Student AI Liaisons

SAIL: Nicolas Gertler, Saybrook College ‘27, Cognitive Science

Main Role: Supporting AI integration through development of the Italian 110 Bot to assist with language learning and classroom activities.

Most Exciting Activity: Prototyping new ways to design and evaluate student performance with gen. AI, especially exploring conversational scaffolding and adaptive feedback.

SAIL: Nicolas Gertler, Saybrook College ‘27, Cognitive Science

Main Role: Assisting with final assessment redesign.

Most Exciting Activity: Helping reframe evaluation methods to reward creativity and reflective tool use rather than simple output.

SAIL: Nicolas Gertler, Saybrook College ‘27, Cognitive Science

Main Role: Collaborating on a comprehensive course redesign, from readings to assessments, focused on responsible and critical engagement with AI systems.

Most Exciting Activity: Working on how to structure core content modules that balance technical understanding with ethical reasoning, ensuring students can interrogate both how AI works and how it affects society.

SAIL: Deja Dunlap, Pauli Murray ‘25, Applied Mathematics

For this course, I have been working with Fatima to re-imagine the “Race, Gender, and AI” course that she taught a few years ago. With the advent and increasing popularity of AI in recent years, she wanted to incorporate more of the technical aspects of AI into her coursework. I’ve been helping her identify key literature on the development of AI, starting with the Three Principles of AI from Isaac Asimov to the transformers that help make LLMs as effective as they are today. I’m also going to develop a workshop on my research, on racial linguistic bias in AI, to lead the course for one lecture (yay!!). Outside of that, she’s also been having me join in on some of her lab’s research meeting, where I’m working on helping them develop a more intuitive interface for their archival work.

SAIL: Avi Kabra, Pierson College ‘27, Applied Mathematics

Main Role: My work in this course focuses on redesigning the coding lab curriculum to embrace artificial intelligence rather than restrict it. I’ve been designing a sequence of labs in R and Stata that progressively build data literacy and computational reasoning, while integrating LLM-assisted learning for debugging, statistical explanation, and reproducible research.

Most Exciting Activity: The most exciting part has been running the LLM-guided coding sessions, where students learn prompt engineering rather than relying solely on prescriptivist syntax. This approach not only teaches programming and statistical logic from a more holistic perspective, but also models how AI can function as a genuine collaborator in scientific reasoning. 

SAIL: Helen Zhang, Pauli Murray ’28, Applied Math and Comparative Literature 

Main Role: My main role is to take notes during the seminar discussions, then give feedback and my own student perspective on the AI related activities during my weekly one-on-one meetings with Professor Shoemaker. I told Professor Shoemaker about the report and he suggested writing it near the end of the semester, when the students have finished the in-class AI exercises. I have finalized a feedback form with Professor Shoemaker to inform the report, letting the students anonymously reflect on the AI exercises and how their perception of / interaction with AI evolved throughout the course.

Most Exciting Activity: My most exciting activity so far has been helping Professor Shoemaker formulate the rough guidelines of the final paper: I conducted my own AI assignment (co-writing a poem with AI about AI), reflected on it, and conceptualized my own AI philosophy. I am looking forward to discussing further with Professor Shoemaker about it this week!