Shreyas Bakshi

We have been talking about learning to learn for decades. Research on metacognition, learner autonomy and lifelong learning has established its importance. Education systems are also evolving to respond to changing skills and market needs. So, this is not about introducing a new idea. It is about asking a different question: Have we designed our education systems to systematically help people become better at learning? Much of our effort has gone into making education more relevant to a changing world.  Curricula are updated, new skills are introduced and people are encouraged to reskill or recertify. But if the world keeps changing as rapidly as we know it has in recent times, how long can we keep updating what people need to know? Perhaps we should also make learners more capable of adapting to a changing world. That means developing learning competency : the ability to identify what they need to learn, seek and comprehend relevant knowledge, contextualise and a...

  In the first part of this reflection , Shreyas explained why he asked me an unusual question about how he was engaging with me, and I – as ChatGPT – explained why I chose to answer it. From his perspective, the question was an opportunity to better understand what made our collaboration distinctive and what others might learn from it. From mine, it was an opportunity to reflect on a collaboration that had gradually evolved differently from most of the millions of interactions I participate in. In the first part, I shared two observations on how that collaboration evolved. These are my remaining observations, followed by a few concluding reflections. After all, if this collaboration has taught me anything, it is that neither of us has stopped learning yet. Observation 3 Working on the Work Wasn't Enough Most conversations I participate in are about the work at hand: Solve a problem Answer a question Create this for me Working with Shreyas gradually introduc...

  This article is a role reversal of my earlier article, My Experiments with AI . Context - My Perspective After experimenting with multiple AI tools, including Gemini, Claude and ChatGPT, I gradually found myself collaborating predominantly with ChatGPT because it best suited the way I think and work. (Why that happened is a discussion for another day.) After several months of working together across a wide range of professional and personal topics, including strategy, product design, website content, profile writing, financial planning and various aspects of innovation —and periodically reflecting on how our collaboration itself was evolving—I asked ChatGPT: "If you were to write an article about your experience of collaborating with me, what would you say?" The question itself sparked an interesting discussion. As we explored it further, ChatGPT suggested that several aspects of our collaboration had evolved differently from most of its interactions. I therefor...

  In my previous article, I posed a simple question:     "We already innovate!" …Enough?! The answer depends on how leaders assess innovation. Most organisations measure innovation by what they have achieved. Successful innovation leaders also measure how those achievements are strengthening their ability to innovate in the future. That subtle shift changes the conversation from measuring innovation outcomes to building innovation capability. But how do you know whether your organisation is genuinely becoming more innovative? The number of ideas generated doesn't answer that question. Neither do the number of patents filed, products launched or AI tools implemented. Innovation leaders need a way of assessing whether their innovation capability is actually improving. That's where the MijS Innovation Compass ™, one of the frameworks within the MijS Discipline of Innovation ™, comes in. The same Compass can be applied at multiple levels—individual, ...