Xinde (Cinder) Zhang, Ph.D.
We stand at a critical juncture where technology is fundamentally reshaping finance education. My teaching philosophy, shaped by my multidisciplinary background in computer science, mathematics, and finance, centers on preparing tomorrow's finance leaders for this technological transformation. Recent industry data validates this approach—by 2024, over a third of consumers will rely on AI for financial advice, while financial institutions are rapidly integrating AI into their core operations. This evolution demands professionals who can not only use technology but shape its implementation in finance.
My approach to bridging traditional finance with emerging technologies has proven highly effective, as demonstrated by our Financial Analytics concentration growing to 138 majors within two years. More importantly, with starting salaries for AI-skilled finance professionals ranging from $100,000 to $150,000, the market clearly values this integrated skill set. As detailed in the Appendix, this success stems from a carefully structured approach that transforms complex technical concepts into accessible, practical knowledge for business students.
Teaching technology to business students requires recognizing and leveraging their unique strengths. Business students bring valuable assets to technical learning: strong analytical thinking developed through financial analysis, pattern recognition skills honed through market study, and a practical problem-solving orientation. These characteristics form a strong foundation for technical learning when properly leveraged.
The journey from business understanding to technical implementation follows a natural progression. Students begin with familiar financial concepts, using their business intuition as a bridge to understanding technical implementations. This progression, illustrated through a detailed teaching session in the Appendix, demonstrates how students move from conceptual understanding to practical implementation through structured, scaffolded learning experiences.
My teaching philosophy rests on three integrated principles, each validated by current industry trends and learning science research:
As financial institutions increasingly embed AI in their operations, students must develop both technical proficiency and critical judgment. This dual competency enables them to harness AI effectively while maintaining independent analytical capabilities. The approach detailed in the Appendix demonstrates how students learn to use AI tools while developing core programming skills, preparing them for a future where human judgment and AI capabilities work in synergy.
Complex technical concepts become accessible when presented through familiar financial contexts. Visual representations of trading strategies, for instance, serve as a bridge to understanding programming logic. This approach, demonstrated through practical examples in the Appendix, helps students develop both technical skills and deeper financial understanding simultaneously.
The goal extends beyond technical proficiency to developing leaders who can drive technological transformation in finance. This requires a balanced development of technical competence and business acumen. Through structured projects and real-world applications, students learn to evaluate technical solutions within broader business contexts.
The practical implementation of this philosophy relies on three integrated components:
Visual tools serve as a crucial bridge between financial concepts and technical implementation. Flowcharts of trading strategies, for instance, help students understand both the financial logic and its programming implementation. This approach makes complex technical concepts accessible while reinforcing financial understanding.
Technical skills develop through carefully structured progression. Students begin with visual representations of financial logic, advance to basic programming concepts, and ultimately implement complex financial applications. This progression, detailed in the Appendix, ensures students build confidence and competence simultaneously.
The learning environment integrates AI tools while teaching students to understand their capabilities and limitations. Students learn to use AI for learning enhancement while developing independent problem-solving abilities. This balanced approach prepares them for an industry where AI augments rather than replaces human judgment.
The effectiveness of this philosophy manifests in several key outcomes:
Student Success: Strong placement records in both traditional finance and fintech roles demonstrate the value of our integrated approach. Students successfully implement complex financial technologies while maintaining strong business acumen.
Program Growth: The rapid growth of our Financial Analytics concentration reflects strong student demand and industry recognition of our approach.
Industry Alignment: Our graduates' success in both traditional finance roles and technical positions validates our balanced approach to technology integration.
Looking ahead, I am committed to:
- Expanding experiential learning opportunities through industry partnerships
- Developing new frameworks for human-AI collaboration in finance
- Preparing leaders for continuous technological evolution
- Contributing to the broader dialogue about technology integration in finance education
The future of finance education lies in developing leaders who can seamlessly integrate technical capabilities with financial expertise. Our approach, as demonstrated through practical implementation in the Appendix, ensures students develop into confident, tech-enabled business leaders ready to drive innovation in finance.
- Explicit Objective: Python Control Flow
- Conditional Statements
- While/For Loops
- Implicit Objectives:
- AI-Assisted Learning
- Learning How to Learn FAST
- Prompt Engineering
- Tools:
- Flowchart
- ChatGPT
- Whimsical Diagram
- Approach: Mini-workshop/seminar format with hands-on practice
- Action: Students repeat learning objectives out loud
- Purpose: Ensures active engagement and clear understanding of goals
- Example: IF Statements and Loops
- Action: 3-minute reading of W3Schools/course website tutorial
- Purpose: Self-paced initial exposure to concepts
- Focus: Basic syntax and structure of control flow statements
- Prompt Template:
"You are a patient coding tutor who specializes in teaching complete beginners. I am a finance student who has never coded before. Please explain IF statements like you're teaching a 10-year old, using a simple example about deciding to buy ice cream based on having enough money." - Action: Students learn to craft specific prompts for ChatGPT
- Purpose: Master both Python concepts and effective prompt engineering
- Technique: Prompt engineering--Role Playing
- Hint: Can you ask the AI to improve the Prompt for something you would like to learn?
- Action: Walk through Flowchart as a class
- Purpose: Ensure understanding of logic flow
- Focus: Connection between visual representation and code
- Trading Game Concept:
- Buy when price < 7-day average
- Sell otherwise
- Run for 20 days
- Task:
- Students refine game description with ChatGPT with precision
- Students prepare prompts for generating precise Flowchart
- Students walk through the Flowchart and approve the Trading Game logic
- Trading Game Logic Explanation:
- Buy when price < 7-day average
- Sell otherwise
- Run for 20 days
- Task:
- Student explains their Flowchart logic to their neighbor
- Their neighbor does the same
- Flowchart is approved by peer
- Action: Convert Trading Game Flowchart to Python code using careful prompting
- Focus: Precise translation of visual logic to code
- Outcome: Functional trading strategy implementation
- Multi-Modal Learning:
- Visual (Flowcharts) → Logical (Code) → Practical (Trading)
- Bridges abstract concepts with real-world applications
- AI Integration:
- Students learn WITH AI, not FROM AI
- Prompt engineering as a transferable skill
- Technical: Python control flow mastery through finance context
- Meta-Learning: AI-assisted problem-solving strategies
- Professional: Communication skills with AI tools
This framework prepares students for the AI-augmented future:
- Adaptable to various programming concepts
- Applicable across business disciplines
- Scalable to any technical subject requiring structured thinking