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Join the GenAI Minor

Preparing students across disciplines to harness generative AI and become the next generation of innovators.

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About the Program

As generative AI rapidly transforms the way we create, communicate, and problem-solve, it is becoming essential for students across all disciplines—not just computer science—to understand and engage with these technologies. This minor is designed to equip non-CS students with foundational knowledge of how generative AI works, hands-on experience with state-of-the-art tools, and critical frameworks for using them responsibly in their fields. By bridging technical concepts with domain-specific applications, the program empowers students to be thoughtful, informed innovators—ready to lead in a world where AI is no longer optional, but integral.

What will you learn?

  • How to Evaluate AI systems:
    • Assess the performance, capabilities, and limitations of generative AI systems in context
    • Compare AI tools and models using appropriate qualitative or quantitative criteria
  • Understand AI architecture:
    • Explain, at a conceptual level, the underlying structures and training processes of generative AI systems
    • Describe how architectural and training choices influence outputs, risks, and use cases
  • How to Apply AI to discipline-specific challenges:
    • Identify research, analytical, or creative challenges in their field that are appropriate for AI-supported approaches
    • Select and justify AI tools and methods aligned with disciplinary norms and constraints

What Makes this Program Different?

  • Generative AI is about creating new content with AI.
  • Data Science is about discovering insights from data.
  • Computer Science is about building the foundations of computing.

Required Courses

  • AI 1010: Survey of Generative AI Tools

    This course provides a hands-on introduction to generative AI tools and their applications across various domains. Students will explore a range of AI-powered tools for text, image, audio, and video generation, analyzing their capabilities, limitations, and optimal use cases. The course will also introduce AI agents examining their functionality and comparing different models in real-world scenarios. Through case studies, side-by-side tool evaluations, and applied projects, students will gain practical experience in selecting and utilizing the right AI tools effectively.

  • AI 1020: Foundations of Generative AI

    This course introduces students to the core concepts and practical techniques of generative artificial intelligence (AI), offering a broad survey of models, use cases, and application areas. Students will engage in hands-on exploration of topics such as prompt engineering, foundational AI architecture, and the stages of training involved in building modern AI systems. The course also addresses security and privacy considerations, the limitations of current technologies, and augmentation approaches, including agentic systems. By the end of the course, students will be prepared to responsibly use AI systems and critically interpret and evaluate new developments and discussions about generative AI in academia, research, and society.

  • AI 2200: Ethics of Generative Artificial Intelligence

    This course offers an introduction to the ethical implications of generative artificial intelligence.  Students will learn about core ethical issues in generative AI, including intellectual property, privacy, bias, fairness, workforce displacement, and artificial general intelligence. Students will engage in activities designed to explore differing and/or opposing viewpoints, and experience ethics as an interdisciplinary, iterative practice.

  • AI-integrated course

    Discipline-based courses in which generative or predictive AI serves as a core method for creating, analyzing, or solving problems, while fostering critical and responsible engagement with its use in the field.

Schedule an Exploration Appointment

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