Collection

2024 ASEE North East Section

Authors

Lina Kloub, University of Connecticut; Aayush Gupta, University of Connecticut

with students of diverse backgrounds and learning styles, ensuring that everyone feels valued and heard in her courses. Her commitment to excellence is evident through her active participation in teaching workshops at the Center for Excellence in Teaching and Learning (CETL) at UConn. Lina’s work in academia reflects her dedication to enhancing education and fostering a sense of belonging among students. Her contributions in both teaching and research continue to make a lasting impact in her field.Aayush Gupta, University of Connecticut ©American Society for Engineering Education, 2024 ChatGPT in Computer Science Education: Exploring Benefits, Challenges, and Ethical

Collection

2024 ASEE North East Section

Authors

Gonca Altuger-Genc, State University of New York, Farmingdale State College; Akin Tatoglu, University of Hartford

time. Whether the changes are technological advancements, improvements in applicationmethodologies or theoretical developments, engineering faculty, and engineering programs andcurriculums update their teaching materials, course offerings as well as content delivery methodsto incorporate such changes so they can teach their students the most up-to-date approaches. Oneof the most recent developments, now slowly becoming a permanent component of daily life, isAI (Artificial Intelligence) and related tools. This study aims to provide an overview of theintegration process of an AI-based chatbot such as ChatGPT into engineering education throughin-class exercises and homework assignments to enhance self-directed learning and tacklechallenges

Collection

2024 ASEE North East Section

Authors

Abdullah Aldwean, University of Bridgeport ; Dan Tenney, University of Bridgeport

Tagged Topics

Diversity

mechanism called attention [18].Despite the LLM technical characteristics and sophisticated capabilities, the introduction ofconversational-based models such as ChatGPT has established a new paradigm in human-AIinteraction. The wide adoption and the fast diffusion of these models reflect a deep interest in AIbased products by users. According to the OpenAI company, the ChatGPT has 100 millionweekly active users [15].Efficient evaluation is a crucial step in ensuring the applicability of LLMs in the healthcaresector. While automated evaluation methods are more cost and time-efficient, human evaluationremains the golden standard for determining the safety and usefulness of LLM in healthcare.Experts-based evaluation is critical in a sensitive domain

Collection

2024 ASEE North East Section

Authors

Christina Cao, .; Danushka Bandara, Fairfield University

., Seyyed-Kalantari, L., Khattak, F. K. (2023). Soft-prompt Tuning for Large Language Models to Evaluate Bias. ArXiv. https://arxiv.org/abs/2306.0473524. Kirk, H. R., Jun, Y., Iqbal, H., Benussi, E., Volpin, F., Dreyer, F. A., ... Asano, Y. M. (2021). Bias Out-of-the-Box: An Empirical Analysis of Intersectional Oc- cupational Biases in Popular Generative Language Models. Neural Information Processing Systems.25. Ferrara, E. (2023). Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models. ArXiv. https://arxiv.org/abs/2304.0373826. Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... Krueger, G. (2019). Release strategies and the social impacts of language models. arXiv