Skip to main content

Track D · AI Literacy for All Disciplines

Track D

AI literacy for every degree at your university — humanities, sciences, law, education, journalism, healthcare, and more. Three layers: universal module, discipline modules, career bridge. NEP-aligned.

Track D curriculum snapshot — three layers and ten discipline modules.

The course list

12 courses in Track D.

Each module is co-evaluated by Kompas resident faculty and your university's academic council. Module content refreshes every six months.

D1

Universal AI Literacy Module

Sem 1–23 credits

Skill outcomes

  • Explain what LLMs do and do not do, including hallucination and reasoning limits
  • Operate ChatGPT, Claude, Gemini, Copilot, NotebookLM, and Perplexity to assessed proficiency
  • Apply structured prompting and chain-of-thought to verify AI outputs
  • Navigate DPDP Act basics, deepfake risks, IP, and academic integrity with AI
Updated 2026-04

D2

AI in Journalism & Mass Communication

Sem 5–63 credits

Skill outcomes

  • Use AI tools for fact-checking and source verification
  • Conduct AI-assisted reporting and automated transcription workflows
  • Detect deepfakes and synthetic media in news contexts
  • Apply ethical frameworks for AI use in journalism

Prerequisites: D1

Updated 2026-04

D3

AI in Law

Sem 5–63 credits

Skill outcomes

  • Conduct contract analysis and clause extraction using LLMs
  • Run e-discovery and document review with AI tools
  • Perform legal research using LLM-augmented case retrieval
  • Build regulatory scanning workflows for compliance updates

Prerequisites: D1

Updated 2026-04

D4

AI in Psychology & Behavioural Science

Sem 5–63 credits

Skill outcomes

  • Apply sentiment analysis to behavioural and clinical text data
  • Evaluate AI tools used in clinical screening and triage
  • Reason about ethical considerations in AI-assisted mental health work
  • Interpret AI outputs in research design and behavioural studies

Prerequisites: D1

Updated 2026-04

D5

AI in Liberal Arts & Humanities

Sem 5–63 credits

Skill outcomes

  • Use LLMs for large-scale text analysis and corpus interrogation
  • Apply digital humanities methods with AI-assisted tooling
  • Integrate AI into cultural and historical research workflows
  • Critically appraise the limitations of AI in humanistic inquiry

Prerequisites: D1

Updated 2026-04

D6

AI in Education (B.Ed.)

Sem 5–63 credits

Skill outcomes

  • Design personalized learning pathways using AI tutors
  • Build assessment and grading workflows with AI assistance
  • Evaluate AI classroom tools for pedagogical fit and bias
  • Develop lesson plans that integrate AI as a teaching aid

Prerequisites: D1

Updated 2026-04

D7

AI in Healthcare & Life Sciences

Sem 5–63 credits

Skill outcomes

  • Interpret medical imaging outputs from AI diagnostic models
  • Use AI tools for bioinformatics sequence analysis
  • Evaluate AI-assisted clinical decision support systems
  • Recognize regulatory and safety constraints on AI in healthcare

Prerequisites: D1

Updated 2026-04

D8

AI in Economics & Public Policy

Sem 5–63 credits

Skill outcomes

  • Combine econometric methods with machine learning techniques
  • Run policy simulations using AI-driven models
  • Apply AI to development economics and impact evaluation
  • Critique AI-driven policy recommendations for bias and validity

Prerequisites: D1

Updated 2026-04

D9

AI in Performing Arts, Music & Film

Sem 5–63 credits

Skill outcomes

  • Produce music using Suno, Udio, and AI music production tools
  • Draft and revise screenplays with AI screenwriting assistants
  • Generate AI-assisted VFX and visual sequences
  • Navigate copyright and authorship issues in generative arts

Prerequisites: D1

Updated 2026-04

D10

AI in Sports & Hospitality

Sem 5–63 credits

Skill outcomes

  • Apply analytics to player, team, and operational performance data
  • Use vision AI for movement, occupancy, and event detection
  • Build demand forecasting models for hospitality operations
  • Translate AI outputs into operational decisions for venues

Prerequisites: D1

Updated 2026-04

D11

AI in Languages & Translation

Sem 5–63 credits

Skill outcomes

  • Use LLM-powered translation tools for professional output
  • Work with regional language AI models including Indic NLP
  • Build NLP pipelines for low-resource Indian languages
  • Evaluate translation quality and cultural fidelity in AI output

Prerequisites: D1

Updated 2026-04

D12

Career Bridge Module

Sem 7–82 credits

Skill outcomes

  • Build an AI-augmented portfolio and GitHub presence
  • Produce AI-fluent CVs, cover letters, and LinkedIn profiles
  • Prepare for interviews using AI mock-interview and coaching tools
  • Ship at least one discipline-relevant AI portfolio project for placement

Prerequisites: D1

Updated 2026-04

Skill Score on exit

Mastery is measured, not assumed.

Every Track D graduate exits with a verifiable Kompas AI Skill Score, calibrated against an industry-co-signed rubric and applied identically across every campus we operate on.

  1. Foundational
  2. Practitioner
  3. Applied
  4. Advanced
  5. Expert
Track D graduates typically exit between Practitioner and Applied. Industry-sponsored capstone work moves the strongest students into the Advanced band.

Capstones from this track

What a Track D student ships at the end.

Examples below are illustrative until real partner-cohort capstones land. The shape — sponsor, problem, outcome, public artefact — does not change.

See the full capstone library
Track DJournalism · VerificationIndependent newsroom

AI fact-checking pipeline for an independent newsroom

Problem

A small newsroom covering state politics needs to verify videos, audio clips, and forwarded screenshots faster than the news cycle moves.

Target outcome

A three-step pipeline (provenance check → entity verification → contradiction surfacing) embedded in the newsroom's CMS. Cuts average verification time from 90 minutes to under 20, with a documented escalation rule for items the pipeline cannot adjudicate.

Track DLaw · LegalTechUniversity legal-aid clinic

Contract-analysis tool for a legal-aid clinic

Problem

A campus legal-aid clinic reviews tenancy and labour contracts for low-income clients. Pro-bono lawyers cannot keep up with intake volume.

Target outcome

An LLM-assisted clause-extraction tool with a strict citation-verification gate. Reviews contracts ~3× faster while flagging cases that still require full pro-bono review. Zero hallucinated clauses in the rollout audit; clinic adopts as standard intake step.

Other tracks

The other four tracks in the Kompas portfolio.

Bring Track D to your campus

Schedule a partnerships conversation.