AI Workshop for Development, Research, and Academic Instruction
Description
This comprehensive 6-hour workshop is specifically designed to bridge the gap between basic AI usage and advanced academic application by shifting the focus from simple chat interfaces to sophisticated CLI and IDE environments where participants will master modern development workflows powered by Google Gemini. The curriculum is divided into two core segments starting with a theoretical foundation where participants explore the Gemini ecosystem including the Canvas interface and API tools while mastering advanced Prompt Engineering techniques for refactoring and debugging. This is followed by a practical lab where attendees build an AI-driven mini-project tailored to research or teaching needs with a critical focus placed on the pedagogical shift of helping lecturers analyze AI-generated solutions and redesign student assignments to maintain academic integrity in the GenAI era.
Intended audience
Academic researchers and lecturers with programming background seeking to integrate GenAI into research and teaching via advanced CLI and IDE tools. This program helps innovation-driven faculty adapt curriculum standards and student assignments to the GenAI era.
Topics
Theoretical Foundation and Work Methodologies
Exploring the Gemini Ecosystem: Working with the Chat interface, utilizing Canvas capabilities for writing code and academic text, and transitioning to working with Gemini API and CLI tools.
Prompt Engineering Methodologies for Programmers: Zero-shot and Few-shot techniques for building functions, refactoring, and debugging.
Working with IDEs: Introduction to the Antigravity editor (or similar AI-based environments).
Working with GitHub Copilot: Managing and assigning tasks within the development environment.
The Lecturer's Perspective: Analyzing the ways students use AI to solve homework assignments.
Redefining Standards: How AI is changing the standard of "proper code" and how to adapt assignments for the new era.
Lab and Practical Application
CLI & Automation Practice: Using Gemini command-line tools to run scripts and automate research tasks.
AI-Driven Project Development: Participants will build a small code project (e.g., processing research data, automating text analysis, or teaching aids).
Using Canvas for Project Management: Interactive work on long code segments, real-time error correction, and algorithm improvement.
Adapting Teaching Materials: A practical session where lecturers take an existing exercise, check how the AI solves it, and rephrase it to challenge students in new ways (e.g., requiring detailed documentation of the prompting process).
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