Artificial Intelligence Tools for L&D – Examples & Uses
The advent of artificial intelligence brings about significant analytical power that corporate L&D can take advantage of. While the AI technologies are generally nothing new, the significant increase in computing power has made the rapid development of recent years possible. Whereas the strong all-powerful AI remains a dream, there are a lot of practical applications for the technology. Here are 3 examples and specific use cases for different AI tools for L&D.
Recommendation engines & algorithms
One of the most commonly implemented AI tool in L&D is a recommendation engine. Most often used for recommending content, the engine analyses the context of an individual learner, and aims to offer a personalised curation of learning resources based on the materials given. However, it’s worthwhile to note that these types of recommendation engines have existed for long, even without AI.
Whereas content recommendation works on a relatively micro level, it’s possible to use the same principles on a wider spectrum. Some of the more advanced recommendation algorithms and AI tools don’t just recommend content, but can also extend to recommend different interventions and courses of action for the L&D team. For instance, the algorithms can provide suggestions on learning paths for different groups.
Another great example of AI tools suitable for L&D are grouping algorithms. While they constitute a very basic form of machine learning, these algorithms can be a powerful tool. Essentially, what the algorithms do is they analyse different individuals or groups (e.g. business units, departments, locations) and their attributes. For instance, the algorithms could detect groups with similar recommended learning paths. Consequently, the L&D could use these inter-organisation groups as basis for organising learning, rather than arbitrary division.
Furthermore, another use case is to use similar grouping algorithms to group people based on their ability. This type of use would detect individuals’ and their groups’ common existing capabilities, and propose reorganisations based on that. In practice, this would enable further personalisation of learning by dividing the organisation into groups, and offering each group the optimal difficulty and degree of content.
Predictive analytics and modelling
Another great use of AI tools for L&D is on the analytics front. While there are several uses for learning analytics, AI makes possible more than what we are used to. Instead of simply reporting descriptive analytics, AI enables us to get into diagnostic, predictive and prescriptive analytics. Diagnostics generally aim to answer why certain things happened (i.e. why did learning results drop). While that in itself is incredibly valuable information from an organisational development perspective, there’s still more to unlock.
Predictive analytics enable us to answer questions about potential impact (e.g. “what will happen if we get learning engagement to increase by 20%?”). This enables organisations to run “what if” analysis and supports them in identifying the areas of L&D where it’s possible to make the most impact. Prescriptive analytics, on the other hand, do a similar thing, centring around inputs (e.g. what do we need to do to raise learning engagement by 20%). While these kind of analytical powers require significant commitment in measurement and defining relevant parameters, they provide a tool for L&D to demonstrate its impact to the business that hasn’t been around before.
While AI is currently suffering a slight inflation thanks to its buzzword status, there are a lot of great AI tools for L&D out there and in the making. These tools not only enable learning professionals to offer better learning experiences, but also to understand the impact of learning. There’s also big potential in automatising a lot of the conventional information gathering. This, in turn, should enable L&D teams to focus on their core competence – delivering great learning. If you’re interested in the different possibilities AI can offer and how to use AI in organisational development, contact us here. We’d be happy to share some of our experiences, examples and research.