This syllabus is designed to ensure that entrepreneurial students do not merely use AI tools, but build with AI deliberately—integrating technical feasibility, business logic, and ethical responsibility. The ultimate aim is for each participant to leave the course with a product-ready, AI-enabled concept that is suitable for further development, incubation, or investment discussion.
By the end of this week, students will be able to:
Reframe entrepreneurial and market-driven problems into AI-addressable system questions
Conceptualize AI as an end-to-end system (data → logic → decision → value), rather than a standalone tool
Distinguish clearly between automation, augmentation, and intelligence in product design
Use AI tools to support problem discovery and market exploration
AI as a thinking and design framework for entrepreneurship
Problem formulation under uncertainty
Human–AI collaboration in early-stage innovation
Market-aware problem selection
Criteria for AI-suitable problems in startup contexts
Translating real-world pain points into input–output abstractions
Data as a proxy for reality: representational limits and bias
Common failure modes of “AI-first” startup ideas
AI-assisted market scanning (trend analysis, user pain extraction, competitor mapping)
Mini case analyses from engineering-driven startups, sustainability ventures, and AI-enabled services
A written AI Problem Statement (½–1 page), including:
target user or market,
decision or value gap,
proposed role of AI
Explicit identification of:
problem owner,
decision context,
human vs. AI responsibilities
In-class verbal articulation of why AI adds value—or why it may be limited
By the end of this week, students will be able to:
Decompose an AI-enabled product into interpretable pipeline stages
Understand core model logic at a conceptual level, without mathematical depth
Build and test a minimal AI workflow using accessible tools
Use AI tools to support technical design and validation reasoning
Anatomy of an AI pipeline in products and services
Supervised learning intuition for entrepreneurs
Evaluation as reasoning, not performance chasing
Data collection vs. synthetic or proxy data generation
Feature meaning, signal vs. noise, and information leakage
Regression and classification intuition through business examples
Training, testing, and validation as risk management tools
Interpreting outputs critically: uncertainty, error, and confidence
Using AI tools to draft pipeline logic, pseudo-code, and evaluation narratives
A working minimal AI prototype or simulation (e.g., simple prediction, scoring, or classification task)
An annotated pipeline diagram showing:
inputs,
processing logic,
outputs,
human intervention points
A short written reflection on model limits, uncertainty, and assumptions
By the end of this week, students will be able to:
Use AI tools to accelerate ideation, prototyping, and iteration
Translate technical outputs into clear user and business value
Design AI-supported systems, not AI-dominated products
Connect AI outputs to business logic and decision-making
Rapid AI-assisted prototyping
AI-supported decision systems
Value proposition design for AI-enabled products
Human-in-the-loop system architectures
Explaining AI outputs to non-technical stakeholders
Automation bias and over-reliance risks in startups
Aligning model outputs with market, regulatory, and operational constraints
Iterative refinement using AI-generated feedback, simulations, and scenarios
Translating technical capability into value propositions, pricing logic, and user benefit
A functional AI-supported prototype or workflow (conceptual or simulated)
A one-page AI Value Proposition, clearly mapping:
problem,
AI role,
user benefit,
business relevance
Demonstration of how a human decision-maker interacts with the system
By the end of this week, students will be able to:
Identify ethical, social, and environmental risks in AI-enabled products
Evaluate robustness, misuse potential, and deployment readiness
Generalize their AI–business thinking to new domains and markets
Articulate responsible innovation strategies for startups
Responsible and ethical AI in entrepreneurship
Risk, limitation, and failure analysis
Transferable AI design thinking
Bias, data provenance, and transparency in market-facing products
Reproducibility, documentation, and investor-facing clarity
Environmental and computational cost awareness (Green AI intuition)
High-level regulatory awareness for founders (non-legal framing)
Transition from course prototype to incubation or acceleration
Final AI Project Presentation, including:
problem definition,
pipeline design,
prototype or simulation,
risks and limitations
A written Responsible AI Reflection (ethical + technical constraints)
Clear articulation of how the approach can be transferred to another sector or problem