Pooriya Jamie

Hello, I’m Pooriya Jamie, a first-year PhD student in Communication at the University of California, Los Angeles (UCLA), advised by Prof. Homa Hosseinmardi. My research examines how algorithmic curation shapes social media experiences and influences user well-being, at the intersection of human-computer interaction, data science, and socio-technical systems.

Previously, I explored AI’s transformative potential in education, including developing AI-assisted tools to analyze student learning behaviors, studying the effects of large language models on problem-solving, and designing adaptive systems for personalized learning. These experiences shaped my broader interest in leveraging computational and human-centered methods to understand and improve experiences in digital environments.

I am particularly interested in projects at the intersection of AI, communication, and human-centered technology, and I welcome collaborations that investigate the social and behavioral impacts of digital systems.

news

Feb 23, 2025	Excited to share that my paper, “Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant’s Perspective,” has been accepted at CHI 2025 LBW!
Oct 11, 2024	I’m thrilled to announce the release of the pre-print of my latest research paper: “Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant’s Perspective.” Read it here
Jul 14, 2024	Excited to announce my summer internship at the University of Twente, working on an AI-assisted assignment assessment system with Dr. Mohammadreza Farrokhnia.
Apr 02, 2024	Thrilled to announce the publication of our latest paper in the SSRN Journal! Read it here 📝

selected publications

Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant’s Perspective

Pooriya Jamie, Reyhaneh HajiHashemi , and Sharareh Alipour

In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems , 2025

Abs HTML

Integrating large language models (LLMs) like ChatGPT into computer science education offers transformative potential for complex courses such as data structures and algorithms (DSA). This study examines ChatGPT as a supplementary tool for teaching assistants (TAs), guided by structured prompts and human oversight, to enhance instruction and student outcomes. A controlled experiment compared traditional TA-led instruction with a hybrid approach where TAs used ChatGPT-4o and ChatGPT o1 to generate exercises, clarify concepts, and provide feedback. Structured prompts emphasized problem decomposition, real-world context, and code examples, enabling tailored support while mitigating over-reliance on AI. Results demonstrated the hybrid approach’s efficacy, with students in the ChatGPT-assisted group scoring 16.50 points higher on average and excelling in advanced topics. However, ChatGPT’s limitations necessitated TA verification. This framework highlights the dual role of LLMs: augmenting TA efficiency while ensuring accuracy through human oversight, offering a scalable solution for human-AI collaboration in education.