For host institutions · law schools and universities
Bring a single-credit AI course to your institution
Computational Thinking and AI in Law is a 16-hour, hands-on course — 8 90-minute sessions, each 60 minutes taught and 30 hands-on, plus 2 Conversations with Experts and a 60-minute evaluation — rigorous enough for academic credit, India-first and current, open to law and non-law students, and designed to slot in through your own academic council (for a law school, without disturbing the BCI-governed core).
Why adopt it
What this gives your students
Six reasons the course earns its place in the curriculum — each grounded in real cases, real tools, and a format that makes the lesson stick.
Future-proofs your graduates
AI is already in Indian courtrooms and firms: the Supreme Court runs SUPACE, SUVAS, and TERES, and large firms are deploying legal-AI tools. This 16-hour course teaches students to prompt AI well, read its output critically, verify it, and know when it cannot be relied on — turning AI anxiety into a professional competency.
Protects students from career-ending mistakes
Indian courts are already imposing consequences for AI-fabricated case law: the Bombay High Court ordered ₹50,000 in costs for citing a non-existent judgment (Deepak v. Heart & Soul, 2026), and the ITAT Bengaluru recalled an order built on fictitious citations (Buckeye Trust); a US court sanctioned the lawyers in Mata v. Avianca. In a pending 2026 matter, the Supreme Court of India has observed that relying on fake AI-generated judgments would amount to misconduct (Gummadi Usha Rani v. Sure Mallikarjuna Rao). The whole course is, in effect, how not to be the lawyer in Mata.
Half taught, half practised — every session
Each 90-minute session is 60 minutes of instruction and a 30-minute guided hands-on segment on the students' own laptops, with a timed run of show, a fixed deliverable and a verification checklist. Twelve of the sixteen hours are core sessions, and a third of every one of them is the students doing the work themselves. Nothing is taught that the room does not then do.
Hands-on, not a lecture series
Every session runs a four-step mirror pattern: an experiment on the class, the same effect shown inside a model, a live demonstration on a real chatbot, and a named takeaway skill. Students feel a bias in their own heads, then watch the machine make the same mistake ten seconds later — and the lesson sticks.
Computational thinking, taught through reasoning students already do
Decomposition is issue-spotting; pattern recognition is analogy from precedent; abstraction is extracting the ratio; algorithm design is applying a legal test. The course assumes no quantitative background — every technical idea arrives through an experiment on the room and a demonstration on a real model.
Built for a mixed cohort
The course is open to law and non-law students, and designed so that interdisciplinary interaction is part of the learning. The first six sessions build a general toolkit for thinking clearly about AI and verifying what it produces; the last two apply it to legal research, practice and the professional duties that govern it — with two practitioner conversations alongside.
Models modern academic integrity
Students are required to use AI — that is the point — but must disclose and verify. Grading rewards judgment, verification, and process, not raw model output. The course models the very norms of disclosure and verification it teaches, giving an institution a reflexive, defensible answer to the AI-in-assessment question.
Why it earns credit
A clean fit for the National Credit Framework
Recommended positioning, beyond the proposal: the course maps cleanly to the UGC/NEP National Credit Framework. Under the NCrF, 1 credit corresponds to about 15 hours of teaching plus roughly 30 hours of out-of-class work — approximately 45 hours of total learner engagement. The course delivers 16 contact hours (eight 90-minute core sessions of 60 minutes taught plus 30 minutes hands-on, two 90-minute Conversations with Experts, and a 60-minute evaluation session) plus readings and recommended graded work (about 30 out-of-class hours) — a clean single-credit course. It can be offered under NEP as a Value-Added Course (VAC) / Skill Enhancement Course / multidisciplinary elective, approved through the institution's own academic council (for a law school, without disturbing the BCI-governed LL.B. core). The course is rigorous enough for credit, with seven defined learning outcomes, a dedicated in-class evaluation, and a graded scheme (participation and hands-on work 10%, Legal Prompt Portfolio 25%, Hallucination Audit 25%, evaluation session 20%, capstone 20%) whose rubrics emphasise correctness of verification, soundness of prompt strategy, awareness of bias and sycophancy, ethical handling, and reflective insight.
