The plagiarism conversation in education has been running on the same assumptions for decades: students produce original work, and if they don’t, we detect it. AI has broken that logic. And Sarah Elaine Eaton (2023) argues we need a fundamentally new framework for thinking about integrity, authorship, and responsibility in a world where humans and machines routinely co-create text.
Her article, “Postplagiarism: Transdisciplinary Ethics and Integrity in the Age of Artificial Intelligence and Neurotechnology,” published in the International Journal for Educational Integrity, introduces postplagiarism as a concept that moves beyond policing and toward ethical reasoning. She doesn’t claim plagiarism disappears. She argues that our inherited definitions can’t hold in a world where the boundaries between human writing and machine output have become genuinely difficult to trace.
What Does Postplagiarism Mean?
Postplagiarism describes an environment in which humans and intelligent technologies routinely work together to produce knowledge. The concept recognizes that AI has complicated familiar distinctions between original writing, assistance, editing, and authorship. A student may generate ideas with AI, write a draft independently, use AI to reorganize it, and then revise the result again. Identifying the precise origin of every sentence becomes increasingly difficult.
Eaton’s response is to place greater emphasis on ethics, attribution, and human responsibility. The important questions concern how AI contributed, whether its use was appropriate, how that contribution was disclosed, and whether the human author can take responsibility for the finished work. Academic integrity still matters. Its application now has to account for writing processes in which human and machine contributions may be deeply intertwined.
Hybrid Writing Is Already Here
The first major argument in the paper is that human-AI co-authorship is already common and will soon feel entirely ordinary. When a student uses ChatGPT to brainstorm, refine a draft, or restructure an argument, the resulting text is neither fully human nor fully machine. It’s a hybrid product. And in hybrid writing, tracing clean lines between what the person contributed and what the AI generated becomes practically impossible.
Detection won’t save us here. Eaton points out that OpenAI itself acknowledged the limits of AI-detection tools, and public cases of false accusations have reinforced how fragile the detection-as-policing model really is. I’ve written about this limitation when covering Corbin, Dawson, and Liu’s (2025) argument that assessment rules without structural enforcement create an “enforcement illusion.” Eaton arrives at the same conclusion from a different direction: surveillance is a dead end, and we need to build integrity into how we think about writing, not into how we police it.
Responsibility Stays Human
Responsibility anchors the entire argument, and I think this is where the paper is strongest. Humans can delegate aspects of writing to AI, but accountability doesn’t transfer. As Eaton puts it: “Humans can retain control over what they write, but they can also relinquish control to artificial intelligence tools if they choose. Although humans can relinquish control, they do not relinquish responsibility for what is written” (p. 5).
Publishers have already drawn this line by refusing to list AI tools as co-authors. In education, the implications are direct: students who use AI still bear responsibility for accuracy, validity, and the intellectual substance of what they submit. Faculty, in turn, bear responsibility for designing assessments that make learning visible even when AI is part of the process.
Cleland et al.’s (2025) AMEE guide on AI disclosure operates on the same principle. Transparency about AI use is a methodological obligation, not a confession. Eaton’s framework explains why: if responsibility remains human regardless of how much AI contributed, then disclosure becomes a way of demonstrating that responsibility, not a way of admitting weakness.
Attribution as Intellectual Stewardship
Eaton also expands attribution beyond the mechanical act of citation into something relational. Eaton argues that attribution is a practice of intellectual stewardship that reflects care for knowledge communities: “Attribution, on the other hand, is about knowing others’ work, being able to speak to it accurately, and showing respect for others’ contributions” (p. 6).
I find this particularly relevant to the conversations I’ve been following about AI and authorship. Kalantzis and Cope (2025) redefined literacy in the AI age as design agency, the active, intentional work of making meaning with purpose and voice. Eaton’s relational view of attribution fits naturally alongside that argument. When we cite, we aren’t just following format rules. We’re participating in a web of intellectual relationships that AI can generate references for but can’t meaningfully participate in. The human writer does that work.
She also draws on Indigenous scholarship to show how standard citation systems marginalize certain forms of knowledge. Oral traditions, community knowledge, and relational ways of knowing don’t fit neatly into APA brackets. Postplagiarism, as she argues, asks us to rethink attribution at that deeper level, and I think this is an important expansion of the conversation that most AI-in-education literature overlooks.
