The essential AI course for all Cornell students, coming Spring 2027.

SUBJECT: Everyday AI: Learning to use AI effectively and responsibly
CATALOG TITLE: INFO 1720, Introduction to Applied AI
SCHEDULE: TBD
DISTRIBUTION REQUIREMENTS: (DLG-AG), (MQL-AG), (SDS-AS)
PREREQUISITES: None
 


Course Description

Artificial Intelligence (AI) is reshaping how people learn, create, collaborate, solve problems, and make decisions every day and in every domain. This course is for anyone who wants to learn about AI and how to use it effectively and responsibly; no prior experience or technical background is required. You will learn the fundamentals as well as the latest technologies in the AI space in ways that are accessible, relatable, and useful for your work, life, and community. Through diverse examples, hands-on activities, and projects, you will learn to build responsibly with AI by drawing on its capabilities, recognizing its limitations, and critically assessing its societal impacts. You will progress from building AI-supported workflows that meet your own needs, to creating applications that serve your discipline or community, to tackling problems collaboratively in new contexts. The course aims to equip you to conduct your own explorations, analyses, and creations with AI in everyday life.

 

two students sit and work on laptops together

Learning Outcomes

By the end of the course, you will be able to:

  1. Explain how AI systems work. Describe the main approaches behind AI (rules, search, machine learning, deep learning, and generative models) in plain terms, and use that understanding to predict what a system will do well and where it will fail.
  2. Use AI effectively and responsibly. Choose suitable tools and strategies, verify outputs, weigh tradeoffs, and judge when AI should or should not be used.
  3. Design and build AI-supported tools. Create workflows for your own needs and for problems in your discipline or community, documenting what the AI does, what people do, and why.
  4. Analyze AI’s impacts. Critically assess how AI affects individuals, communities, and institutions, and account for those effects in your design decisions.
  5. Apply AI in new contexts. Learn about an unfamiliar domain, identify its stakeholders and the problems that matter to them, and develop and present well-justified AI-supported solutions.

Course StructureA color photo showing 2 women working at a laptop.

 

  • Lectures: Build a consistent explanation of how AI works from the ground up, with no technical background needed. The content starts with rule-based systems and search, goes through machine learning and deep learning, and then moves on to generative models and their limitations. Later lectures look at AI's impacts on individuals, communities, and institutions.
  • Sections are hands-on labs. Each one pairs with a lecture concept: you build a small working tool that uses it, then document it on a system card (see below).
  • Homework: Reading elaborations and activities. Reading elaborations help you engage with course materials dynamically. Activities are focused explorations of concepts to check your own understanding. 
  • Projects: Each project is larger in scope than the last, and each has its own focus on impacts.
    • Individual project 1: building AI for yourself. Impact focus: privacy, overreliance, and when AI helps or hinders your own learning and work.
    • Individual project 2: building AI for your discipline or community. Impact focus: fairness, access, and the needs of the people who would use it.
    • Final group project: building AI for an unfamiliar domain. Impact focus: effects on institutions, labor, and accountability. It ends with a public showcase.
  • Project pitches: Each project starts with a short pitch covering a specific problem (who has it and how they handle it now), the AI’s role, its inputs and outputs, and at least one real ethical question the design has to address.
  • System cards: Every project you build has a system card, a structured document covering what the system does, for whom, its inputs and outputs, and which tasks the AI does versus a person. You'll use the same template all semester and revise the card whenever a design decision changes.
  • AI use: You’re encouraged to use AI across the course, including for building projects. Where you’re asked for your own judgment or opinion, such as in reflections, peer feedback, and discussion, we will give specific guidance on how to limit your use of AI.
     

Sample Projects 

In this course, regardless of your prior experience with computing or AI, you will develop AI-supported projects and/or workflows that solve problems related to your own interests—in agriculture, engineering, medicine, business, hospitality, social sciences, arts, public policy, or beyond. Here is a sample list of problems that you might try to solve with AI assistance in this class, some of which are real projects from prior relevant offerings:


AI for yourself

  • Organizing study/research/meeting notes
  • Studying scientific concepts or historical arguments
  • Planning your e.g., budget, subscription, or travel
  • Tracking digital behavior to help reduce screen time with actionable suggestions
  • Planning and creating social media content


AI for your community or discipline

  • Organizing, planning and executing a community garden
  • Enabling work to be done digitally that previously was done on paper
  • Developing and/or comparing engineering designs
  • Producing interactive and exploratory plots of data in your domain of study
  • Helping analysis and organization of data for the wet labs
  • Creating and tracking choreography
  • Analyzing customer feedback and exploring business strategies
  • Analyzing creative texts to learn patterns and collect meaningful statistics/data
  • Analyzing social media content to help our society be more informed


AI for new contexts

  • Creating a mockup/prototype for your startup ideas
  • Exploring public comments on a policy proposal
  • Discovering and uncovering overlooked stories
  • Deciding when (and when not) to use AI
  • Estimating the number of people in line at a coffee shop
  • Suggesting DIY project ideas given existing materials
  • Building a friend-discovery platform that encourages people to connect offline


Instructors

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Lisa Huang

Instructor, Assistant Professor of Information Science

Lisa Huang is an Assistant Teaching Professor in the Department of Information Science at Cornell University. Her teaching and research focus on human-AI interaction in programming systems, with a particular interest in building programming support for non-programmers. She is also interested in understanding how programmers of all kinds reason about code and debugging, and how programming systems can be designed to better support their needs. Her work spans human-computer interaction, AI-assisted programming, and computing education, and has appeared in venues such as CHI, ICSE, SIGCSE, and VL/HCC.

 

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Rene Kizilcec

Co-instructor, Associate Professor of Information Science
 

Rene Kizilcec is an associate professor of information science at Cornell, where he directs the Cornell Future of Learning Lab. Kizilcec studies behavioral, psychological, and computational aspects of technology in education to inform practices and policies that promote learning, equity, and academic and career success. His work has appeared in Science and the Proceedings of the National Academy of Sciences, won multiple awards, and is funded by the National Science Foundation, Schmidt Futures Foundation, and Jacobs Foundation. Kizilcec holds a Ph.D. in communication and M.Sc. in statistics from Stanford University.
 

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This course was developed in consultation with: Matt Wilkens, Bowers Associate Dean of Education; David Mimno, Department Chair of Information Science; Steve Jackson, Vice Provost for Academic Innovation at Cornell; and Sorin Lerner, Dean of Cornell Bowers