This course introduces learners to the practical use of artificial intelligence for generating and improving computer code across multiple disciplines. Designed for students and professionals from any major, it requires no prior programming or AI experience. Learners explore how AI tools support coding tasks through effective prompt design, hands-on experimentation, and guided examples using Python. The course emphasizes real-world applications, including data analysis, basic web development, and research-oriented data visualization. Participants also learn to critically evaluate AI-generated code, identify limitations and errors, and refine outputs for practical use. By the end of the course, learners will be able to responsibly integrate AI-assisted coding into academic, professional, or creative projects, enhancing efficiency, problem-solving skills, and confidence in working with modern AI technologies.
AI Code Generation for Everyone
Recommended experience
What you'll learn
Use AI tools to generate, modify, and improve code for real-world tasks without prior programming experience.
Apply prompt engineering techniques to guide AI models in producing accurate and useful code.
Evaluate and refine AI-generated code for data analysis, web applications, and visualization projects.
Details to know
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February 2026
7 assignments
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There are 7 modules in this course
Welcome to the course! This topic introduces AI code generation as a transformative approach to software development, where large language models generate code from natural language prompts or contextual cues. Learners examine how AI-assisted coding differs from traditional programming, its historical evolution, practical benefits, limitations, and its growing role in modern development workflows.
What's included
2 videos6 readings1 assignment
This topic explores prompt engineering as the skill of translating human intent into precise instructions for large language models. Learners study how prompt clarity, specificity, context, and format shape AI outputs, and practice iterative refinement techniques to reliably generate accurate, useful, and ethical code solutions.
What's included
1 video5 readings1 assignment
Vibe coding introduces a creative, conversational style of building software by describing goals, aesthetics, and behavior in everyday language while AI handles technical implementation. Learners explore how vibe coding lowers technical barriers, accelerates prototyping, and empowers non-coders and developers alike to rapidly turn ideas into working applications.
What's included
1 video4 readings1 assignment
This hands-on module applies AI tools to generate, interpret, and modify Python programs. Learners work with basic syntax, control structures, and logic, using AI to accelerate development while learning to read, test, and debug generated Python code.
What's included
2 videos2 readings1 assignment1 discussion prompt
This topic demonstrates how AI can assist with data mining and data visualization tasks. Learners generate code to collect, analyse, and visualize data, while developing skills to assess data quality, interpret results, and refine AI-generated analytical workflows.
What's included
2 videos2 readings1 assignment1 discussion prompt
This module introduces AI-assisted web development by guiding learners to create a simple personal webpage. Using AI-generated HTML, CSS, and basic interactivity, learners focus on structure, design, and usability while understanding how web components work together.
What's included
2 videos2 readings1 assignment1 discussion prompt
This topic explores AI-assisted coding for research and simulation tasks. Learners use AI to generate code for simulations, experiments, and visualizations, emphasizing interpretation, and presentation of results in academic and exploratory research contexts.
What's included
4 videos4 readings1 assignment2 discussion prompts
Instructor
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Felipe M.
Jennifer J.
Larry W.
Chaitanya A.
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