AI and Big Data for Isan and Korat

We talked about many things, from the BDI data project to the arrival of AI and big data and what it means for the people of Isan (the Northeast) and of Korat (Nakhon Ratchasima). The common image is that Isan is all farmland, but in recent years many other industries have grown in the Northeast, especially in Korat, for example creative work and manufacturing. In fact, Korat’s GPP from agriculture is about 40 billion baht, while manufacturing is about 90 billion baht. The common image is understandable, though, because most of the land is still used for farming (8 million rai out of about 12 million rai in total), and the agricultural workforce is about 800,000 out of roughly 2.6 million people in the province.

I see two ways that big data and AI can help economic development: from the policy side, and from the side of everyday users. On the policy side, this is what BDI is trying to do: use big data to support decisions and to identify target groups for development. The problem we may run into is translating the data into something local areas can actually use. From my experience with poverty alleviation and disaster management work, the main gap is this. When government or academia builds a tool, it usually lacks the social side needed to persuade local areas to adopt the data. For example, there are more forecasting systems than there are 7-Eleven branches in the whole province, but when a project ends, there is no further development and no handover to local people to look after the system. It gets shelved and left hanging as a dashboard somewhere.

The other side is users. Local people don’t need fancy AI models predicting all sorts of things. I’d like us to see that AI can help with routine and language tasks. Tourism is one example. On the policy side, big data and AI can help forecast visitor numbers and segment customers. On the user side, I recently taught a group of tourism students, and I think that just using AI to translate when a foreign visitor messages in the morning, or hooking up a simple chatbot that can answer customers about tour programs, already helps a lot. The main problem is first getting local people to understand that developing AI, and using AI and big data, is not something far from them, and not that difficult.

Finally, there is a question we haven’t worked out: in education, how should we let students use AI without depending on it entirely and believing it 100%? How can AI be part of a child’s development in a way that still lets them think for themselves in the long run? (For example, I once asked students to brainstorm problems from a picture, and many of them pulled out their phones, took a photo for ChatGPT, and pasted in the answer.) This extends to training the teachers who will go out and teach: how can teachers use these tools in their teaching? And lastly, how can entrepreneurs and the private sector in the area use AI and big data to grow the economy and develop the city of Korat?

These questions will still need time to study, and we need to design suitable ways of training. I’m not sure myself yet what that will look like. At the Nakhon Ratchasima Office of Lifelong Learning (NRLL), we will have several initiatives this year to test things out. I hope we can be part of the solutions that make Korat a hub for both the economy and education in the future.