| Title: |
The Impact of Falsely Detecting AI-Generated Text on Academic Assessment |
| Language: |
English |
| Authors: |
Arifi N. Wak (ORCID 0000-0003-1222-0978); Hanadi Abdelsalem (ORCID 0000-0003-2591-4661); Muhammad Waqar Ashraf (ORCID 0000-0002-7087-9714); Maura Pilotti (ORCID 0000-0001-7955-680X); Khadija El Alaoui (ORCID 0000-0002-6852-8800) |
| Source: |
International Society for Technology, Education, and Science. 2024. |
| Availability: |
International Society for Technology, Education, and Science. 944 Maysey Drive, San Antonio, TX 78227. Tel: 515-294-1075; Fax: 515-294-1003; email: istesoffice@gmail.com; Web site: http://www.istes.org |
| Peer Reviewed: |
Y |
| Page Count: |
14 |
| Publication Date: |
2024 |
| Document Type: |
Speeches/Meeting Papers; Reports - Research |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Artificial Intelligence; Writing Tests; Plagiarism; Accuracy; English (Second Language); Second Language Learning; College Freshmen; Introductory Courses; Computer Uses in Education; Scores; High Achievement; Foreign Countries |
| Geographic Terms: |
Saudi Arabia |
| Abstract: |
Large Language Model (LLM) Functionalities, such as Chat Generative Pre-Trained Transformer (ChatGPT), are a form of artificial intelligence (AI) that have the ability to produce human-like writing in response to a wide array of input. As this technology has become a staple of everyday life, many educators find themselves rapidly redefining best assessment practices regarding this new area of potential plagiarism. As it becomes increasingly difficult to discern between well-written assignments produced by students and those that have been generated by AI, educators may turn to AI-detection programs to determine whether work submitted by students is in fact their own. However, these programs notoriously return a high rate of false detection of AI-generated text. This phenomenon leads to the increased likelihood that students will be unfairly academically penalized. In this study, hand-written examinations for a university-level introductory academic writing class were submitted to different AI-detection programs. Results indicate an association between higher exam grades and greater false detection of AI-generated writing. These results suggest that educators must carefully consider the benefits of such programs when assessing students' work. [For the complete proceedings, see ED672804.] |
| Abstractor: |
As Provided |
| Entry Date: |
2025 |
| Accession Number: |
ED673127 |
| Database: |
ERIC |