TY - GEN
T1 - Enhancing Collaborative Academic Writing with Generative Artificial Intelligence
T2 - Poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wideAIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025
AU - Korchak, Anna
AU - Fanguy, Mik
AU - Adamovich, Kseniia
AU - Zhang, Han
AU - Baldwin, Mattew
AU - Costley, Jamie
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The increasing use of Generative Artificial Intelligence (Gen-AI) in higher education offers a lot of opportunities to enhance collaborative writing. However, educators face challenges in understanding how students use these tools in group settings, which complicates their effective integration into the classroom. Moreover, little is known about the effects of specific Gen-AI applications. To fill this gap, the present study aims to identify particular Gen-AI applications in group writing tasks and examine how they relate to students’ group performance. The study focuses on analyzing how students use Gen-AI in group work, particularly when completing writing tasks. The sample includes 143 Korean students enrolled in an academic writing course, who were asked to collaboratively write a section of an academic paper. Based on students’ written self-reflections and identified Gen-AI applications in their texts, three categories of Gen-AI use were elicited: conceptual, language, and integrated. Conceptual applications involved tasks such as brainstorming, summarizing, paraphrasing, and other content-related activities. Language applications focused on grammar correction, vocabulary enhancement, and similar linguistic tasks. Integrated applications combined both conceptual and language uses. Conceptual and integrated applications were found to positively impact group performance, while language applications did not. These findings contribute to the broader discourse on Gen-AI use in education, with a particular focus on group-based applications and their effects.
AB - The increasing use of Generative Artificial Intelligence (Gen-AI) in higher education offers a lot of opportunities to enhance collaborative writing. However, educators face challenges in understanding how students use these tools in group settings, which complicates their effective integration into the classroom. Moreover, little is known about the effects of specific Gen-AI applications. To fill this gap, the present study aims to identify particular Gen-AI applications in group writing tasks and examine how they relate to students’ group performance. The study focuses on analyzing how students use Gen-AI in group work, particularly when completing writing tasks. The sample includes 143 Korean students enrolled in an academic writing course, who were asked to collaboratively write a section of an academic paper. Based on students’ written self-reflections and identified Gen-AI applications in their texts, three categories of Gen-AI use were elicited: conceptual, language, and integrated. Conceptual applications involved tasks such as brainstorming, summarizing, paraphrasing, and other content-related activities. Language applications focused on grammar correction, vocabulary enhancement, and similar linguistic tasks. Integrated applications combined both conceptual and language uses. Conceptual and integrated applications were found to positively impact group performance, while language applications did not. These findings contribute to the broader discourse on Gen-AI use in education, with a particular focus on group-based applications and their effects.
KW - Academic Writing
KW - Artificial Intelligence
KW - Collaborative Writing
KW - Generative Artificial Intelligence
KW - Student Performance
UR - https://www.scopus.com/pages/publications/105013020036
UR - https://www.scopus.com/pages/publications/105013020036#tab=citedBy
U2 - 10.1007/978-3-031-99264-3_37
DO - 10.1007/978-3-031-99264-3_37
M3 - Conference contribution
AN - SCOPUS:105013020036
SN - 9783031992636
T3 - Communications in Computer and Information Science
SP - 297
EP - 304
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED - 26th International Conference, AIED 2025, Proceedings
A2 - Cristea, Alexandra I.
A2 - Walker, Erin
A2 - Lu, Yu
A2 - Santos, Olga C.
A2 - Isotani, Seiji
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 July 2025 through 26 July 2025
ER -