Dear UMBC Undergrad Students, 

We're conducting a research study titled "Measuring College Students' Reliance on Generative AI in Academic Writing: Impacts and Insights from a Mixed-Methods Study." This study aims to investigate how undergraduate students utilize generative AI tools, such as ChatGPT, and the impact these tools have on their writing. Our goal is to understand how these advanced technologies are transforming writing practices and behavior.

To participate, complete the "Reliance on Generative AI" survey via Qualtrics, which should take no more than 10 minutes. All information you provide will be kept anonymous and confidential. Participation is completely voluntary, and you can withdraw at any time without any negative consequences. As a token of appreciation, ten lucky participants will each receive $100, selected through a random lottery among all survey completers. Additionally, participants may choose to take part in a brief follow-up online interview (<1 hour) for an extra $25 cash incentive.

Eligibility for this study requires you to be a current UMBC student who has used AI technologies for academic purposes. If you have any questions, need more information, or have concerns about this study, please feel free to contact Shahin Hossain at shahinh1@umbc.edu or call (443) 478-6437. You can also contact Dr. Ramon Goings at rgoings@umbc.edu.

For questions about the review process or your rights as a research participant, you can reach out to the UMBC Office of Research Protections and Compliance at (410) 455-2737 or via email at compliance@umbc.edu.

Your participation is highly valued, as your insights will greatly enhance our understanding of the role of AI in literacy development within higher education. To join the survey, please follow this link: https://umbc.co1.qualtrics.com/jfe/form/SV_6KBpfotc3SrbwMe

Kindest regards,

Shahin Hossain

Ph.D. Candidate, Language, Literacy, and Culture

Dr. Reetam Majumder from the University of Arkansas will lead a three-hour short course on AI for statistical analysis from 2:00 to 5:00 pm on Friday, October 31, 2025, in room 104 of UMBC's Math & Psychology building. The course will provide a hands-on introduction to deep learning models using the Keras package in R.

Register here by October 24.

Deep learning is the cornerstone of all current AI models. While they have historically been used extensively in supervised and unsupervised learning problems, deep learning is also seeing increased adoption for statistical inference associated with complex/intractable models, massive datasets, or both. The short course will provide a hands-on introduction to deep learning models using the keras package in R.

The course aims to familiarize statisticians with the entire pipeline for analyzing data, including software setup, data preprocessing, and some common neural network architectures used in predictive modeling for different data types like tables, images, and time series. Participants will also be introduced to deep learning approaches for density estimation and parameter estimation in statistical problems, and an overview of the current state-of-the-science. 

The hands-on sections will be demonstrated on Google Colab, and participants will be able to follow along on their own personal devices. Course materials, including code, data, slides, and resources for installing the Keras and Torch deep learning libraries on R, will be made available on GitHub.