This project addresses the urgent need for cyber safety education for teenagers. Specifically, this project creates conversational agents, “Social Media Co-Pilot,” for a social media simulation platform called Social Media TestDrive. These Co-Pilots will help teenagers understand cyberbullying situations, support victims in a considerate manner, and assist in composing positive messages to bullies. To achieve this, this project will create accessible tools for teachers to create custom chatbots for their educational needs by combining the fluency and adaptivity of pre-trained Large Language Models (LLMs) and the controllability of dialogue trees.

This project is supported by the NSF  (#2302977). Michael Hedderich (NLP) and Chao Zhang (HCI) are student leads.

Generative Artificial Intelligence (genAI) models such as chatGPT have started reshaping knowledge work, turbocharging many intellectual tasks while automating and eliminating others. How can we prepare students across disciplines for such a future? This project will develop the first Experiential Learning module that enables students across majors to examine and harness genAI capabilities for their respective disciplines. Essential to this module is GenAI Explorer, a new GPT-based software that enables students to experiment systematically with genAI capabilities without writing code. Teachers from different domains (e.g., journalism, law, writing) can adapt the software to their respective domains and teaching needs.

This project is supported by Schmidt Futures and Cornell Center for Teaching Innovation. Talia Wise (HCI) and Khonzoda Umarova (NLP) are student leads. Example publications include CoAuthor (CHI'22.)

This project helps lay people with varying levels of medical literacy to comprehend medical texts that they hope to read, for example, a patient reading discharge notes from their doctors, a caregiver reading a clinical trial report to understand better a sick family member’s prognostics, a biomedical engineer reading medical literature for their own engineering needs, and even a physician reading another medical specialty's latest discoveries. To achieve this goal, this project creates a medical reading tool that interactively simplifies and elaborates the parts of texts that users struggle to understand. Using purpose-built Augmented Language Models, this tool can adapt the explanations to the user’s medical literacy and reading needs and support medical texts across disease areas.

This project is supported by Schmidt Futures and Cornell's Digital and AI Literacy Initiative. Michael Hedderich (NLP) and Chandrayee Basu (ML) are student leads. Example publications include Med-EASi (AAAI'23) and explaining AI predictions with medical literature (CHI'23).