As per a McKinsey Global Survey, 87% of executives and managers say their organizations either face skill gaps already or expect skill gaps to develop within the next few years.
Frankly speaking, it takes time to close a skill gap traditionally or through conventional methods. However, advanced education technologies like AI can help bridge skill gaps by addressing skilling, upskilling, and reskilling challenges. AI carries tremendous potential to solve the skill gap problem, address the learning needs of employees, and prepare the future workforce.
To shed light on how to leverage the power of AI for Augmenting Skilling, Reskilling, and Upskilling, Harbinger’s Senior Director for EdTech, Rahul Singh shared his expert insights in a recent webinar organized by the International Accreditors for Continuing Education & Training (IACET).
In this webinar, Rahul spoke about the trending use of AI for skilling, reskilling, and upskilling and how AI can help tackle the forgetting curve challenge. He also dived deep into implementing an AI-enabled skilling framework, AI-enabled nudge-learning model, meeting the learner in the flow of work, and more.
Let’s look at some of the interesting and insightful answers Rahul shared in the webinar’s Q&A session.
What is the opportunity for people working in the training, education, and L&D domains?
Rahul: Leveraging AI for skilling, reskilling, and upskilling initiatives will open up a range of opportunities. But if I have to pick the top four opportunities, those will be:
When a learner is completing their course, are they notified about their progress, or is only the employer notified?
Rahul: In the current context of things, we want to make learning learner-driven and student-driven. The more control a learner or student has over their learning and the more information they have regarding their progress, the more motivated they will be to continue their learning process.
When it comes to learning progress notifications, it’s recommended to send the notifications to the learner. However, the employer also needs to be aware of the progress of the learner. This helps the employer to understand how their investment is working in terms of closing the skill gaps they are dealing with.
So, both the learner and employer need to receive notifications of the learning progress. Having said that, it could differ from case to case and organization to organization and depend on the specific requirements of L&D organizations.
How can AI coaching programs help create better peer-to-peer interactions and engagements?
Rahul: There needs to be a social element added to today’s learning, and more so to the current hybrid work environment. What typically happens is that a virtual coach could be implemented as a ‘one and all’ technology for everyone within an organization or institution. Of course, the virtual coach could show a different behavior for every employee or student based on the questions and concerns they have.
But let’s say, there are a couple of employees in the same department and have some common questions or challenges they are trying to solve with the help of the virtual coach. So, when the virtual coach talks to the first employee and tries to solve their problem, it becomes smarter in the process.
And when the second employee comes with the same set of issues, the virtual coach now has a use case of the first employee to answer in a better way. It leverages this use case to give a more effective, smarter answer to the second employee.
What is the starting point for implementing an AI-enabled reskilling framework? Do we need to have complex systems in place to implement it?
Rahul: To begin with, we need to have a competency-based learning framework in place. It doesn’t need to be very complex, i.e., you don’t need to have a plethora of software products or platforms to enable the framework.
No, we don’t need to have too many complex systems in place. There are several ready-to-use AI-enabled frameworks available which act like solution accelerators. A good example would be the conversational chatbot Google Explore. The beauty of AI is that it has a continuous learning process. So, the more you speak with such chatbots, the more they learn from you. They keep getting smarter day in, day out.
Any advice to HR professionals or career coaches who are concerned about their jobs being replaced by technology?
Rahul: In my opinion, the role of AI in education, learning, and training is going to be crucial going forward. However, AI is never going to replace the actual human coaching part because the technology needs to learn from us.
AI will, in turn, help organizations create automated workflows and a continuous support system. Considering that HR professionals and career coaches can’t be available as individuals 24/7, this will be very helpful for them to better serve their employees, students, and learners.
Is it possible for AI-based coaching programs to nudge learners equitably? Could these programs scale to learners dynamically?
Rahul: If we take the one-size-fits-all approach, we again go back to adopting a traditional learning method. But AI is different and enables non-conventional learning.
There are multiple ways to implement an AI-enabled virtual coach. When implemented in an institution or organization, there is a high possibility that the chatbot could converse with both experienced and less experienced employees or freshers.
Based on the type of questions individuals ask, the chatbot could give paricular responses. But the accuracy of understanding and answering questions keeps improving with time. Therefore, expecting a 100% accuracy from a chatbot from day one might not be reasonable to a certain extent. AI-enabled chatbots gradually become smarter over a period of time.
Do you have any recommendations on AI programs when it comes to navigating data privacy laws like GDPR and CCPA?
Rahul: There has been much talk about the need to use AI ethically, and we should definitely respect that. Any kind of technology implementation, whether it’s a simple LMS or a complex AI-enabled virtual coach implementation, must work according to the state-specific or country-specific data privacy laws. As a community or society, it’s our responsibility to ensure the ethical use of AI.
Can you share some examples of successful implementation of nudge learning by organizations?
Rahul: There are a couple of stories I would like to share.
The first one is about Google. So, Google conducted an organization-wide survey, probably a decade ago. The survey results revealed a concern of Google employees, and it was about mid-level managers at the organization being intimidating.
To counter this problem, Google’s HR and L&D teams took note of employee intimidation incidents within the organization at the global level. They then used these intimidation scenarios to create microlearning nuggets to solve the problem. They called these nuggets “whisper courses,” and an improvised version of these courses was sent to every employee via email on particular day and time.
After a specific period of time, Google rolled out another survey. This time, the organization realized a positive impact of that nudge-learning, and the intimidating behavior had reduced to quite some extent.
The second story is about the UK-based airline Virgin Atlantic. The company was facing two major problems in its airline business: carbon emission and financial loss.
They came up with a nudge-learning program to train their pilots on how to consume less fuel. After a specific period of time when the program was evaluated, the airline found that its carbon emission and fuel consumption had reduced to a certain extent, which added to its profit margin.
There is a lot to discuss on skilling, upskilling, and reskilling, and how AI can help solve related challenges. If you wish to share your thoughts on these concepts or need AI-based solutions to address skill gaps or solve skilling, upskilling, and reskilling problems, leave a message at email@example.com.