Xiangxiong Zhang
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AI chatbots can help students and faculty with teaching and research tasks such as explaining concepts, drafting and editing text, generating code, and exploring literature.
If you have never used any of these tools, that is completely fine. Everyone starts somewhere. A good first step is simply signing up for one of the chatbot tools below and asking it a question related to your work. There is a learning curve, and it is normal for the first few attempts to feel awkward. The examples on this page are meant to show what becomes possible over time, not what you should expect on day one.
Note: This page was created in Jan/Feb 2026. AI tools evolve so rapidly that info here can be completely outdated by March 2026.
Be cautious with AI agent tools – they can read, modify, and delete files, run code, and access the internet on your behalf.
Free tiers of AI tools are deliberately limited and do not represent what the technology can actually do. To evaluate AI fairly, it is worth trying the full version ($20/month for most tools) – the difference is substantial.
AI agents not only go beyond chatbots by autonomously executing multi-step (e.g., reading and editing files, running code, and searching web), but also provide a much more reliable way to make advanced large language model useful for many tasks robustly. They make working with AI more efficient and automated, requiring less manual back-and-forth. Representative AI agent tools include Claude Code, OpenAI Codex, GitHub Copilot, and Cursor.
Disclaimer: The examples below show how I have started using Claude Code in my own workflow. Many of these are certainly not the best or most efficient way to use the tool, but they serve as a practical starting point for beginners to see how AI agents can be useful in everyday academic work.
Common misconceptions: (1) Some people (especially young people and students) see the word "Code" in Claude Code and assume it is only a coding tool, but it is far more than that. (2) People who don't write code at all naturally assume Claude Code is irrelevant to their work. In fact, I have been using Claude Code the same way I use LaTeX, Word, and Zoom as an everyday tool. Much of my work on the computer, whether it involves coding or not, whether it is for teaching, research, or any other task, is now greatly expedited by Claude Code.
- Example A: I used Claude Code to create this page and this search tool for teaching schedule, despite knowing nothing about Python or HTML for scraping and querying data. The point is that AI agents can expand your domain of capability – you can build things that would have been outside your skill set entirely.
- Example B: Zoom recording for a brief introduction to using an AI agent (Claude Code) for daily work such as teaching and research, illustrated through a few concrete examples. The MATLAB codes generated by the AI shown in the recording above can be accessed here. Please use it with caution because there are no guarantees, although the code should run without errors.
- Example C: Watch this Zoom Recording (21 min) to see how I built a slash command in Claude Code so that one command + a name automatically does three things: 1. generate a PDF offer letter; 2. write an HTML email preserving font style and links; 3. update the excel file. I know nothing about bash scripts, but the AI agent handled that part – this is another example of expanding capability rather than just saving time.
- Example D: Notice that agent tools can automate many daily tasks, e.g., each entry on this webpage was generated by one slash command (skill) in Claude Code. MCP (Model Context Protocol) is a more consistent and precise way to achieve similar effect, and each entry on this webpage was generated by a skill invoking an MCP in Claude Code.
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“Just play and build something.” — Peter Steinberger, creator of OpenClaw, on getting started with AI
- Boris Cherny, Creator of Claude Code, on evolution of coding
With or without AI, just like with or without internet, email, LaTeX, personal computer, smartphone, Google, Google Scholar, the workflow in academia never changes: idea → implementation → validation.
If we use AI tools properly, we can reduce the percentage of time spent on implementation. This allows us to spend more time on ideas, and that is exactly why rigorous classroom learning matters even much more in the age of AI than it ever did before.
The image below shows only the percentage of time spent on each step, not the absolute time.
