Generative AI for High School Innovators

I’m pleased to announce the Center for Future Work Places and Practices (CFWPP) will be running a new two-week course this summer for high school students, entitled “From Data to Generative AI: A Journey through Machine Learning.”

This course is designed for students in Grades 9–12. No prior experience in coding or artificial intelligence is required—just bring your curiosity and enthusiasm.

Win a Scholarship! Essay Contest for VA Students

Virginia high school students in grades 9-11 are invited to participate in an essay contest exploring AI’s future!

Theme: How AI is shaping society, education, work, or ethics
Length: 800-1,000 words
Prize: Five (5) winners will receive a full scholarship to this summer school!
Deadline: June 20, 2025
Award Notification: June 30, 2025
Submit: Essay + CV to vtcfwpp@gmail.com

What Student Will Learn

Understand how generative AI works – from neural networks to transformers like GPT and DALL·E.
Create content with AI tools – use platforms like ChatGPT, Midjourney, and Llama to write stories, design graphics, and generate music.
Analyze ethical and social implications – explore bias, misinformation, creativity, authorship, and responsible AI use.
Develop your own AI mini-project – pitch and present a creative application of generative AI to solve a real-world problem or express a unique idea.
Build foundational knowledge in machine learning concepts like training data, model outputs, and prompt engineering.

Agenda/Schedule (July, 2025)

Week 1: Foundations of Machine Learning and AI
Session 1: Introduction to AI and Machine Learning
Session 2: How Machines Learn
Session 3: Data in Machine Learning
Session 4: Machine Learning with AWS
Session 5: Building Our First ML Model

Week 2: Generative AI and more
Session 6: Introduction to Generative AI
Session 7: Neural Networks and Deep Learning
Session 8: Natural Language Processing (NLP)
Session 9: Ethics and Societal Impact of AI
Session 10: Capstone Project and Showcase

Prerequisites: Curious, open to learning, comfortable with basic use of computers.

Tuition Fees: In-State: $400, Out-of-State: $1000

Center for Future Work Places and Practices – Networking Event

On May 9, from 2-4pm, the Center for Future Work Places and Practices (CFWPP) will hold its annual friends and affiliates networking meeting at the Virginia Tech Inn. The meeting will provide attendees with an opportunity to meet and interact with other affiliates and friends of the center. During the event, we will have a brief presentation about the center activities over the last year, followed by several short presentations about ongoing research related to the future of work. There will also be plenty of time for coffee/free discussion. Please register here.

Symposium on the Human-Centered Future of Work

Registration for the VT Center for Future Work Places and Practices (CFWPP) Symposium on the Human-Centered Future of Work is now open.

When: 8:30am-4:30pm, Friday, Nov 3, 2023

Where: North End Center 2420, Virginia Tech 300 Turner St. NW, Blacksburg. A Zoom connection is available for those unable to attend in person.

Performing Alternative Economic Models

On Wednesday, October 25, from 9-11am, Steven T. Licardi and I will be hosting an event as part of the Virginia Tech Humanities Week. Please join us if you would like to engage in our “Performing Alternative Economic Models” experience.

Performing Alternative Economic Models

Ralph Hall and Steven T. Licardi

Time: 9:00 – 11:00 a.m.
Location: Squires 134 and Zoom

For many, our current economic models no longer meet our basic human needs. Over the past two years, performance artist Steven T. Licardi and SPIA faculty member Ralph Hall have been exploring ways to advance new economic thinking – related to Community Wealth Building – through a SciArts project. Through this collaboration, they arrived at “Ping-pong-omics” and performance art as an engaging way to reveal economic inequality and challenge participants to imagine, explore, and develop alternative economic models.