Concurrent Session #7: Writing to Learn, Communicate, and Engage: AI-Content Generators, Detectors, and Assumptions About Writing Across the Curriculum
Location
Key West C
Start Date
25-9-2023 12:00 PM
End Date
25-9-2023 12:30 PM
Description
Across higher education, faculty may take varying positions on students’ use of AI writing technologies in their classes, labs, and research projects. Positions may vary significantly depending on the goals of particular assignments, courses, or disciplines. Regardless of their positions, faculty may turn to AI content detectors to determine the extent to which a student has used AI-developed content. In this session, we will review the current state of AI content detectors, identifying the ways that they detect certain linguistic patterns, structural features, and/or repeated words and presenting preliminary summaries of the research about whose writing might be advantaged or disadvantaged by such detectors. We will invite participants to explore similarities and differences of these AI-detection tools to commonly implemented plagiarism detectors and highlight the implications of such detectors for students, faculty, and learning environments, ultimately highlighting our assumptions about what it means to write.
Recommended Citation
Pinkert, Laurie and Santa Rosa, Priscila Schilaro, "Concurrent Session #7: Writing to Learn, Communicate, and Engage: AI-Content Generators, Detectors, and Assumptions About Writing Across the Curriculum" (2023). Teaching and Learning with AI Conference Presentations. 30.
https://stars.library.ucf.edu/teachwithai/2023/monday/30
Concurrent Session #7: Writing to Learn, Communicate, and Engage: AI-Content Generators, Detectors, and Assumptions About Writing Across the Curriculum
Key West C
Across higher education, faculty may take varying positions on students’ use of AI writing technologies in their classes, labs, and research projects. Positions may vary significantly depending on the goals of particular assignments, courses, or disciplines. Regardless of their positions, faculty may turn to AI content detectors to determine the extent to which a student has used AI-developed content. In this session, we will review the current state of AI content detectors, identifying the ways that they detect certain linguistic patterns, structural features, and/or repeated words and presenting preliminary summaries of the research about whose writing might be advantaged or disadvantaged by such detectors. We will invite participants to explore similarities and differences of these AI-detection tools to commonly implemented plagiarism detectors and highlight the implications of such detectors for students, faculty, and learning environments, ultimately highlighting our assumptions about what it means to write.