I appreciate your thoughtful question. This issue's implications include the risk of unjust penalties for students who utilize AI tools for legitimate refinement purposes. Solutions may involve developing more sophisticated detection algorithms that accurately distinguish between AI-generated content and human-written work enhanced by AI, ensuring fair and reliable evaluations.

Enoch this is a great discussion, and very topical at the moment.

In my academic interactions with students, I frequently encounter this issue. Specifically, the challenge arises when content is pasted into a GPT-based tool. In most cases, the tool appears to incorporate this content into its internal database or inventory.

To investigate this phenomenon, I conducted several tests throughout this year. For instance, I input previously AI-checked text into the same tool and inquired whether it had generated the text. Remarkably, the system affirmed that it had authored the text approximately 87% of the time, even in instances where this claim was demonstrably incorrect. This suggests that the tool does not reliably evaluate authorship but instead references its database. When it identifies identical sentence structures, content, or stylistic patterns, it attributes the work to itself without discerning actual authorship.

With AI tools becoming increasingly sophisticated, it is difficult to distinguish between human and AI generated material, unless context is presented throughout the content.

Detecting Research Misconduct in the Age of Artificial Intelligence

"The rise of fraudulent papers, exacerbated by AI-assisted technologies, threatens scientific integrity, but new detection tools offer hope...

To counteract the rising number of fraudulent papers and depollute the literature, scientists are developing tools that detect these “fingerprints,” with emerging detection tools pitting AI against AI...

It’s important to be able to identify AI-generated content to prevent misuse, such as creating fake scientific findings, but Sadasivan and others working in this area have shown how detectors are unreliable. He and his colleagues planned adversarial attacks on detectors that rely on watermarking schemes and found that they are vulnerable to attacks meant to mislead detectors, causing them to falsely label human-written text as AI-generated. Detection tools that use neural networks trained on human and AI-generated content to reliably classify materials present an alternative, post hoc method, but they suffer from high false positive rates and are subject to false negatives if they are presented with new AI-generated material that the LLM has not been trained on..."

https://www.the-scientist.com/detecting-research-misconduct-in-the-age-of-artificial-intelligence-72300

AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

"Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass human creativity. We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text by reconstructing it from existing text snippets on the web. CREATIVITY INDEX is motivated by the hypothesis that the seemingly remarkable creativity of LLMs may be attributable in large part to the creativity of human-written texts on the web. To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm that can search verbatim and near-verbatim matches of text snippets from a given document against the web. Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2% higher than that of LLMs, and that alignment reduces the CREATIVITY INDEX of LLMs by an average of 30.1%. In addition, we explore variations in the CREATIVITY INDEX among different human authors and discuss the potential factors contributing to these differences. Finally, we showcase a novel application of CREATIVITY INDEX for zero-shot machine text detection, where it proves to be surprisingly effective—outperforming the strong zero-shot system DetectGPT by a substantial margin of 30.2%, and even surpassing a leading supervised system, GhostBuster, in five out of six domains..."

https://openreview.net/forum?id=ilOEOIqolQ

Article Identifying AI-Generated Research Papers: Methods and Considerations

" The study of AI-generated research papers aims to establish robust methods for identifying and differentiating between content authored by humans and that generated by artificial intelligence. The trustworthiness of academic discourse can be reinforced, ethical standards upheld, and the integrity of scholarly research preserved via improving detection techniques. Ongoing research into identification methodologies continues to be pivotal in achieving these objectives."

Testing of Detection Tools for AI-Generated Text

"The paper examines the general functionality of detection tools for artificial intelligence generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and Plagiarism Check) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings."

Preprint Testing of Detection Tools for AI-Generated Text

Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary?