What it does for employability
The verification-disciplined junior firms and the bench now want
Firms and the bench want AI-competent, verification-disciplined juniors. AI is already embedded in Indian legal practice — the Supreme Court runs SUPACE, SUVAS, and TERES — so graduates must actually operate these tools. But operating them badly is now sanctionable: Indian courts have imposed costs and recalled orders over AI-fabricated citations, and the Supreme Court has observed, in a pending 2026 matter, that such conduct would amount to misconduct. Employers therefore need juniors who can prompt AI effectively, match the right tool to the task, interrogate output, and verify every citation and proposition to source with a documented trail. The course produces exactly that: graduates who treat verification as a professional duty, understand confidentiality limits (never paste privileged data into public LLMs), and can pair human judgment with machine speed. The capstone — a verified AI-assisted work product with a verification trail — is itself a portfolio piece demonstrating this discipline to employers. For non-law students the same verification discipline transfers directly: any discipline that cites sources or relies on AI-generated analysis needs people who can prompt precisely, interrogate an output and check it before it is relied on.
What it needs
Light to host
No specialised computer lab. Bring-your-own-device, one standard room, Wi-Fi, and a presenter tool stack we provide.
Devices (BYOD)
Bring your own device: students use their own laptops for the hands-on demonstrations and their own phones for live polling — there is no specialised lab or hardware to provision. Students already have the devices and, crucially, need to operate the AI tools themselves.
Room
One standard room with a projector. A cohort of 30–60 works well for the hands-on sessions and is scalable with teaching assistants. No specialised computer lab required.
Connectivity
Wi-Fi sufficient for students to run AI tools on their laptops and vote on their phones. Because Wi-Fi and model variance can disrupt live demos, the presenter pre-tests all prompts and keeps fallback screenshots.
Presenter tool stack
Live AI demonstrations run on a chatbot, plus real legal-research tools, from the presenter's laptop via the projector. Live polling uses a hosted commit-then-reveal app (computationalthinking.ai/live — yes/no, scales, multiple-choice, numeric and word-cloud questions, with a Group A/B split for the anchoring experiment) that students open on their own phones; a hosted third-party polling tool is an accepted fallback.
Lab tooling
Default stack: a generalist model (Claude/ChatGPT/Gemini) plus free Indian Kanoon and, where available, trial access to one grounded legal-research tool — so students can compare a generic chatbot against a grounded, citator-backed engine.
Delivery schedule
16 contact hours: eight 90-minute core sessions (60 taught + 30 hands-on), two 90-minute Conversations with Experts, and a 60-minute evaluation session. Deliverable weekly (one core session a week, with the conversations and the evaluation scheduled into the sequence) or as an intensive over four days. Dates and fee are set with the host institution.
Expert conversation speakers
Each of the two Conversations with Experts is led by a different practitioner, tied to material the students have already been taught: one on AI in practice (building on Sessions 6–7), one on AI, regulation and the bench (building on Sessions 5 and 8). Speakers are confirmed with the host institution; the host is welcome to propose practitioners from its own network.
How to start
Start small, or go all in
A scaling offer — try a single session, run the full single-credit course, or have us equip your own faculty to deliver it.
- 01
Single-session guest lecture
A standalone 90-minute session drawn from the course — for example the opener (Session 1: the Ladder of Misinference, anchoring run live on the room, and the cautionary cases) or the research workflow (Session 8: the four-step authority check). A low-commitment entry point that showcases the mirror pattern and the live AI demonstrations for a single class or seminar.
- 02
Full single-credit course
The complete 16-contact-hour course delivered for academic credit — eight 90-minute core sessions with live experiments and a 30-minute hands-on segment each, two practitioner Conversations with Experts, a 60-minute evaluation session, a full assessment scheme with rubrics, and this course companion website — packaged as a single-credit VAC for submission to the institution's academic council.
- 03
Train-the-trainer
Equip the institution's own faculty to deliver the course in-house — handing over the syllabus, session decks, lab guides, assignment briefs and rubrics, the citation codebook, and the live-polling app, so the institution can run and re-run the course sustainably.
Ready to map it to your credit framework?
Request the syllabus & a callFAQs
Questions your council will ask
Bring it to your institution
Request the syllabus & a call
The course exists so your students never file a fabricated case — the mistake that drew a sanction in Mata v. Avianca, a ₹50,000 cost order in the Bombay High Court, and the Supreme Court of India's warning that it would be misconduct. Bring it to your students before the lesson arrives the hard way.
You enquire — a couple of lines is plenty.
We send the syllabus, the session plan, and the assessment scheme.
A call to map it to your credit framework and calendar.
We reply within one working day. No commitment — the syllabus is yours to evaluate. Or write to [email protected].