Syllabus & credit

A credit-bearing course, built on the insight that legal reasoning is computational thinking.

Sixteen contact hours across eight hands-on modules for India's National Law Universities — teaching B.A. LL.B. and LL.M. students to use AI effectively and responsibly: to prompt it, read its output critically, verify it against source, and know where the law forbids relying on it.

16 contact hours8 modules · 2 hours eachCredit-bearing (NCrF)BYOD · AI labsFor Indian NLUs

Programme learning outcomes

What a graduate of the course can do

Eight outcomes, spanning law WITH AI — the tool — and law OF AI — the subject.

01
Explain, in plain terms, how an LLM produces text (training data to patterns to predictions) and why it hallucinates, and articulate the distinction between generative and retrieval-grounded systems.
02
Decode any AI answer using the Ladder of Misinference and verify legal citations to source, discharging the duty to check.
03
Prompt AI effectively for legal tasks using the four computational-thinking pillars: decomposition, pattern recognition / few-shot, abstraction, and algorithm design / chain-of-thought.
04
Select and match legal-AI tools to tasks (research engines vs. drafting vs. review vs. discovery) and understand their provenance and limits.
05
Recognize cognitive bias, statistical error, and model sycophancy in legal reasoning and AI output, and counter them.
06
Analyze law-of-AI problems at an informed level: algorithmic bias and due process, AI-fabricated evidence, IP in training data, and data protection under the DPDP Act.
07
Apply the professional-ethics rules governing AI use: competence (including technological competence), candour to the tribunal, confidentiality, and supervision.
08
Produce a verified, AI-assisted legal work product with a documented verification trail and a reflective account of the process.

Credit mapping

How it earns credit

Mapped to the UGC/NEP National Credit Framework (NCrF), where 1 credit equals 15 hours of lecture/teaching (or 30 hours of practical), plus roughly 30 hours of out-of-class work, for about 45 hours of total learner engagement. The course's 16 contact hours, combined with its labs, readings, and assignments (~30+ out-of-class hours, for ~45 learner-engagement hours), map cleanly to a 1-credit course. It is positioned under NEP as a Value-Added Course (VAC) / Skill Enhancement Course / multidisciplinary elective — categories an NLU can approve without disturbing the BCI-governed core curriculum. Adding roughly 14 more contact hours (to ~30) scales it to a 2-credit elective; a scaled 2-credit variant is available on request.

Default · Value-Added Course

16 contact hours

The 16-contact-hour Value-Added Course exactly as designed: eight 2-hour modules with embedded Mirror-Move demos, the hands-on AI labs, the full graded assessment scheme, and the capstone — about 45 hours of total learner engagement.

Assessment

How the work is graded

Grading rewards judgment, verification, and process over raw model output — the course models the very norms it teaches.

Component

Weight

Note

Participation & in-class polling

10%

Commit-then-reveal live polling and engagement across the Mirror-Move demos.

Lab 1 — Hallucination Audit (Module 4)

20%

The signature assignment: students receive an AI-drafted legal memo seeded with errors and verify every citation and proposition to source, submitting an audit report.

Lab 2 — Legal Prompt Portfolio (Module 5+)

20%

Documented prompts, outputs, and critique across a set of legal tasks (summarize a judgment, draft a clause, build an issue list, generate a counter-argument).

Group exercise — AI-assisted moot/negotiation/drafting

20%

Includes a process & verification log recording who used which tool, what was checked, and what was rejected.

Capstone (Module 8)

30%

A verified AI-assisted legal work product (e.g., a research memo or contract) plus a verification trail and a reflective essay on the student's process and the ethics.

Optional objective quiz on law-of-AI concepts

Optional

Short quiz covering hallucination, generative-vs-grounded systems, DPDP basics, and the duty of candour. Rubrics across all components emphasize correctness of verification, soundness of prompt strategy, awareness of bias/sycophancy, ethical handling (confidentiality, candour, disclosure), and reflective insight. Weights are tunable to the host NLU's norms.

For the briefs, rubrics, and integrity notes behind each component, see the assessment detail .

Delivery

Two delivery options are offered (the host NLU chooses). Weekly: 8 sessions of 2 hours each (8 x 2h), one module per week. Intensive: delivered over 4 days, two modules per day. Each module runs ~70 minutes of teaching/discussion (with embedded Mirror-Move demos) plus ~50 minutes of hands-on lab/exercise. Delivery is BYOD — students use their own laptops for the AI labs and their own phones for live polling, while the presenter runs live AI demos on a chatbot and real legal-AI tools on the projector laptop. Cohort size of 30–60 works for labs and is scalable with TAs.

Who it's for

Future litigators, transactional lawyers, judges, regulators, and legal academics: 5-year B.A. LL.B. and LL.M. students (ages ~18–24) at India's National Law Universities — verbally sophisticated and trained in close reading and argument, but often non-quantitative and AI-anxious, with a direct, career-shaping stake in using AI effectively and responsibly.

The plan

Eight modules, two hours each

Each module pairs ~70 minutes of teaching and Mirror-Move demos with ~50 minutes of hands-on lab.

Ready to look closer?

See how the course slots into an NLU's curriculum, or walk the eight modules in full.

For law schools See the modules