Search Results for author:
Found 9 papers, 0 papers with code
The Silicone Ceiling: Auditing GPT's Race and Gender Biases in Hiring
In Study 1, we ask GPT to score resumes with 32 different names (4 names for each combination of the 2 gender and 4 racial groups) and two anonymous options across 10 occupations and 3 evaluation tasks (overall rating, willingness to interview, and hireability).
"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings.
Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models
In this paper, we propose a new way to teach programming with Prompt Problems.
Comparing Traditional and LLM-based Search for Image Geolocation
Participants using traditional search more accurately predicted the location of the image compared to those using the LLM-based search.
Decoding Logic Errors: A Comparative Study on Bug Detection by Students and Large Language Models
Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle.
More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems
These capabilities have prompted instructors to rapidly adapt their courses and assessment methods to accommodate changes in learning objectives and the potential for academic integrity violations.
The Robots are Here: Navigating the Generative AI Revolution in Computing Education
Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts.
Comparing Code Explanations Created by Students and Large Language Models
In this paper, we explore the potential of LLMs in generating explanations that can serve as examples to scaffold students' ability to understand and explain code.
Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity
Cross-domain analogical reasoning is a core creative ability that can be challenging for humans.