"Currently there is a broad consensus among scholars that artificial intelligence (AI) tools can be used in research and publication, and that their use should be disclosed. Publishers and influential organizations, like the International Committee of Medical Journal Editors, have developed different and sometimes contradictory disclosure policies. We review some of these policies, examine the ethical reasons for disclosing AI use in research, and develop a framework for disclosure. We distinguish between mandatory, optional, and unnecessary disclosure of AI use, arguing that disclosure should be mandatory only when AI use is intentional and substantial. AI use is intentional when it is directly employed with a specific goal or purpose in mind. AI use is substantial when it 1) produces evidence, analysis, or discussion that supports or elaborates on the conclusions/findings of a study; or 2) directly affects the content of the research/publication. To support the application of our framework, we state three criteria for identifying substantial AI uses in research: a) using AI to make decisions that directly affect research results; b) using AI to generate content, data or images; and c) using AI to analyze content, data or images. Disclosure should be mandatory when AI use meets one of these criteria."

https://www.researchgate.net/publication/390144392_Disclosing_artificial_intelligence_use_in_scientific_research_and_publication_When_should_disclosure_be_mandatory_optional_or_unnecessary

Deep Learning Approaches for Detecting Text Generated by Artificial Intelligence

"Large language models have been a hot topic for discussion and research for quite a few years, allowing them to infiltrate in many industries, especially education. Their rise in popularity among students was caused by their vast capabilities in giving quick and reliable answers to questions on any topic. The use of these models for the purpose of generating schoolwork can be seen as a challenge to academic integrity. We investigate the development of AI capable of detecting AI-generated texts and explore with training different types of deep learning models, on a mixed dataset, containing essays, both human written and AI-generated, as well as movie reviews and books. We experimented with LSTM (Long short- term memory) and fine-tuning transformer-based models. We achieve results close to the state of the art, and, in some cases, we surpass a few of these models. For instance, one of our models surpasses a state-of-the-art model on a set of both student written and generated essays, in terms of accuracy by up to 5%, and F1 score by up to 4%, in two different experiments. Furthermore, another model of ours surpasses a state of the art model on a set of essays, but this time only in terms of precision, by only 1%. These results indicate the potential of properly fine-tuned transformer-based models, as well as the importance of a well-prepared dataset."

Article Deep Learning Approaches for Detecting Text Generated by Art...

The accuracy-bias trade-offs in AI text detection tools and their impact on fairness in scholarly publication

"Artificial intelligence (AI) text detection tools are considered a means of preserving the integrity of scholarly publication by identifying whether a text is written by humans or generated by AI. This study evaluates three popular tools (GPTZero, ZeroGPT, and DetectGPT) through two experiments: first, distinguishing human-written abstracts from those generated by ChatGPT o1 and Gemini 2.0 Pro Experimental; second, evaluating AI-assisted abstracts where the original text has been enhanced by these large language models (LLMs) to improve readability. Results reveal notable trade-offs in accuracy and bias, disproportionately affecting non-native speakers and certain disciplines. This study highlights the limitations of detection-focused approaches and advocates a shift toward ethical, responsible, and transparent use of LLMs in scholarly publication."

https://peerj.com/articles/cs-2953/

In California, Colleges Pay a Steep Price for Faulty AI Detectors

Turnitin, which sells a popular plagiarism detector, released tool to identify AI-generated writing just six months after ChatGPT’s debut. California colleges have now spent millions to catch plagiarism and AI with the company’s software. But is the tech worth it?

The biggest tipoff [of AI use] are fake quotes or hallucinations, which Turnitin isn’t good at catching...

https://undark.org/2025/07/01/california-colleges-ai-detectors/

The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors

"This study evaluates the accuracy of 16 publicly available AI text detectors in discriminating between AI-generated and human-generated writing. The evaluated documents include 42 undergraduate essays generated by ChatGPT-3.5, 42 generated by ChatGPT-4, and 42 written by students in a first-year composition course without the use of AI. Each detector’s performance was assessed with regard to its overall accuracy, its accuracy with each type of document, its decisiveness (the relative number of uncertain responses), the number of false positives (human-generated papers designated as AI by the detector), and the number of false negatives (AI-generated papers designated as human). Three detectors – Copyleaks, TurnItIn, and Originality.ai – have high accuracy with all three sets of documents. Although most of the other 13 detectors can distinguish between GPT-3.5 papers and human-generated papers with reasonably high accuracy, they are generally ineffective at distinguishing between GPT-4 papers and those written by undergraduate students. Overall, the detectors that require registration and payment are only slightly more accurate than the others."

Article The Effectiveness of Software Designed to Detect AI-Generate...

Far more authors use AI to write science papers than admit it, publisher reports

Finding highlights promise, questions about detectors of AI-generated text...

https://www.science.org/content/article/far-more-authors-use-ai-write-science-papers-admit-it-publisher-reports