Taught By

Dr. Nathanial Wiggins

Dr. Nathanial Wiggins is an Adjunct Professor at the University of Texas at Austin, home to one of the nation's top-ranked engineering schools, where Dr. Wiggins mentors engineering students on artificial intelligence research projects. Dr. Wiggins is also Director of Engineering at the University of Oklahoma and Senior Lecturer of Industrial and Systems Engineering at the University of Houston, and holds a Ph.D. in Systems Engineering and Engineering Management from Texas Tech University.

Dr. Wiggins was Principal Investigator for an NSF-funded AI Technology Lab and led the Bridges to STEM Careers Collaborative, mentoring students in AI and technology research. Across these university roles, Dr. Wiggins has guided students from research idea to finished project and technical presentation.

With more than 15 years of university teaching in AI, machine learning, and Python programming, Dr. Wiggins brings college-level research standards to younger students, helping them build the independent research, coding, and presentation skills that stand out to top universities.

Week by Week Curriculum

Week 1: How Machines Think & Previewing Your Research Project

Learn how AI models and robots sense, decide, and act, as taught in university AI courses
Run a real AI model on your own computer and test what it gets right and wrong
Preview the final research project and its two paths: AI behavior or robot decision-making

Week 2: Python Programming & Steering a Simulated Rover

Learn Python from the start: variables, loops, if-statements, and functions
Steer a rover around a grid on your screen by editing a short Python program
Compare two search strategies for collecting objects and count the steps each takes

Week 3: Talking to an AI Model & Developing Your Research Question

Write a program that sends questions to your AI model and saves its answers
Turn the saved answers into your first data table and chart
Choose your path and develop an original research question with a testable hypothesis

Week 4: Research Methods & Running Your Experiment

Learn how researchers run a fair test: change one thing, repeat it, and compare to a baseline
Read and summarize a peer-reviewed AI or robotics research article
Run your experiment with live feedback and Q&A from your UT Austin mentor

Week 5: Analyzing Results & Writing Your Research Paper

Analyze your data with averages and clear charts, and decide what the evidence supports
Draft your research paper with mentor guidance during project work time
Finalize a polished research presentation slide deck

Week 6: Final Research Presentations & Next Steps

Present your independent research project to your mentor and peers
Archive your paper on Zenodo, an open research repository, with a permanent DOI you can cite
Plan next steps, such as science fairs and AI competitions, in a final Q&A with your mentor

AI and Robotics Research with UT Austin Professor

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