Diagnosis Unreliability of ChatGPT for Journal Evaluation

Autor:	Mehdi Dadkhah, Marilyn H Oermann, Mihály Hegedüs, Raghu Raman, Lóránt Dénes Dávid
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	hijacked journals predatory journals chatgpt artificial intelligence language models research ethics Therapeutics. Pharmacology RM1-950
Zdroj:	Advanced Pharmaceutical Bulletin, Vol 14, Iss 1, Pp 1-4 (2024)
Druh dokumentu:	article
ISSN:	2228-5881 2251-7308
DOI:	10.34172/apb.2024.020
Popis:	Purpose: Academic and other researchers have limited tools with which to address the current proliferation of predatory and hijacked journals. These journals can have negative effects on science, research funding, and the dissemination of information. As most predatory and hijacked journals are not error free, this study used ChatGPT, an artificial intelligence (AI) technology tool, to conduct an evaluation of journal quality. Methods: Predatory and hijacked journals were analyzed for reliability using ChatGPT, and the reliability of result have been discussed. Results: It shows that ChatGPT is an unreliable tool for journal quality evaluation for both hijacked and predatory journals. Conclusion: To show how to address this gap, an early trial version of Journal Checker Chatbot has been developed and is discussed as an alternative chatbot that can assist researchers in detecting hijacked journals.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/cd3628c775af4a178a63d7c7a0c7e9e2 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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