Leveraging AI/ML in Learning and Development
Laxmish Bhat, Co-Founder & COO at iZen
Artificial Intelligence in general and Machine Learning in particular (AI/ML) have ushered a new era in many industries, especially in the last two years, albeit with varying degrees of adoption. AI/ML solutions are all around us, and new ones are being created every day, though we may not realize it. Learning and Development (L&D)is not immune to this.
Over the years, various technologies and gadgets have helped us learn more efficiently and effectively. L&D domain has been changing significantly; with the advent of computerization and the Internet, we moved from traditional ways of learning to e-Learning to social and blended learning to digital-learning, which is based on learning-experience and design-thinking.
Now, AI/ML is changing L&D in a fundamental way.L&D is a broad spectrum and the varied lifestyles/preferences of people have been constantly changing; therefore, the L&D processes and technologies need to adapt accordingly. Fortunately, we have technologies like AI/ML to better empower today’s students and employees.
Adaptive, Personalized Learning: From Vision to Action
The domain of L&D is one of the early areas to be digitized. However, there is a strong need to make it adaptive and bring personalization to learning, in order to transform learning for every learner.
If we can provide it with the right data points, an AI/ML solution can design “individual learning experience”, bring “effective and efficient learning”, and a lot more, limited only by our imagination and business justification.
It is not an easy task, but it is certainly feasible to gather the right data on things such as individual learning-goals, career-aspirations, skill-requirements of current/future job demands, learner behavior/habits/preferences, learning-environment, and resources at their disposal. Also, it would be helpful to know what has worked in the past for each of these scenarios.
As the content and delivery-format are aligned to the aspiration and learning style/preference of the learner, there will be effective and efficient learning. This will result in better business value, given the better synergy between demand and supply of talent. The solution will get better and better as the usage increases, creating a virtuous cycle of a learning experience.
AI/ML solutions can predict what one may want to learn OR recommend what one should learn. Based on the right type of data, usage and resulting outcomes over a period of time, the solution itself can suggest learning objectives, recommend the best learning style, and show the type of content and delivery modes that work best for a particular student.
Such a solution can also recommend learning plans based on changes in regulations, policies, project requirements and trends that a human brain may not notice.
Content Curation and Delivery
In today’s world of information overload and the dynamic nature of the content, effective curation requires automation. The amount of data and number of sources is overwhelming today and hence AI is coming in to bring it all back to human scale.
Whether it is a traditional type of course material that we need to author or the snippets for “micro-learning” requirements of today, an AI algorithm can crawl multiple sources and documentation and create the right learning material that is contextual, crisp and actionable.AI/ML can deliver exactly what you need when you need it and how you need it.
AI/ML solutions can help create tests/quizzes based on the course materials. By referring to the student performance, it can also generate a plan of action including the areas to focus on and a custom-built study material that is based on the data / deeper understanding of the student.
Learning is a two-way process and students need quick, crisp answers to their queries while studying. It will be extremely beneficial to have a BOT that can answer any questions for you in a particular field or help with a type of job. This BOT can understand the context and can leverage vast resources to give you just what you need.
In the case that the BOT cannot resolve a question, it can refer the user to a human SME, who can answer it. In this process, the Human SME would have trained the BOT, which would allow it to handle a similar situation next time.
In today’s workplace and even in academia, the way by which people want to learn is changing drastically. When they want to learn something new, courses/classes are still needed. However, in most cases, they need help “now”, and all they need is some quick help with a concept, code-samples, tool-usage or Q&A. Workers/students want micro-learning, embedded in their day to day workflow, and “spaced” according to their schedules and needs.
Such a BOT can work as a TA (Teaching Assistant) as well, handling student questions related to the course and the class-logistics.
AI/ML is beginning to enter the fascinating world of L&D. It is certainly going to disrupt and transform the space, by bringing in cognitive automation and the personalized and adaptive digital learning.