content can be tested and iterated on more quickly.

Arist helps learning teams to save an average of 82%  on learning content creation (time and costs). Messaging app-based microlearning provides an ideal form factor for improving content creation speeds even more with the use of generative AI.

AI for L&D takeaways

Increasing learner touchpoints and impact-related L&D metrics have placed L&D at the intersection of many data streams ripe for AI disruption. Generative, insight-centered, or recommendation-centered uses of AI offer step changes in learning team productivity that can set your team up to drive impact. While a growing suite of services enable teams to enjoy the benefits of AI with little-to-know configuration on their end, it’s still useful to keep a few tips in mind:

  • Start small with a clear hypothesis and goal. Adjust accordingly as data comes in. For example: speeding up course creation will boost learner-reported “relevancy” of courses
  • Be aware of support costs. Not all AI is “plug and play.” Make sure to discern where a service falls in the build, buy, partner spectrum.
  • Open up to data from outside of L&D. While you may not have had the bandwidth to take on more data in the past, impact data often lives outside of L&D tools, and AI may help you to better wrangle diverse data streams.
  • Realize AI shouldn’t be a “black box.” Choose vendors who are willing to give you enough data on the inner workings of their product to make informed decisions about what precisely your AI-enabled tools are doing.

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