STOP USING AI DETECTORS!!!

AI detectors like ChatGPT and Turnitin are ineffective in catching students using generative AI. A classroom experiment conducted by a teacher showed that 12-year-olds were able to beat these detectors easily. The experiment also revealed that the detectors falsely accused students and targeted non-native English speakers. Educators are seeking innovative solutions to address this problem.

Rethinking Plagiarism in the Era of Generative AI

The purpose of this paper is to explore the impact of generative artificial intelligence (AI), particularly large language models (LLMs), on academic writing, plagiarism, and intellectual property. The paper aims to analyze the evolving landscape of English composition courses in light of increasing integration of AI in academia, and to examine the implications of AI-enabled

GENAI DETECTION TOOLS, ADVERSARIAL TECHNIQUES AND IMPLICATIONS FOR INCLUSIVITY IN HIGHER EDUCATION

This paper “GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education” reveals that GenAI detection tools have significant limitations and are not reliable in detecting machine-generated content. The low accuracy rates and the potential for false accusations raise concerns about fairness, inclusivity, and the negative impact on certain groups such as non-native English

Up in the Air-Educators Juggling the Potential of Generative AI with Detection, Discipline, and Distrust (Center for Democracy and Technology)

The report is important for educators because it sheds light on the challenges and risks associated with the use of generative AI in the classroom. It highlights the need for clear policies, guidance, and training to ensure responsible and safe use of generative AI by both teachers and students. Educators can use this information to

AI can do your homework. Now what?

The recent video from Joss Fong appears to be a great catalyst for initiating discussions. If you have not watched it yet, I suggest that you do it now! Key insights:

Testing of Detection Tools for AI‑Generated Text

The article discusses the potential risks associated with the unfair use of AI-generated content in an academic environment and the efforts to detect such content. The authors examine the functionality of various detection tools for AI-generated text and evaluate their accuracy and error types. The study aims to determine if existing detection tools can effectively