On the question of whether AI diminishes human creativity, Eaton pushes back firmly. Technologies have always provoked similar anxieties, from the printing press to smartphones, and human creative capacity has adapted every time. AI may provoke, assist, or inspire, but it doesn’t replace human imagination.
I agree, and I think the evidence supports her position. Niloy et al. (2024) found that ChatGPT reduced originality in creative writing when students used it passively, but the problem was the passive use, not the tool itself. Roe, Furze, and Perkins (2025) proposed their Critical AI Literacy framework precisely to help students engage with AI critically and creatively. Eaton’s argument reinforces the same point from a philosophical angle: creativity is a human capacity, and the question is whether we design educational experiences that nurture it alongside AI or allow AI to substitute for it.
The Neurotechnology Warning
The most forward-looking section of the paper concerns neurotechnology and brain-computer interfaces. Eaton warns that educators were unprepared for both COVID-19 and generative AI, and that commercially available neurotechnology may arrive with similar speed. Once these technologies become invisible and embedded, the very concept of detecting unauthorized assistance collapses.
Her language is provocative:
It might be reasonable to assume that when commercialized neuro-educational technology becomes implantable/ingestible/embeddable and cosmetically invisible the academic integrity arms race will be over, as detection will truly be an exercise in futility. (p. 8).
Whether or not brain-computer interfaces reach classrooms in the near term, the broader point holds. Each generation of technology makes detection harder and the case for ethical reasoning stronger. If we build our integrity systems on the assumption that we can catch people, we’re building on something that gets weaker every year. If we build on the assumption that we should teach people to take responsibility for their intellectual work, we’re building something durable.
What Postplagiarism Means for Educators
For educators, postplagiarism changes the questions we ask about student work. Trying to determine whether a text is completely human-written will often tell us less than examining how the student used AI, what intellectual decisions the student made, and whether the submitted work reflects genuine understanding.
This requires clearer conversations with students about authorship and responsibility. Students need to know that using AI does not transfer accountability to the tool. They remain responsible for unsupported claims, fabricated references, inaccurate information, and ideas they cannot explain. Disclosure should document how the work was produced and where human judgment entered the process.
In practice, educators can respond by asking students to:
- describe where and why AI was used;
- retain prompts or relevant records of their interaction with AI;
- verify factual claims and references independently;
- explain important choices made during drafting and revision;
- identify which parts of the finished work reflect their own analysis;
- take responsibility for the accuracy and intellectual substance of everything submitted.
Assessment design also matters. Drafts, reflections, conferences, oral follow-ups, and process records can reveal learning more effectively than attempts to classify a finished text as human- or AI-generated. These practices allow educators to evaluate how students developed their ideas and exercised judgment.
Postplagiarism does not give students permission to submit unexamined AI output. It gives us a more realistic framework for discussing integrity when writing is increasingly produced through interaction with intelligent tools. For me, Eaton’s most useful contribution is her insistence that responsibility remains human. Technologies may participate in the production of knowledge, but people must still answer for how that knowledge is created, represented, and shared.
Reference
- Cleland, J., Driessen, E., Masters, K., Lingard, L., & Maggio, L. A. (2025). When and how to disclose AI use in academic publishing: AMEE Guide No. 192. Medical Teacher. https://doi.org/10.1080/0142159X.2025.2607513
- Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: Why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 50(7), 1087–1097. https://doi.org/10.1080/02602938.2025.2503964
- Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(23). https://doi.org/10.1007/s40979-023-00144-1
- Roe, J., Furze, L., & Perkins, M. (2025). Digital plastic: A metaphorical framework for Critical AI Literacy in the multiliteracies era. Pedagogies: An International Journal. Advance online publication. https://doi.org/10.1080/1554480X.2025.2557491
- Sperber, L., MacArthur, M., Minnillo, S., Stillman, N., & Whithaus, C. (2025). Peer and AI Review + Reflection (PAIRR): A human-centered approach to formative assessment. Computers and Composition, 76, 102921. https://doi.org/10.1016/j.compcom.2025.102921
- UNESCO. (2024). AI competency framework for students. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/JKJB9835
