Make AI work for your personal learning & development
Artificial intelligence (AI) already has influence on what we read, watch, and buy. AI continually nudges our behaviour and shapes our daily lives through content recommendations. Movies through Netflix, videos through YouTube, products through Amazon. All based on an AI-driven prediction of what we are likely to want next. This core concept often applies to the learning and development industry too.
It’s all about personalisation
In the Global Leadership Forecast 2018, more than 25,000 leaders picked their most wanted learning features (ranging from self-study to mobile-based to long-term developmental assignments). Above all other choices, personalisation was the number one feature for virtually all learners: frontline or senior-level leaders; Millennial, Gen X, or Baby Boomer; based in China, Germany, the United States, Brazil, or India; in their first six months or after more than 15 years as a leader; or leading within the sales, finance, IT, or HR functions. There wasn’t a single major group of leader-learners who didn’t have it at the top of their list.
Apparently, personal learning & development is a true unifying thirst across all leader segments. L&D managers are tasked to find new ways to meet these expectations efficiently across the enterprise. This user and business need—learning relevance via scalable personalisation—is a critical role AI can play.
What does it mean, personal learning & development?
Mostly, three main types of AI-based recommendation systems are mentioned and they all focus on the “what’s next” for learning.
Popularity-Based: For these recommendation systems, the most basic type, neither the characteristics of the learning content nor the characteristics of the learner matter. AI simply serves up the most-selected learning asset from the cumulative choices of all previous learners.
Content-Based: For these recommendation systems, the learning content matters but user characteristics do not. The AI system picks the next learning asset based on similarity to content a user has already picked. This similarity can be based on modality (such as virtual versus in-person), skill coverage, or many other classification factors.
Collaborative Filtering: For these recommendation systems, the user matters but the content doesn’t. The AI system picks the next learning asset based on what past learners with similar characteristics picked. These characteristics can include learner position (for example, individual contributor, frontline leader, senior leader), function (such as sales, operations, finance), and tenure, among other user profiles.
At DuoTrainin, the focus is User-Based. We are all different but on the basis of the above three approaches, the resulting individual trainings are still the same for everyone. That just won’t do. A person’s knowledge, experience and functional viewpoint should be part of any training, no matter the subject. Because if not, the first half of any training will be just a confirmation of what you already know. A User-Based AI approach to learning makes sure that the learner experiences a personalised approach that will result in a steeper learning curve.
In summary: AI should focus on intelligence built into each training and less on the learning journey. An individual’s learning journey should not be based on popularity and other learners. An individual’s learning journey should be based on his/her competencies and skill gaps as identified in collaboration with a mentor, coach or supervisor. That is true personal learning & development.
The classic mistake
This skewed focus on the learning journey also causes a key mistake HR/L&D make from the outset: they start with a content-based approach to learning and development. This involves curating and categorising content based on the skills it covers, the modality used to deliver it (such as web, self-study, classroom), the learner level it targets, and the management framework.
Focus on the individual learner
Instead, the focus must be on knowing your individual learners, from the outset. A persona, or avatar won’t do. If you are planning to use any type of collaborative filtering approach to AI, you have to understand your users at the deepest level possible. This can only be a collaborative organisational approach, involving all management. Overcommunication and full transparency are key conditions for this to succeed.
Truly personal learning & development
Relevance through personalisation is the right place to start to maximise user experiences and business value from your learning assets. By combining organisational experience to create individual learning journeys with AI-based intelligence built into each and every training, learning professionals can pave the way for people-data-driven approaches that meet the needs of the modern learner while also drawing on the latest AI technologies.