Teaching Evaluation 2026

Today I received this year’s teaching evaluation. My PhD course always had high scores, which I didn’t worry about. What concerned me was the master’s course of data science.

We all know that AI progresses at an unprecedented speed, with potential to dramatically disrupt the current social-economic structures. Econ5821, Data Science for Economists, turns out to be a set of skills sitting closely to AI’s firing range.

Before AI, economics students were relatively weak in coding. I thus spent the first few lectures to cover basic coding and advanced coding. Nowadays, vibe coding becomes the norm. I encouraged students to do their works with the assistance of AI. If we can vibe coding with prompts, we would not do it manually. I set the AI policy of this course to be the most open: I allowed any AI with without acknowledgement or restrictions.

Around the New Festival, AI agents’ ability substantially improved. I taught myself the latest agentic AI, and shared with students. In particular, I demonstrated it with the open-source large language models hosted by our department’s DIY cluster, and hooked Openclaw with unlimited free tokens that our department generously provided. Reflected from the teaching evaluation, students liked what I had prepared.

In recent years, there have been a lot of discussions in China about the deteriorating relationship between college teachers and students. Will universities become obsolete? In the time of cyberpunk, the human-to-human interactions is ever more valuable. The following quotation is to be remembered:

道不远人。人之为道而远人，不可以为道。