By Shradha Dhar

The recent breakthroughs in Artificial Intelligence (AI) have transformative potential to revolutionize end-to-end learning and development process. The answer is clear – AI is designed both as a disruptor and an enabler, and definitely not as a replacement. It is a game changer for the L&D industry because it can automate repetitive tasks, offer insights and recommendations, and enhance the experience of the learners.

I recently attended an insightful virtual session on Enhancing L&D with Generative AI: Pioneering the Future of Work, organized by People Matters in association with Coursera. The panelists were thought leaders from the industry who have both implemented AI in Learning at the workplace, and hold visionary ideas about its future impact.

If you are a L&D practitioner and like me, curious to explore how AI can be leveraged in learning, do check out the insights shared in the session. I had documented these for my personal learning –  extending here to the larger community for shared learning.

My Key Takeaways from the session

Segment 1: A Disruption or An Enabler: Is Generative AI a game changer?

The potential of Generative AI in the context of Learning and Development was discussed. Some of the key points highlighted by the speakers are as follows.

  1. Recent research by McKinsey estimates that Generative AI has the potential to generate $4.4 trillion global productivity over the next decades, which includes 3-5% surge in sales productivity
  2. There is huge potential of Generative AI in the learning & development industry, both as a disruptor and an enabler, but that completely depends on how the organizations bring it to life and make it accessible
  3. Disruption with AI has already happened with ChatGPT being used for self-learning
  4. Generative AI has also made skill building possible now, apart from the common knowledge building for which most digital platforms have been created
  5. There is a huge potential in AI being used for training need analysis, that requires gathering, reviewing and analysing large amounts of employee skill data, right from the shop floor to the top management, across industries

Segment 2: What are the use cases of Generative AI in Learning? How do you customize the learning using Generative AI?

Generative AI can be used in the following listed ways to support and enhance the learning experience.

  1. Chatbots are a big thing now in the space of learning, as it can simulate human-like dialogue. It can be used for answering immediate questions, giving feedback, doing assessment debriefs and can even provide personalized coaching.
  2. Gen AI can now help to personalize the learning content to the regional languages of the learners. It has made learning accessible to local employees, especially on the shop floor. Coursera has also been able to bridge the language barriers by translating their content to 9 languages, not just in terms of transcript but also in video-courses.
  3. Facial & Emotional recognition is another added feature that enhances online learning experience. It can be used for skill building by combining it with role plays in the context of learning. Facial recognition has also made proctoring easier when completing assessments.
  4. Adaptive recommendation systems using AI have helped to personalize the learning experience by recommending learning content and modules based on identified skill gaps, individual preferences and characteristics.
  5. Scalability and enablement of people who require re-skilling, especially those who are coming back after a career break.
  6. AI helps to curate and build high quality content reducing the time, effort and cost required for content development. Coursera is currently reducing their content development time with the use of AI. They are also able to use AI for content authoring wherein content creators can upload the required content, mix & match different other kinds of content from Coursera and design a customized learning path of a fixed duration.
  7. The Coursera Coach is another use case for Gen AI in learning. When learners get stuck or have questions on how to apply the concept or framework, the feature of the Coursera Coach can be availed. The Coach also helps to summarize the learning, suggest what to learn next, give examples, and answer other questions.
  8. Reducing screen fatigue and information overload are one of the other use cases of Gen AI in learning which needs further deliberation on application. AI can be used to enable the learners to find the right information (and not all the information) amidst the information overload. AI can make it easy for the learners to absorb and retain the information.
  9. Prompt engineering is the future which can help to find and ask the right questions to get the right answers. Example is ChatGPT which has been blown up for prompt engineering.

Segment 3: How can organizations chart a reskill and upskill policy using Gen AI?

The speakers discussed 3 insightful and creative ways in which Gen AI can support organizations with employee reskill and upskill, especially at high volumes.

  1. Use Gen AI to baseline and identify skill gaps – The skill gap on current versus ideal skill level, along with futuristic career goals of the individual can be identified and mapped to suggest a personalized learning path. A graded learning path can be designed based on this information. 
  2. Predictive Analysis for Speed – Since speed is important for any business, leveraging predictive analysis to fast-track upskilling programs and conducting mass customization of learning based on role and industry, is the expectation from Gen AI. With this, platforms can show where the person currently is versus where they should move in the career path, and then recommend powerful content. AI can help with the speed in which Learning & Development deliverables need to be made.
  3. Reskilling with adaptive learning – Let’s say 5 people have to learn a topic, but they are at different levels of knowledge and skill with respect to the topic. Gen AI can be used to adapt the topic and learning based on the individual needs of the 5 learners. This is a great way to support employee re-skilling by structuring the learning paths in Basic, Intermediate and Advanced levels (or even Professional Paths like from Data Analyst to Data Scientist) and assigning learners to each path based on their level of knowledge and skill.

Concluding Comments by Speakers

  1. Gen AI & online learning will be responsible for transitioning people into new roles, and impact upskilling and reskilling of employees. It will also open the doors for assessing talent.
  2. The role of the senior leadership is critical – Are they demonstrating learning agility and adopting technology? Leaders have to start talking about it. They have to play the role of digital champions and also adopt technology. The Reverse Mentoring program helps senior leaders today to become comfortable and adapt to technology.
  3. Technology Quotient and Digital Mindset are important traits to have for every employee within an organization today. They need not be experts on technology, but basic understanding of what the various technologies mean, and having an open mindset to learn about it, is a critical sign of a healthy organization.
  4. Finally, we need to Democratize Learning by helping learners to learn what they need instead of mandating learning hours and topics for them.

Overall, the virtual session was extremely beneficial for me to push my thinking beyond the usual application of using Gen AI in learning. You can watch the full virtual session here.

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