Course correction: Building a case for investment in AI for learning

Uma Ganesh | November 9, 2020 |

The biggest value-add AI could bring to learning is better understanding of learners, their learning styles and their evolving learning needs

Building a case for AI in learning is much more complex as compared to other areas of business. However, learning and talent development trajectories cannot be ignored as part of the digital transformation agenda of the business and therefore the need to come up with smart measures for evaluating the potential for AI in learning.

The biggest value add AI could bring to learning is better understanding of learners, their learning styles and their evolving learning needs. AI may replace trainers in some cases fully or partially and by following the learning patterns of the trainees, will empower the training managers to come up with customised and varied offerings. AI powered environment facilitates learning through trial and error methods and encourages the learners to experiment without fear or being intimidated.

AI systems could provide expertise and answer queries intelligently and through this process become smarter and more intelligent with every transaction, thus slowly becominge a synergistic companion for the learner as well as the trainer whose role over a period of time will become that of a facilitator and can even take on the role of grading, evaluating the students and providing feedbacks from time to time.

At times, the learning content and pedagogy may not be just right because of which learners struggle with their grades and the learning outcomes may not be satisfactory. AI systems enable large organisations to study these patterns for exact course corrections and thus make learning purposeful. They bring together the vast amounts of data about individual learning, social contexts, learning contexts and personal interests and thus it is possible to derive insights from interactions and use them to make learning adaptive as well as contextual.

When knowledge is served up as required and contextualised, it becomes much more valuable than when it is static and has the same flavour at all times to everyone. Contextual support at the time of addressing a customer query or resolving a problem at customer site not only enables the executive to be productive and efficiently tap into the knowledge just in time but the organisation knowledge repository also constantly grows in this process and becomes more intelligent over a period of time. The examples of Siri, Corona, Deep Mind acquired by Google and driver less cars are well-known, all of which highlight the potential of machine learning unleashed by AI systems applied in different ways. IBM’s Watson is a classic example of the deep commitment to ongoing learning—which has seen brilliant results in a variety of fields such as oncology, travel, law and finance.

Training managers motivated by the potential of AI interventions in the talent development process can identify a specific area where a pilot could commence with clearly defined ROI parameters. With the help of a relevant AI tool and ongoing analytics, it would be possible to assess the progress the learning programme makes.

While it is not easy to determine the outcomes of investments in AI in learning as compared to marketing or supply chain, it is very essential to get granular to define the direct and indirect measures of productivity and business outcomes to get the mandate for digital spend in learning.

Originally appeared in Financial Express