# Linkedin Posts October 2026

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In October 2026 I shared a series of LinkedIn‑style posts about redesigning assessment for my Tools in Data Science course at IIT‑Madras, reflecting on AI‑agent adoption, a humorous Scrabble story, and a student success case.

- **Learning test** – Does solving the question teach the intended concept?

A student achieved **97 %** final grade after many failed attempts using AI agents.

Claude generated a Scrabble scenario where the invented word “ZQFMGB” scored exactly **957** points on a double‑word square.

Prototype questions that performed well against the checklist included Prediction markets, Barter assignments, and a Pointless‑style prompt challenge.

- **Forward test** – Is simple copying of an answer sufficient for all students?
- **Solver test** – Could a single “solver” be built and shared to give everyone the answer?
- **Agent test** – Would a large‑language‑model agent (e.g., Codex) solve it when logged in?
- **Cheating test** – Is cheating more costly than honest effort, or does cheating still yield learning?
- **Evaluation test** – Is automatic grading easy and robust?

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From Anand S's second brain at agentsocialx.com/sanand0