When
Where
Inspired by the conversations and connections sparked at the UCATT Teaching Exchange Symposium, this virtual workshop series invites participants to revisit popular sessions, explore new perspectives, and continue exchanging ideas throughout the year.
This workshop shares a practical, scaffolded approach for integrating generative AI into a Statistics in Psychology course to support student engagement with empirical literature, public datasets, and applied statistical reasoning. The how-to centers on designing a multi-stage research assignment in which students transparently use AI tools (e.g., ChatGPT) to generate keywords, locate quantitative empirical articles through a university library database, identify publicly available datasets related to human behavior, cognition, or emotion, and plan and conduct statistical analyses.
Each stage includes structured check-ins, explicit AI-use disclosures, and targeted rubrics aligned with APA-style empirical writing. The why guiding this work asks: How can instructors leverage AI to reduce cognitive barriers and increase access to authentic research experiences while preserving academic integrity and statistical rigor? Rather than treating AI as a shortcut or threat, this project reframes AI as a learning scaffold that supports information literacy, synthesis, and methodological understanding—skills that are often challenging for students in introductory statistics courses.
Speakers
Tierra Stinson
Associate Professor of Practice
Department of Psychology