New Course Offering – LBR 211: Critical Data and AI Literacies

The University Libraries is excited to announce the launch of its second credit-bearing course for Fall 2026 – LBR 211: Critical Data and AI Literacies. This course addresses a crucial need in our data-saturated world: the ability to truly understand and interpret data.

LBR 211 is a 3-credit, in-person course held on Tuesdays and Thursdays from 9:30–10:50 AM.

It is designed as a hands-on course that equips students with the practical and critical skills necessary to engage with data –both quantitative and qualitative– responsibly. Students will learn to collect, clean, analyze, and present data while exploring essential, complex questions such as Who collected this data? What might be missing? Whose interests does the data serve?

The curriculum also dives deep into critical topics such as hidden biases in datasets, the ethics of AI, and the real-world impact of AI- and data-driven decision-making. Through various in-class activities and a collaborative research project, students will transform raw data (e.g., from numbers, interviews, surveys, texts) into compelling and responsible insights.

Students are introduced to a range of tools and choose what works best for them, including the option to thoughtfully incorporate –or opt out of– generative AI in their projects. This course is open to all students, regardless of their background or previous experience. No prior coding experience is required!

LBR 211 is a valuable addition to any student’s schedule, fulfilling both the TECH and DIV Stony Brook Curriculum requirements.

Enroll in LBR 211 to gain the knowledge and perspective needed to navigate and shape the data-driven world around you. Start seeing data for what it really is.

Interested in becoming a TA?

We are also seeking motivated upperclassmen (U3 or U4) to serve as Teaching Assistants (TAs) for LBR 211. Ideal candidates will have a strong foundation in data and/or AI literacy, demonstrated through successful completion of LBR 210 or equivalent coursework about data and/or AI.

Interested students should email the course instructor, Ahmad Pratama, for application details.