This course follows a project-based learning structure. Instead of isolated weekly homework tasks, students will develop one cumulative AI project, delivered through four structured assignments, each aligned with a specific week of the course.
Each assignment builds on the previous one. By the end of the course, students will have produced a coherent AI-enabled system concept that integrates problem formulation, technical reasoning, value creation, and responsible deployment.
Total number of assignments: 4
Assignment type: Cumulative, project-based
Submission format: Written documents, diagrams, and prototypes (conceptual or functional)
Focus: Reasoning, system thinking, and decision-making — not coding performance
Week: 1
Format: Written assignment (½–1 page)
Objective
This assignment focuses on problem formulation. Students are expected to move beyond vague ideas and articulate a problem that can be meaningfully addressed through AI as a system.
Required Content
The AI Problem Statement must clearly define:
• The target user or market
• The decision gap or value gap being addressed
• The proposed role of AI in the system
• The problem owner (who ultimately makes or acts on the decision)
• A clear distinction between human responsibilities and AI responsibilities
Expected Learning Outcome
Students demonstrate the ability to:
• Reframe real-world or market-driven problems into AI-addressable system questions
• Explain why AI adds value — or where its contribution may be limited
Week: 2
Format: Technical + written submission
Objective
This assignment introduces AI pipeline thinking. Students decompose an AI-enabled product into interpretable stages rather than treating AI as a black box.
Required Deliverables
Students must submit:
• A minimal AI prototype or simulation
(e.g., simple prediction, scoring, or classification task; functional or conceptual)
• An annotated pipeline diagram, explicitly showing:
• Inputs (data)
• Processing logic
• Outputs
• Human intervention points
• A short written reflection discussing:
• Assumptions
• Uncertainty
• Model limitations
Expected Learning Outcome
Students demonstrate:
• Conceptual understanding of supervised learning pipelines
• Awareness of data quality, signal vs. noise, and evaluation as risk management
• Critical interpretation of AI outputs rather than performance chasing
Week: 3
Format: Prototype + written value articulation
Objective
This assignment bridges technical insight and business value. Students must show how AI outputs translate into real-world decisions and benefits.
Required Deliverables
• A functional or simulated AI-supported prototype or workflow
• A one-page AI Value Proposition, explicitly mapping:
• Problem → AI role
• AI role → User benefit
• User benefit → Business or operational relevance
• A clear explanation of how a human decision-maker interacts with the system
Expected Learning Outcome
Students demonstrate the ability to:
• Design AI-supported systems rather than AI-dominated products
• Communicate AI outputs to non-technical stakeholders
• Align technical capability with market, regulatory, and operational constraints
Week: 4
Format: Final presentation + written reflection
Objective
The final assignment emphasizes responsible deployment and transferability. Students critically evaluate risks, limitations, and broader impacts of their AI-enabled systems.
Required Deliverables
• A Final AI Project Presentation, including:
• Problem definition
• Pipeline design
• Prototype or simulation
• Identified risks and limitations
• A Responsible AI Reflection, addressing:
• Ethical considerations and potential bias
• Data provenance and transparency
• Environmental and computational cost awareness
• Misuse and failure scenarios
• A transferability statement, explaining how the same AI reasoning framework could be applied to a different sector or problem
Expected Learning Outcome
Students demonstrate:
• Responsible innovation thinking
• Awareness of ethical, social, and environmental implications
• Ability to generalize AI–business reasoning beyond a single use case
Assignments are evaluated based on:
• Clarity of reasoning
• System-level thinking
• Critical awareness of limitations
• Alignment between problem, AI role, and value creation
Technical complexity or advanced coding is not required. The emphasis is on thinking with AI, not merely using AI tools.