Beyond detection: Software tools for academic integrity in the post-plagiarism era

Synopsis

This study investigates software tools to safeguard academic integrity in the post-plagiarism era. Using a qualitative, exploratory approach, the research maps and categorises available SW tools, and discusses their pedagogical assumptions, functions, and ethical implications.

Abstract

Post-plagiarism refers to an era in human society in which advanced technologies, including artificial intelligence and neurotechnology, are a normal part of life, including how we teach, learn, and interact daily (Eaton, 2023).

Wide accessibility of generative artificial intelligence (GenAI) and a variety of tools built on top of large language models (LLMs) are rapidly disrupting teaching and learning at all levels, particularly in higher education. This disruption shifts academic integrity debates from traditional topics such as plagiarism and contract cheating to more nuanced discussions on teaching, learning, achieving desired learning outcomes, and assessment in compliance with fundamental academic integrity values. According to Sarah Elaine Eaton, in the post-plagiarism era, advanced technologies such as artificial intelligence and emerging neurotechnologies are so deeply embedded in knowledge creation that traditional definitions of plagiarism—focused on copying from identifiable sources—are no longer sufficient. Authorship increasingly becomes hybrid and collaborative between humans and software systems, making it impractical to draw clear boundaries between original and assisted work. As a result, the emphasis shifts away from detection and punishment toward ethical responsibility, transparency, and integrity in how tools are used. Rather than asking whether work has been written by software, the post-plagiarism era centers on broader, transdisciplinary questions of ethics, accountability, and fairness in technologically mediated learning and research (Eaton, 2023).

The changing landscape of the educational sector’s needs in the field of academic integrity directly influences the number of software companies that provide tools aiming at supporting academic integrity. The initial hype after ChatGPT`s release mainly led to the emergence of tools for attempting to detect LLM-generated text. Even though these tools may work to a certain extent, the prevalence of false positives and false negatives, together with the fact that they are unable to provide any evidence for disciplinary procedures, makes them unsuitable for use in higher education (Weber-Wulff et al, 2023). This does not change with the impression that systems are “getting better”. Bassett et al. (2026) have clearly demonstrated that it is impossible to correctly detect all LLM use and will remain so.

Providers of academic integrity software tools began offering additional features going beyond the detection of plagiarised or LLM-generated content. Some focus on the process of student writing, which can provide useful insight into how a piece of text was created, but on the other hand, raises serious concerns about students’ privacy and well-being. Others aim to verify whether students have achieved expected learning outcomes, creating tests and quizzes based on submitted text that can be answered right after submission or later during a consultation with a tutor. Another method used to attempt to verify the authenticity of student writing consists of calculating various stylometric features. These can provide some indication of authorship or the use of LLMs during the writing process, although prior writing samples from students are necessary for these calculations. Nonetheless, the landscape of the software tools for academic integrity in the post-plagiarism era is diverse and changing rapidly.

The goal of this research project is to map the current state of the art in the field of software tools aiming to support the integrity of student learning and assessment. The diversity of these tools prevents us from setting objective quantitative evaluation criteria. Therefore, we employ a qualitative approach to map and categorise available tools. We aim to explore the functions these tools offer, the assumptions they embed about authorship and integrity, and how well they align with educators’ actual needs. The work emphasises familiarisation and conceptual analysis, positioning this as a first, foundational paper that sets the stage for later empirical evaluation.

This project aims to answer the following research questions:

RQ 1: What types of software tools currently exist to support academic integrity beyond plagiarism and LLM-text detection, and how can they be systematically categorised?

RQ 1a: What functions do these tools provide to help educators understand whether students have achieved desired learning outcomes?

RQ 2: How do process-based academic integrity tools conceptualise authorship, learning, and ethical writing in a post-plagiarism context?

RQ 3: What ethical, pedagogical, and practical concerns arise from using these tools, particularly regarding student agency, transparency of assessment, and the ease of circumvention?

Our study adopts a qualitative, exploratory methodology designed to map and critically examine software tools that claim to support academic integrity beyond plagiarism and LLM-generated text detection. We began with a systematic identification of tools using predefined inclusion and exclusion criteria, focusing on products aimed at higher education educators that offer process-based insights into student writing or its authenticity verification, rather than solely product-based detection. Data collection is currently ongoing and consists of document analysis (e.g., web sites, white papers, marketing materials), tool demonstrations attended by at least two researchers (or if this isn`t possible they are recorded and watched by the other researchers), and testing the tools from the educators’ perspective. Rather than evaluating accuracy or effectiveness through controlled testing, we prioritise learning how the tools function, what pedagogical approaches they employ, what kinds of data they collect, and the integrity-related problems they purport to address.

The subsequent analysis will follow an iterative, interpretive approach. Tools will be categorised based on their primary functions (e.g., process monitoring, authorship verification, stylometry, content interrogation, or assessment support), the stage of writing they engage with (process vs. product), and the pedagogical assumptions they encode about learning, authorship, and ethical use of LLMs. This will be achieved through reflexive team discussions. This methodology positions the study as a scoping and conceptual analysis that informs future empirical work.

At the time of the abstract submission, the research team identified seven tools that offer features beyond detection. We are currently conducting a comprehensive analysis and test of the functionality of these tools. We will then analyse the data and write a paper answering the above-mentioned research questions. This study provides the first systematic evaluation of post-detection integrity tools, examining how they operationalise `postplagiarism and the changing landscape of authorship`. These findings will inform ethical tool adoption and policy development in higher education.

Bassett, M. A., Bradshaw, W., Bornsztejn, H., Hogg, A., Murdoch, K., Pearce, B., & Webber, C. (2026). Heads we win, tails you lose: AI detectors in education. Journal of Higher Education Policy and Management, 1–16. https://doi.org/10.1080/1360080X.2026.2622146

Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 23. https://doi.org/10.1007/s40979-023-00144-1

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26. https://doi.org/10.1007/s40979-023-00146-z