Smarter learning organisations: How AI is influencing corporate learning

By Kim Ochs*

Source: Unsplash (Andrew Neel)

Opportunities to learn, grow, and obtain certifications are increasingly becoming standard employment perks, particularly at corporations, large NGOs and established companies. A 2019 study from Sitel Group in the United States found that 37% of current employees said they would leave their job if they were not offered training to learn new skills. “Learning in the flow of work,” a term coined by global analyst Josh Bersin, has become a trend and offices of human resources (HR) are expanding to include learning and development (L&D).

Content management systems (CMSs), learning management systems (LMSs), learning experience systems (LXPs), as well as learning content providers (e.g. LinkedIn Learning, Coursera), provide the technical infrastructure. Artificial intelligence (AI) is an important enabling technology in all of these systems. With their very diverse products and services, some of these vendors talk about a “Netflix of learning,” while others find the term problematic. As with Netflix, there are four main functions of these organisational learning systems, backed by machine learning and AI: aggregation, exploration, optimisation, and recommendation.

Aggregation is the first step, which brings all of the content together. A CMS, such as Microsoft SharePoint provides a solution for storing, managing, searching, delivering and sharing files and information. A LMS is used to administer, deliver, track, and manage content specific to training and education (e.g. course delivery, tracking assignments, grading, etc.). Often, an organisational single-sign-on (SSO) solution is implemented to give an end-user the ability to seamlessly access all of the content residing in both systems. At this stage, AI might be used for advanced tagging for structured and unstructured content, or to classify and extract information and then automatically apply metadata.

Next is exploration—the ability to search and find what you are looking for by theme, topic, and a variety of other search terms. Search terms are built on a taxonomy, usually starting with the vendor’s own taxonomy and often incorporating terms provided by the organisation. For example, if the organisation has offices in multiple countries, each of which has its own specific induction programme, the organisation might include their list of countries as part of the taxonomy so people can easily search and find their country’s specific induction programme.

AI and machine learning make optimisation possible. This allows system administrators and L&D managers to see which content people are interacting with, how long they are engaging, and identify where improvements can be made. The specific approach, depth of learning, and level of analytics provided varies across vendors, as well as their focus, which can be a distinguishing factor in system selection.

Finally, the systems make recommendations. As organisations look to include and integrate external learning content into their offerings, such as public blog posts or videos, or external content from learning providers (e.g. LinkedIn Learning, Coursera or EdX), a Learning Experience Platform (LXP) can be used. This leverages the existing technologies in the organisational learning system and uses AI to make recommendations to users, suggesting new and related learning content, or creating learning pathways based on experiences.

As always, when looking at the implementation of AI, it is important to ask: where are the risks that accompany these rewards? Or where does the human need to step in? At the organisational level, here are a few questions L&D administrators could ask as they are evaluating and implementing these systems:

1.     Do I have a good understanding of the taxonomy that will be used to make recommendations? Are there terms that could be problematic in the context of my organisation, or what specific terms should I provide to the vendors? It is advisable for the information technology team and human resources / L&D team to discuss these questions together, and develop consensus before the systems implementation is started. Also, keep the vendors updated on major changes in vocabulary and terminology, following strategic reviews or significant business changes.

2.     What content do we (not) want to be searchable for employee-learners? And are the original content creators aware of who might be looking at their content included in the index and search? Consider guidelines you might need to provide or expectations you might need to set with content providers.

3.     What content needs to be excluded, due to data protection laws (e.g. GDPR regulation) in the European Union)? How should it be managed to ensure it is excluded?

4.     How might we need to change administrative permissions among L&D or HR staff? Organisations with compulsory training or compliance requirements often connect the talent management and learning systems. For example, once an employee completes a training, their personnel file will be updated, and a reminder set up for the following year.  When such systems are linked, it is important to also check what information will be displayed and to whom.

5.     What information are you agreeing to share with the vendors? Check the terms and services carefully.


As an employee-learner, here are some strategies:

1.     Get very clear about your learning goals and priorities. Recommendations can be helpful, but not always. If you liked one show on a streaming service, the suggestion to “try this” might leave you completely satisfied or scratching your head and questioning the “intelligence” of the AI. Know your learning goals. Not every recommendation is going to be a winner.

2.     Be discerning in your content selection. Learning opportunities can seem endless, but time is still limited. Focus on finding learning opportunities that include reflection and critical thinking, and applied activities. Read comments and reviews. Talk to colleagues about which learning opportunities they found most relevant to the specific context in which you work.

3.     Apply what you learn as soon as possible.  Create ways to take the knowledge and use it in your work, and to implement the new skills. For many people, online learning can feel passive – watching videos, clicking boxes. Find ways to make the content active.

4.      Create learning communities. Find ways to discuss and share what you learn with your colleagues. Exchange ideas on an online discussion board, self-organise meetings, or meet offline over lunch. Share strategies on how to apply what you learn.

5.     Check your settings and permissions. Most of the learning solutions mentioned have mobile applications. As with any app on your phone, check your settings and permissions to share only the information you are comfortable sharing.

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*Kim Ochs has been active in the field of educational technology for more than a decade, spanning work in higher education, research, and start-ups, working with international organisations, NGOs, private companies, and edtech investors. Kim holds a doctorate in educational studies from the University of Oxford.


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Speaker's Biography: Daniel Mutembesa is a research scientist and collaboration lead at the Makerere Artificial Intelligence Lab. He focuses on algorithmic game theory and mechanism design, behavioural and forecast modelling in crowdsourcing games, and applied artificial intelligence in the developing world.

His research covers algorithmic mechanism design of community sensing games for surveillance in agriculture and health, modelling participant behaviour in their unique low-resource settings, community graph networks, and machine learning models to forecast the risk burden of rural communities for diseases like malaria.

He is a recent grantee of the Facebook Mechanism Design for Social Good Research award.

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