TY - GEN
T1 - Extended Abstract
T2 - 2024 IEEE International Professional Communication Conference, ProComm 2024
AU - Rakedzon, Tzipora
AU - Tsabari, Ayelet Baram
AU - Segev, Elad
AU - Yosef, Roy
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The integration of generative AI tools in academic writing, particularly for STEM students and researchers, is a growing trend. These tools may save time and enhance grammatical accuracy; however, they can also be used pedagogically to help students improve their own academic and science communication skills. Despite this, studies evaluating the impact of AI tools on students' writing outcomes, specifically in STEM, are limited. This research focuses on whether individualized AI-based feedback helps STEM graduate students improve their writing of short research summaries for a lay audience. To this end, students participated in the task in one of four feedback conditions: a group that receives feedback on appropriate jargon use before each iteration; a group that receives interactive feedback from ChatGPT with rewrite suggestions; a group that receives interactive feedback both about jargon and ChatGPT rewrite suggestions; and a comparison group that does not receive any feedback before each rewrite. Results indicate that AI-based feedback helped students improve their use of jargon and readability, though no significant improvement was observed in message distillation. In this presentation, we share the empirical research and available online task for use in STEM academic and science writing programs.
AB - The integration of generative AI tools in academic writing, particularly for STEM students and researchers, is a growing trend. These tools may save time and enhance grammatical accuracy; however, they can also be used pedagogically to help students improve their own academic and science communication skills. Despite this, studies evaluating the impact of AI tools on students' writing outcomes, specifically in STEM, are limited. This research focuses on whether individualized AI-based feedback helps STEM graduate students improve their writing of short research summaries for a lay audience. To this end, students participated in the task in one of four feedback conditions: a group that receives feedback on appropriate jargon use before each iteration; a group that receives interactive feedback from ChatGPT with rewrite suggestions; a group that receives interactive feedback both about jargon and ChatGPT rewrite suggestions; and a comparison group that does not receive any feedback before each rewrite. Results indicate that AI-based feedback helped students improve their use of jargon and readability, though no significant improvement was observed in message distillation. In this presentation, we share the empirical research and available online task for use in STEM academic and science writing programs.
KW - ChatGPT
KW - STEM academic writing
KW - generative AI
KW - science communication training
UR - http://www.scopus.com/inward/record.url?scp=85201307270&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ProComm61427.2024.00064
DO - https://doi.org/10.1109/ProComm61427.2024.00064
M3 - منشور من مؤتمر
T3 - IEEE International Professional Communication Conference
SP - 294
EP - 295
BT - Proceedings - 2024 IEEE International Professional Communication Conference, ProComm 2024
Y2 - 14 July 2024 through 17 July 2024
ER -