learning-design on S Anand

Workshops help AI adoption

Sat, 11 Apr 2026 13:08:46 +0800

To teach a mindshift change like AI adoption, I’ve tried to:

Workshop: get them to do it. “Let’s try something. Can you share your screen?”
Live-code: show them how. “I’ll share screens and tyep this.”
Demo: show what’s possible. “Here’s what I built.”
Talk: explain it. “Here’s something we can build.”
Interview: ask them about it. “What do you think?”
Listen: let them yap.

The most effective are on top.

But there is one intervention I didn’t evaluate:

Exercise: give them a problem to solve.

This takes more time and patience on my part, but might have the highest value.

What this means for me is:

Don’t live-code. Workshop it. “Can you share your screen and try that?” Preferably as an open-ended problem.
Don’t demo/talk. Live-code it. “Let me share my screen and try that.”
Prepare micro-experiments. Keep a ready catalog if things to try.

Directional feedback for AI

Mon, 09 Mar 2026 16:54:11 +0800

People worry that AI atrophies skills. Also that junior jobs, hence learning opportunities, are shrinking.

Can AI fill the gap, i.e. help build skills?

One approach is: Do it without AI. Then have AI critique it and learn from it.

(Several variations work, e.g. have the AI do it independently and compare. Have multiple AIs do it and compare. Have AI do it and you critique - but this is hard.)

I’ve been preaching this for a while but didn’t practice consciously until today. Here’s the rough prompt I used:

I created a data story (see index.html) and reviewed it using the "Revise data story" prompt in prompts.md.

How can I improve my reviews?

Use principles of psychology, design, visual communication, storytelling, data visualization, and more. Think about what feedback and expert would have given.

- What patterns would an expert in this field check / recognize that beginners would miss?
- What questions would an expert ask that a beginner would not know to?
- What problems / failures would an expert anticipate that beginners may not be aware of?
- How would an expert analyze this? At each step, what they are looking for and why?

Then craft a prompt to revise the data story. Compare that with my prompt.

How is it similar or different? What can you teach me based on this comparison on how I can improve giving feedback and suggest improvements to coding agents for data stories?

Document this in SELF-IMPROVEMENT.md, with examples and suggestions.

This started telling me how I could have given it ultra-specific feedback. So, I pivoted:

If I'm a non-expert and my aim is to provide feedback to AI coding agents that will enable the coding agent to act like an expert and use its judgement, I wouldn't be giving specific feedback - I would be driving it directionally based on my taste and judgement. From THAT perspective, suggest how I should think differently about sharing feedback - what I did well and what could improve. Append this to SELF-IMPROVEMENT.md.

This was gold.

There’s a different approach: directional feedback. Describe the experience gap: how the document should feel versus how it currently feels – and trust the agent’s expertise. Bring taste, intent, and audience knowledge. The agent brings expertise.

This is not a lesser form of feedback. It’s a different division of labor. And when done well, it produces better results than imprecise expert feedback, because the agent can optimize holistically rather than executing a checklist.

It suggested best practices:

Who is the audience? Like you said: “help a layman who does not understand security understand the impact of the findings”
What should they experience? Not “it’s missing X,” but “something feels thin here” or “I don’t feel the urgency yet”.
How should it taste? e.g. “Highlight elements (sparingly, wisely) in bold” – your use of “sparingly, wisely” is excellent.
How to test? E.g. “a reader who just reads the bold text should understand the entire story”.
Trust the agent. E.g. Tell the agent to “use your judgement”, “act as the expert”, explicitly inviting the agent to go beyond your instructions.

And now, I’ve learnt a little more about giving feedback as a non-expert.

Should courses be hard or easy?

Fri, 22 Nov 2024 07:28:34 +0000

Here’s a post I shared with the students of my Tools in Data Science course at IITM. This was in response to a student posting that:

The design of TDS course lecture videos are designed in such a way that it could be understood only by the data scientists not by the students like me who are entirely new to the field of data science. Though I have gone through 6 weeks of course lecture videos, I am not fully aware of the usage of ChromeDevTools, Bash, Github etc….

IITM Term 1: German. In my first term at IIT Madras (1992), I took German 1 with Prof D Subramanian.

The first words D.Subs said when he entered the room were, “Wer sind Sie?”

I had no clue what he was talking about. Nor did the others. After individually asking about a dozen students, Ashok Krishna replied, “Ich bin Ashok.” (He knew German.)

The rest of the term proceeded in the same vein. He threw us in the deep end, spoke only German, and I ended up with a “D” and learning very little German. Ashok Krishna thrived in that course.

IITM Term 2: German. In took German 2 with Mrs Schindler in the next term. The experience was very different. She explained each word. She had us listen to tapes in German. It was very relaxed.

I ended up with a “B” and learning very little German. (I’m not good with human languages.) But many others enjoyed it.

Which is better? I’m not sure. I think gentle courses are good for beginners and tough ones for advanced student, but classes usually have a mix of both. Aptitude and interest help, too.

IITM Term 1: Physics. Prof. PC Deshmukh handled our first physics course. (He was the one responsible for renaming Mardi Gras “Saarang”.) Our class felt the exams were too tough. My friend Badri and I explained this to him. “Well, you two should be lobbying for even tougher exams,” PCD said. “That’ll benefit smarter guys like you more.”

IIMB Term 1: Statistics. My classmate Athreya was a class representative for statistics (a tough subject.) Going against tradition, he lobbied for a tougher test. He was almost lynched afterwards.

Which is better? I’m not sure. An easy exam is good for student grades and morale. A tough exam is good for college reputation and advanced students. Classes usually need both.

In short, I have no clue.

Students who take this course learn several topics 3 months.

Faculty like me haven’t managed to learn how to calibrate course content or exams even after 3 years.

But, I’m learning. Thanks to you.

So please keep the feedback coming. We’ll keep improving. And thanks for putting up with us!

Comments

Tarun 22 Nov 2024 5:27 pm: Hmm thought provoking, my view is courses should be hard ..because we now live in a world of information overload .. the easy stuff is readily available, we just need to put in the effort to seek that information .. its the advanced bit which is still gold dust.
Mukundhan 22 Nov 2024 4:02 pm: An interested mind would love an advanced course, such courses are a rarity these days. But an advanced course without an “involved” instructor is useless. May be you need to prepare the students for the advanced topics- 1- Use a basic course or training material as a precursor 2- Be a lot more available for clarifying things at the start. 3- Keep the content & assignments hard, but the final evaluation easy or some mix of hard & easy. This will help students have a sense of achievement. 4- Evaluate for understanding and not absolute correctness in the assignments.