Overview

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.

Syllabus

Introduction to AI Code Generation

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.

Basic Prompt Engineering

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.

Vibe Coding

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.

Activity 1: Simple Paper-Rock-Scissors Game

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.

Activity 2: Code Generation for Data Mining and Data Analysis

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.

Activity 3: Code Generation for Web Applications

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.

Activity 4: Code Generation for Research and Data Visualization

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.