Academy · University programme

AI and Generative AI Curricula for
Business Students at Chitkara

Two courses delivered for Chitkara Business School BBA cohorts — one building conceptual AI and machine-learning literacy without code, the other a hands-on generative AI course for FinTech students. This is a curriculum profile: what the courses cover, how they run, and what students produce.

Programme context

Both courses run at Chitkara Business School for undergraduate business cohorts, and both are part of the same university relationship rather than separate engagements. They are built for different audiences: the AI and machine-learning course is taken by BBA students in logistics and supply-chain and aviation management, and is deliberately conceptual and no-code; Generative AI for Business is taken by BBA FinTech students and includes a technical foundation block alongside the applied syllabus.

The learning outcomes described below are the outcomes the courses are designed to produce. They are not a record of what any cohort achieved — no completion, grade or outcome data is published here.

University programmeTwo courses, one institutionUndergraduate business cohorts
BBA — LSCM & AVM

Introduction to Artificial Intelligence and Machine Learning

School
Chitkara Business School
Session
2026–2027
Contact hours
30 hours

Split evenly into two halves: 15 hours of conceptual, no-code AI and machine learning, and 15 hours of prompt engineering and AI-assisted research.

Learning objectives

  • Explain AI and machine learning concepts — supervised, unsupervised and reinforcement learning — and their business applications
  • Apply data preprocessing, feature selection and model-evaluation ideas to business scenarios without writing code
  • Interpret neural network architectures and weigh AI solutions against ethics, governance and responsible-AI frameworks
  • Design prompts for large language models and use AI tools to run structured research and synthesis

Part A — AI and ML core learning (15 hours, no code)

  • AI/ML foundations and business impact; AI vs ML vs deep learning
  • Data concepts and feature engineering, taught conceptually
  • Supervised learning, and evaluation metrics — accuracy, precision, recall, F1, confusion matrix
  • Unsupervised and reinforcement learning; clustering and K-Means
  • Neural networks and deep learning basics; CNN and RNN introduction
  • AI across marketing, finance, HR and operations
  • Generative AI and large language models
  • AI ethics and governance — bias, fairness, explainability, responsible-AI frameworks

Part B — Prompt engineering and research through AI (15 hours)

  • Prompt types — zero-shot, few-shot, chain-of-thought — and the role of context
  • Advanced techniques: prompt chaining, role prompting, iterative refinement, structured output
  • Prompts for market research, competitor analysis, financial summaries and HR tasks
  • AI-driven research methodology: literature search, summarisation, cross-referencing against authoritative sources, citation practice
  • Critical evaluation of AI outputs — identifying bias, fact-checking generated content
  • AI for presentations and documentation

Representative practical activities

  • Building reusable business prompt templates for market research and HR tasks
  • Synthesising literature with AI research tools and structuring the findings
  • Spot-the-error work on AI-generated text, and fact-checking against authoritative sources
  • A mini research project applying AI tools to a business problem, presented with the methodology, the prompts used, the findings and a critical evaluation

What students produce

  • A prompt portfolio of 10 prompts for a chosen business use case, each with its rationale, its iterations and an evaluation of the output
  • An AI-assisted research report, presented with the method and prompts that produced it
  • An individual research brief with a classroom presentation
BBA (FinTech)

Generative AI for Business

School
Chitkara Business School
Session
2026–27
Contact hours
30 theory + 28 practical = 58 listed contact hours

Six chapters of theory run alongside 14 labs, moving from what generative AI is, through prompt engineering and workplace writing, to visual content and retrieval-augmented generation.

Learning objectives

  • Explain how generative AI creates content, distinguish it from traditional AI, and map the tool landscape to workplace output requirements
  • Apply structured prompt engineering — task, context, constraints, output format, role and iterative refinement — to produce testable business outputs
  • Apply generative AI to workplace writing and analyse drafts through a four-dimension review gate: accuracy, tone, compliance and context
  • Create AI-generated visual assets and presentations, and evaluate them against professionalism, brand-consistency, IP, bias and disclosure checklists
  • Explain retrieval-augmented generation and verify AI outputs through a claim–source–match–context–action routine before business use

Curriculum structure

  • Ch 1 — Understanding generative AI: vocabulary, output types, the prompt–output loop, hallucination, and a task-type × output-stakes decision matrix
  • Ch 2 — Popular GenAI tools: the tool landscape, conversational AI, search-connected AI and grounding, image generation, and a tool-selection flowchart
  • Ch 3 — Prompt engineering basics: the four ingredients, clear and testable instructions, role prompting, iterative prompting, prompt patterns and the prompt audit
  • Ch 4 — GenAI for workplace writing: the guided drafting loop, structured email prompts, summary fidelity, report scaffolding, compliant marketing copy and the four-dimension review gate
  • Ch 5 — GenAI for images, design and presentations: diffusion basics, five-part image prompt anatomy, brand kits, deck generation, visual quality audit and disclosure ethics
  • Ch 6 — RAG and AI accuracy: why models give wrong answers, retrieval plus generation, grounded versus ungrounded answers, and the limits of RAG

Representative practical activities

  • Running a deliberately vague prompt across two models and comparing the context each one invents
  • Building a structured prompt from a messy business scenario, then repairing a flawed output through targeted follow-up prompts
  • PII-safe prompting with placeholders, against a prompt audit checklist
  • Drafting workplace emails from a six-part structured prompt, then reviewing the drafts for accuracy, tone, compliance and context
  • Checking a summary for the omission and the invented fact, and scaffolding a report with explicit verification placeholders
  • Taking one brief to several marketing formats, then rewriting a non-compliant version
  • Iterative single-variable refinement of image prompts, and a professionalism and risk triage of generated slides
  • Uploading a policy document and comparing grounded against ungrounded answers, then running a five-step verification routine

What students produce

  • A capstone generative-AI workflow portfolio presented end to end — the brief, the tool selection, the generation, the review gates and the verification trail
  • Reviewed workplace-writing deliverables: structured emails, verified summaries and report scaffolds
  • A mini campaign set produced against a brand checklist, and an audited presentation deck

Technical foundation modules

The handout also describes technical foundation modules delivered as content beyond the approved syllabus, through separately scheduled extra classes and lab sessions. These sit outside the 58 listed contact hours above.

  • Python basics — syntax, control flow, core data structures, functions, file handling, hands-on in a notebook environment
  • NumPy for numerical computing, and Pandas for data handling, including a data-cleaning exercise on a FinTech transactions dataset
  • Machine learning basics — train/test split, regression and classification demos, evaluation metrics, overfitting intuition
  • Deep learning basics — neurons, layers, activation functions, and embeddings as learned numeric meaning
  • The Transformer architecture — tokenisation, embeddings, self-attention intuition, and why context limits and hallucination follow from the mechanism
  • RAG internals — building a mini pipeline end to end: chunking, embedding generation, vector storage and similarity search, and grounded answers with citations

Agentic AI and multi-step AI workflows in FinTech operations, listed for advanced learners.

Assessment approach

Both courses are assessed on a mix of continuous practical work and examination. The AI and machine-learning course assesses through a case study and through the prompt portfolio and AI research report, alongside an end-term examination. Generative AI for Business assesses through continuous lab evaluations — a prompt-engineering practical and the capstone workflow portfolio — alongside sessional tests and an end-term examination.

Assessment components, their weightings and their scheduling are set by the department. One handout presents its weighting table explicitly as a sample for reference. Weightings and dates are therefore not reproduced here.

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