MSBAi: AI-First Strategy Document

Program-level details: See program/CURRICULUM.md

Launch: Fall 2026 (Fall 1 term) Strategic Positioning: “Analytics-to-Action with AI” Key Differentiation: AI-native curriculum + applied experiential learning + flexible pathways Informed By: Undergrad AI integration strategy + IBC market research + MSBA course portfolio

Executive Summary

MSBAi is an 18-month, 36-credit online Master of Science in Business Analytics designed with AI as the central organizing principle, not a supplementary topic. Every course integrates practical AI tools, prompt engineering, and LLM-assisted analysis.

Program Structure:

Duration: 18 months (4 semesters + 1 summer)
Credits: 36 total (24 core + 8 elective + 4 capstone)
Format: 8-week modular courses within 16-week semesters
Common Thread: Python + Jupyter Notebooks across all courses

Key Features:

AI-First Curriculum: Every course integrates AI tools and workflows (not optional)
Stackable Design: 8-week modular courses enable flexible pacing and future certificate pathways
Applied Learning: Portfolio-based assessment + real client capstones
Career-Focused: Designed for promotion seekers and career pivoters

Target Market: Working professionals seeking career advancement in analytics + AI, emphasizing affordability, flexibility, and job-ready outcomes.

Part 1: AI-First Curriculum Philosophy

Why “AI” Must Be in the Name & Core

The IBC market research identified a critical insight: “AI fluency + adaptability” is the top emerging capability employers expect. Yet most programs treat AI as an elective topic or afterthought.

MSBAi Strategic Differentiation:

AI is not optional. Every student graduates with:
- Hands-on LLM experience (ChatGPT, Claude, open-source models)
- Prompt engineering, RAG pipeline building (LangChain, vector DBs), and agentic AI patterns
- Practical understanding of model evaluation, MLOps/LLMOps, and responsible AI
- Portfolio of AI-assisted and AI-built analytics projects
AI-native tools, not AI as theory. Students use AI as a tool to accelerate analytics work:
- AI for data exploration and hypothesis generation
- AI for code generation and debugging (pair programming with LLMs)
- AI for automating routine analysis tasks
- AI for storytelling and communication
Aligned with Gies’ Campus AI Framework. MSBAi contributes to the official Campus AI curriculum tracks:
- AI Basics/Fundamentals: All students achieve literacy
- AI & ML Technologies: Mid-level competency in applied ML
- Agentic Systems & Workflows: Build RAG systems, implement function-calling agents, design AI-enhanced business processes (delivered via GenAI elective + BADM 576 LLMOps)
- Human-Centric AI: Ethics, governance, responsible AI integration

Programming Model: L-C-E (Literacy -> Competency -> Expertise)

All MSBAi learning outcomes follow the Literacy-Competency-Expertise progression (adapted from UNESCO AI framework):

Level	Definition	Bloom’s Level	In MSBAi Context
Literacy (L)	Foundational understanding of AI concepts, capabilities, limitations	Remember, Understand	“Explain what an LLM is; recognize its limitations”
Competency (C)	Practical application skills using AI tools in business workflows	Apply, Analyze	“Use Claude/ChatGPT to accelerate your analysis; evaluate outputs critically”
Expertise (E)	Advanced development, customization, and strategic application	Evaluate, Create	“Design AI-enhanced analytics workflows; lead adoption initiatives”

Progression Path:

Fall 1-2 (Core Courses): L -> C (Build AI literacy + foundational competency)
Spring 1-2 (Electives): C -> E (Deepen competency, start moving to expertise)
Summer-Fall 3-4 (Specialization + Capstone): E (Apply expertise to real business problems)

Part 2: Core Curriculum (8-Week Redesign)

Design Principles

Python + Jupyter Notebooks as computational backbone
- All students work in same environment (reproducibility, peer learning, portfolio building)
- Jupyter books with integrated text, code, visualizations
- Pre-built environments (Google Colab, cloud-hosted JupyterHub)
- Students build portfolio of notebooks across entire degree
AI-assisted content delivery
- Shorter, denser lectures (10-15 min vs. traditional 50 min)
- Interactive coding walkthroughs with AI pair-programming examples
- Auto-generated summary notes and study guides
- Students use Claude/ChatGPT for personalized tutoring
Project-based assessment (no traditional exams)
- 2-3 major projects per 8-week course
- Continuous mini-assessments (weekly labs, reflections)
- Real datasets, real business questions
- Portfolio-ready outputs (GitHub repos, Jupyter notebooks, dashboards)
Staggered skill progression
- Courses are sequenced across semesters to manage workload (not run in parallel)
- Skill themes — visualization, regression, ML — build progressively across the program
- Cross-course connections reinforced through shared Python/Jupyter backbone and spiral curriculum
AI-enhanced collaboration
- Students use AI agents for research, literature review, brainstorming
- Teach “AI crediting” (how to cite and acknowledge AI contributions)
- Build study groups using shared Claude projects for collaborative coding

Core Courses (36 credits total)

For complete course details, semester timeline, and credit breakdown, see program/CURRICULUM.md

For per-course AI integration details, see individual course files in courses/

L-C-E Framework: Curriculum Progression

All MSBAi learning outcomes follow the Literacy-Competency-Expertise progression:

Level	Definition	Bloom’s Level	Curriculum Stage
Literacy (L)	Foundational understanding of AI concepts, capabilities, limitations	Remember, Understand	Fall 2026 — Core foundations
Competency (C)	Practical application skills using AI tools in business workflows	Apply, Analyze	Spring 2027-Summer 2027 — Infrastructure + decision-making
Expertise (E)	Advanced development, customization, and strategic application	Evaluate, Create	Fall 2027-Spring 2028 — ML + capstone

Example: BADM 554 Learning Outcomes

Literacy: Explain how databases store, retrieve, and process data; understand SQL’s role in data pipelines
Competency: Write SQL queries in Python; use pandas DataFrames to manipulate data; design basic data models
Expertise: Design scalable data systems; integrate APIs into Python workflows

All courses follow this L-C-E structure. See individual course syllabi for complete learning outcome specifications.

Part 3: Specialization Pathways & Electives

See individual elective course pages: GenAI, Quantum, Elective 3 (TBD). For future elective ideation, see Future Electives & Tracks.

Elective Tracks

Elective tracks are under development. See Future Electives & Tracks for ideation on specialization pathways including finance, marketing, healthcare, and data engineering.

Part 4: Fall 2027-Spring 2028 — Specialization & Capstone

Fall 2027 (8 credits)

BADM 576: ML & Data Science (4 credits, Weeks 1-8)
Specialization Elective (4 credits, Weeks 9-16) — Finance, Healthcare, Marketing, or Data Engineering track

Spring 2028 — Capstone/Practicum (4 credits)

Two-Part Structure:

Part 1: Portfolio Assembly (Weeks 1-2)

Curate 3-4 best projects from degree (Jupyter notebooks, dashboards, code repositories)
Create GitHub portfolio + personal website
Practice portfolio pitch for job interviews

Part 2: Applied Client Project (Weeks 3-8)

Real-world consulting project with Research Park partners
Students tackle actual business problem
Deliverable: Python code + Power BI dashboard + executive summary
AI-enhanced: Students use Claude for project planning, peer review, and documentation

Conclusion