00:01It gives us a clear mandate that we should have AI as an ecosystem, as the spine of the
00:11institution and then build around it. It would also then restrict the kind of, because the
00:20layering brings in information and the information of course has a lot of ethical issues. But if AI
00:27is part of the hard stuff itself, then you know how to use it, where to use it, in what
00:33elements to use,
00:34it's part of pedagogy, it's part of assessment, it's part of validation, it's parts of monitoring etc.
00:42So it becomes more comprehensive in the system. If it is just as a layer, then there are always
00:47many chances of. So what does this mean from an institution point of view? It has two pathways.
00:53One is, of course, if the institution has been existing for decades or a century, then it has
01:01to come in a retrofitting form. And then what should be the elements of that retrofitting? How that
01:08retrofitting should happen requires a lot of introspection and pathways to go. Is retrofitting
01:17again top starting from the programs and offering or is it again bottom-up where we look at each of
01:24our systems, look at our elements, look at our pedagogy, look at how we develop content and see that how
01:33AI can come into each one of them. The second pathway which is easier, of course, is that if you
01:39are
01:39studying something new, then you need to build it from that perspective. Now that brings me to the last
01:48part. The skilling part in this is that there is a huge demand out there and that is the third
01:56layer
01:56which Dr. Ashwin also alluded to. And the demand is largely from the industry pathways. So how much of
02:04that AI learning and AI education, how much of it is connected to the larger ecosystem, whether it is
02:15MSME or the large corporate world and how much of it is seamlessly integrated.
