Collection

ASEE Zone 1 Conference - Spring 2023

Authors

Devang Jayachandran, Pennsylvania State University, Harrisburg; Pranit Shrikrishna Maldikar, Pennsylvania State University; Jeremy Joseph Blum, Pennsylvania State University, Harrisburg

generative artificialintelligence systems more widely available. One such system is ChatGPT, created by OpenAI,which has the ability to handle a variety of tasks, including answering complex questions,analyzing data, and engaging in contextual conversation. This paper assesses the impact ofChatGPT on programming assignments by examining its strengths and limitations in solvingchallenging problems presented in the IEEEXtreme programming contest problem set, whichincludes problems that were not included in ChatGPT's training data. ChatGPT's performancevaries depending on the type of problem, performing well in identifying underlying concepts andgenerating basic solutions for easier to medium-level problems, but facing difficulties with edgecases and

Collection

ASEE Zone 1 Conference - Spring 2023

Authors

Bradley J. Sottile, The Pennsylvania State University

Tagged Topics

Diversity

theme of the third proposed question group is intended to examine student, faculty,and stakeholder views on ChatGPT and artificial intelligence text or image generators. ChatGPThas caused disruption already for educational practice. Faculty across the country haveconsidered the question of how they might restructure their courses to reduce ChatGPT’s impacton educational quality (Huang, 2023). Recently, Kung et al. (2022) examined the use ofChatGPT to take medical licensing exams – and ChatGPT did surprisingly well on the exams.Whether ChatGPT can be considered a paper author for scientific work has even become adebatable proposition (Stokel-Walker, 2023). If one carefully considers the reference list for theinstant paper, they will discover

Collection

ASEE Zone 1 Conference - Spring 2023

Authors

Buket D Barkana, University of Bridgeport; Ioana A. Badara, University of Bridgeport; Navarun Gupta, University of Bridgeport; Junling Hu, University of Bridgeport; Ausit Mahmood, University of Bridgeport

Tagged Topics

Diversity

creating a laboratory course wherestudents learn the applications of AI and get to play and experiment with concepts that they can right away see beingapplied through concepts of simple Calculus and Python programming.Deep Convolution based networks with the Triplet loss were quite successful (e.g., FaceNet) in face recognitionresulting in greater than 99% accuracy on benchmarks such as LFW. With the recent success of Transformer basedNatural Language Processing architectures (e.g., ChatGPT), transformers have been attempted in Computer Visionapplications. They have shown considerable success with better computational efficiency than CNN-basedarchitectures. In this project, we compare the FaceNet and transformer-based architecture for face