| website | https://levon003.github.io/ |
| bluesky | https://bsky.app/profile/zwlevonian.bsky.social |
| github | https://github.com/levon003 |
INCREDIBLY excited to announce this: I collaborated with the Internet Archive to make a new 24/7 lofi livestream!
By day, you can see scanners scanning and what they're scanning, and when they're not scanning, you will see public domain silent movies and pictures. Sorta like an Internet Archive TV
Watch it LIVE here: https://www.youtube.com/watch?v=aPg2V5RVh7U
Large language models (LLMs) are flexible, personalizable, and available, which makes their use within Intelligent Tutoring Systems (ITSs) appealing. However, their flexibility creates risks: inaccuracies, harmful content, and non-curricular material. Ethically deploying LLM-backed ITSs requires designing safeguards that ensure positive experiences for students. We describe the design of a conversational system integrated into an ITS that uses safety guardrails and retrieval-augmented generation to support middle-grade math learning. We evaluated this system using red-teaming, offline analyses, an in-classroom usability test, and a field deployment. We present empirical data from more than 8,000 student conversations designed to encourage a growth mindset, finding that the GPT-3.5 LLM rarely generates inappropriate messages and that retrieval-augmented generation improves response quality. The student interaction behaviors we observe provide implications for designers---to focus on student inputs as a content moderation problem---and implications for researchers---to focus on subtle forms of bad content and creating metrics and evaluation processes.Code and data are available at https://www.github.com/DigitalHarborFoundation/chatbot-safety and https://www.github.com/DigitalHarborFoundation/rag-for-math-qa.
Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers - a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.
Sophia 
