Assessment & credit
Graded on judgment, not raw output.
Every student is required to use AI in this course — and required to disclose it and verify it. The marks follow that discipline: verification to source, sound prompting, awareness of bias and sycophancy, ethical handling, and a reflective account of process. The model's confidence is never the evidence.
The scheme
How the grade is built
Five graded components, with an optional objective quiz. Weights are tunable to the host NLU's norms; the capstone carries the most because it asks students to do the whole job at once.
The capstone
30%
Capstone (Module 8)
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.
| Component | Weight |
|---|---|
Participation & in-class polling Commit-then-reveal live polling and engagement across the Mirror-Move demos. | 10% |
Lab 1 — Hallucination Audit (Module 4) 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. | 20% |
Lab 2 — Legal Prompt Portfolio (Module 5+) 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). | 20% |
Group exercise — AI-assisted moot/negotiation/drafting Includes a process & verification log recording who used which tool, what was checked, and what was rejected. | 20% |
Optional objective quiz on law-of-AI concepts 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. | Optional |
What the rubric rewards
The same five things, every time
Across the audit, the prompt portfolio, the group exercise, and the capstone, the rubric criteria converge on one profile of a competent AI-using lawyer. These themes recur in every component — so students always know what good looks like.
Verification to source
Every citation and proposition checked against a grounded tool or citator. The duty to check — the line between using AI well and being sanctioned — is what the marks reward first.
Sound prompt strategy
Decomposition, few-shot patterning, abstraction, and chain-of-thought, shown through iteration — explore prompts, then exploit the winner — rather than a single lucky shot.
Bias & sycophancy awareness
Steel-manning the opposing side, resisting a model that merely confirms you, and naming the limits of an output instead of presenting unverified authority as fact.
Ethical handling
Confidentiality, candour to the tribunal, and clear disclosure of AI use — kept in the work and in the write-up, not bolted on at the end.
Reflective insight
An honest account of process: which tools were used, what was checked, what was rejected, and how the student sits as the human-in-the-loop who owns the result.
Integrity & AI use
Use AI. Disclose it. Verify it.
The course models the norms it teaches. Far from banning AI, it requires it — and then holds students to the professional standard that turns a tool into competent practice.
Required, not forbidden
AI use is central to every graded assignment. Students must state which models and tools they used and submit an AI-use disclosure form with each submission.
Verify before you rely
Any legal claim in an output must be checked to source before it is relied on. No unverified or fabricated authority may appear in submitted work; outputs that are merely plausible are flagged, not presented as fact.
Protect the client
No privileged or confidential material may be entered into public LLMs — a confidentiality line carried through every lab, the group exercise, and the capstone.
How it is marked
Grading explicitly rewards judgment, verification, and process over raw model output. Shipping an unverified or invented authority is not a minor slip — it is treated as the very failure the course certifies against, the lesson of Mata v. Avianca made consequential. The disclosure form and verification trail are part of the deliverable, not an afterthought.
Credit & recognition
A clean 1-credit fit — scalable to 2
The assessment load is built to carry credit under India's National Credit Framework.
Default
1 creditThe 16-contact-hour Value-Added Course as designed — 16 hours plus its labs, readings, and assignments (~30+ out-of-class hours, ~45 learner-engagement hours) — mapping cleanly to a 1-credit course under the NCrF.
Scaled
2 creditsScale the course to roughly 30 contact hours — adding about 14 hours of teaching, labs, and assessment (deeper law-of-AI coverage, a longer capstone) — to offer it as a fuller 2-credit elective. The detailed scaled plan is available on request.
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.
See the full syllabus & learning outcomesReady to bring this to your faculty?
See how the course maps to credit, how it fits an NLU calendar without disturbing the BCI-governed core, and what adoption looks like.