Data+AI
Professional Development

Are you interested in teaching an introductory AI course in your department?
Join us for a hands-on, practical exploration of using AI tools with students in your classroom.
This page is available at go.uic.edu/dataaipd

The workshop is full. For historical reasons here is the Application link

Links: Slides from PD, Data+AI Course, Datasets, Google Photo Album

We’d like to invite you to a free 3-day Data+AI Professional Development workshop designed for all UIC faculty, regardless of tech background. We will cover teaching techniques and tools modeled after the success of the CS 218 / FIN 294 Data+AI course, taught Fall ‘25 and Spring ‘26.

You will leave with practical techniques you can use immediately in any class or in professional projects—whether you choose to adopt a few ideas or the entire course.

What: Data+AI Professional Development (PD)

When: 3 days, May 13-15 (Wed-Th-Fri), 9am-3pm
(The week after UIC finals, before the 4-week first summer session.)

Where: UIC Campus, CDRLC 1422

Logistics

Location and Parking

Our building is called the "Computer Design and Research Learning Center" (CDRLC), and is at 850 W. Taylor St.

The entrance to the closest paid parking ($15/day) is a half a block south, at 1145 S. Morgan St. We are also accessible via public transit. If you don't mind walking ~5 minutes, see this map of free parking spots in the area.

Our meeting room in the CDRLC building is room 1422, just off the main atrium, on the west side of the first floor.

Hours:

We'll start each day promptly at 9am, and end at 3pm. Doors open at 8:15am if you want to come early.

Food:

There will be coffee, snacks, and an assortment of boxed lunches. We'll end each day at 3pm.

Get Ready:

To make our time together as profitable as possible, please do the following ahead of time:

Get a ChatGPT+ subscription, which is $20/month (as of May '26). We acknowledge there are many GenAI options, but will all be using ChatGPT to make it easy to share our work with each other.
Subscribe to a two-week free trial to n8n, the agentic AI system we’ll be using. See the left-hand "Starter" column, and select the blue "Start free trial" button.
Create a google site with a single page, where the site should have a name that includes your first and last names, as shown in the following YouTube video.
Fill out this brief form, supplying your preferred name for your name tag, indications whether you were able to complete the above steps, a link to some AI-related article you find interesting, and 3-5 words that spring to mind when you think about AI in education.

ChatGPT+

n8n Trial

Create Google Site

Details for PD Participants:

Prerequisites: Curiosity. There are no technical prerequisites besides comfort with general computer use.
This is being offered by the College of Engineering’s CS Department, facilitated by current Data+AI instructors and experienced professional development facilitators Dale Reed and Baker Franke.
88% of students in the existing course say they would recommend or highly recommend it to their friends. Students from a wide range of majors have been successful in the course.
A variety of box lunches will be provided, though we are not able to accommodate special dietary restrictions.

What you will Experience and Learn:

Effective prompting techniques for Large Language Models (LLMs), including making custom GPTs
Code generation for technical tasks using Google Colab
Machine learning using Decision Trees
An introduction to Agentic AI using an industry-standard tool n8n.io
Data analysis and problem solving applied to real-world tasks
Teaching and hands-on learning using peer instruction and inquiry
AI ethical issues

This course is:

NOT a technical course on how AI systems are built. We will not study the mathematics or engineering behind LLMs or neural networks.
NOT a deep dive into machine learning theory or algorithms. The emphasis is on using existing tools, not developing new models.
NOT a programming-heavy course. No technical background is required; participants learn how to use LLMs to generate code to effectively leverage modern AI tools.