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AI or Human? Finding and Responding to Artificial Intelligence in Student Work

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Title: AI or Human? Finding and Responding to Artificial Intelligence in Student Work
Language: English
Authors: Gary D. Fisk (ORCID 0000-0001-6203-0713)
Source: Teaching of Psychology. 2025 52(3):314-318.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 5
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Descriptors: Technology Uses in Education, Writing (Composition), Plagiarism, Identification, Computer Software, Accuracy, Artificial Intelligence, Man Machine Systems, Natural Language Processing, Integrity
DOI: 10.1177/00986283241251855
ISSN: 0098-6283
1532-8023
Abstract: Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing between AI-generated vs. human-authored text. This weakness raises a significant possibility of false positive errors: human-authored writing incorrectly judged as AI-generated. Literature review: AI detection methodology, whether machine or human-based, is based on writing style characteristics. Empirical evidence demonstrates that AI detection technologies are more sensitive to AI-generated text than human judges, yet a positive finding from these technologies cannot provide absolute certainty of AI plagiarism. Teaching implications: Given the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions. Conclusion: Faculty should cautiously embrace the use of AI detection technologies with the understanding that false positive errors will occasionally occur. This use is ethical provided that the responses to problematic cases are approached with the goal of educational growth rather than punishment.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1474461
Database: ERIC
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  Data: Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing between AI-generated vs. human-authored text. This weakness raises a significant possibility of false positive errors: human-authored writing incorrectly judged as AI-generated. Literature review: AI detection methodology, whether machine or human-based, is based on writing style characteristics. Empirical evidence demonstrates that AI detection technologies are more sensitive to AI-generated text than human judges, yet a positive finding from these technologies cannot provide absolute certainty of AI plagiarism. Teaching implications: Given the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions. Conclusion: Faculty should cautiously embrace the use of AI detection technologies with the understanding that false positive errors will occasionally occur. This use is ethical provided that the responses to problematic cases are approached with the goal of educational growth rather than punishment.
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