National Law Universities · a credit-bearing course
Use AI like a lawyer should: effectively, and responsibly.
Computational Thinking & AI for Lawyers is a 16-hour course for National Law University students. It teaches you to prompt AI well, interrogate what it returns, and verify before you rely on it — so you wield it as an extension of legal reasoning, not as a liability.
- contact hours
- 16 contact hours
- modules
- 8 modules
- credit (VAC)
- 1 credit (VAC)
The cautionary tale
The stakes are no longer hypothetical. This course exists to teach you how not to be the lawyer in Mata.
“A New York lawyer told a judge that ChatGPT "could not possibly be fabricating cases." It had invented six. The $5,000 sanction is the cheap version of this mistake; in India in 2026 the Supreme Court warned that leaning on fake AI-generated judgments would be misconduct.”
Mata v. Avianca, Inc., 678 F. Supp. 3d 443 (S.D.N.Y. 2023)
Judge Castel; $5,000 sanction; six fabricated cases (Varghese, Martinez, Shaboon, Petersen, Miller, Estate of Durden); the lawyer's fatal assumption that ChatGPT could not be fabricating cases. The flagship cautionary tale and the course's framing device.
Gummadi Usha Rani v. Sure Mallikarjuna Rao (SC of India, SLP (C) No. 7575/2026, Narasimha & Aradhe JJ.)
A pending Special Leave Petition in which the Supreme Court, on a trial court order built on fake AI-generated judgments, observed that such a decision "would be a misconduct and legal consequence shall follow" and issued notice. Not a final holding; the India-first anchor for why verification is a duty.
The spine of the course
You already think computationally. Legal reasoning is computational thinking.
The four pillars of computational thinking are not new skills you must learn — they are skills you have practised since your first moot. Once you see the mapping, prompting and interrogating an AI stops feeling foreign.
Breaking a messy fact pattern into discrete, ordered questions is exactly what decomposition asks of a program.
Reasoning by analogy — matching this case to the line of authority it resembles — is pattern recognition by another name.
Stripping a judgment down to the principle that binds is the lawyer's version of abstracting the essential from the noise.
Walking a multi-part test step by ordered step is an algorithm — a procedure that yields a result the same way every time.
The same bridge gives the course its decoder, the Ladder of Misinference — statement to fact to data to evidence to proof. A hallucinated citation is a statement costumed as binding proof; every module climbs this ladder on a real AI output.
The arc · eight modules, sixteen hours
From the spine to a verified, defensible workflow
Eight two-hour modules ladder from why legal reasoning is computational thinking to a capstone that ties the human and the machine together. Each is hands-on, built around live demonstrations on real AI.
- 01
Map the spine
You Already Think Computationally: Legal Reasoning Meets the Machine
Open module - 02
Demystify the machine
How the Machine Learns (and Why It Invents Cases)
Open module - 03
debias
The Lawyer's Blind Spots: Cognitive Bias, Statistics, and the Sycophantic Bot
Open module - 04
Verify
Decoding the Answer: Verification & the Duty to Check
Open module - 05
prompt
Prompting Like a Lawyer: Computational Thinking as Prompt-Craft
Open module - 06
Apply: match tool to task
AI in Practice: Research, Drafting, Review, Discovery, Access to Justice
Open module - 07
law-of-AI
Law OF AI: Bias, Evidence, Liability, IP, Data Protection
Open module - 08
synthesize
Capstone & the Future Lawyer: Becoming the Human-in-the-Loop
Open module
Credit-bearing · ready to adopt
Bring an AI course your students will actually be able to defend in court.
See how Computational Thinking & AI for Lawyers slots into your programme as a one-credit value-added course — delivery, assessment, and the case for adoption.