When I was a student, I read works by Philip Altbach and Hans de Wit. Later, I became a fan of Elena Denisova-Schmidt and her work on fraud and corruption in higher education. Something very special happens when you actualy get to work with folks whom you have admired for years or even decades. When Elena invited me to contribute to a new edited volume she was working on with Philip Altbach and Hans de Wit, I jumped at the chance.
“Introduction In this chapter, I discuss corruption in the post-plagiarism era, focusing specifically on the weaponization of plagiarism and, by extension, the manipulation of reputation by moral judgment using intentionally orchestrated campaigns or selective disclosure with a focus on higher education. I begin by defining key terms such as corruption, plagiarism, and post-plagiarism. Then, I discuss the development of corruption in the age of artificial intelligence. I explore the weaponization of reputation and morality, and consider the impact of such tactics on society and democracy. Corruption, moral grandstanding, and virtue signaling are not new; however, technologies such as social media platforms and artificial intelligence can—and have—catalyzed some forms of corruption. I conclude by considering the future of ethics and integrity in the post-plagiarism age, including a call to action to uphold and enact integrity going forward. While concerns about post-plagiarism extend to almost all areas of human life, in my chapter I deal only with the realm of higher education.”
Sarah Elaine Eaton, PhD, is a Professor and Research Chair in the Werklund School of Education at the University of Calgary, Canada. Opinions are my own and do not represent those of my employer.
Chapter 10 explores the theoretical, policy, and practical aspects of navigating pedagogical ethics in learning environments augmented by generative artificial intelligence (GenAI). The chapter considers the role of higher education and the need to reconceptualize academic cheating in a post-plagiarism era. It discusses the role of learner agency, accountability, and responsibility within the context of learning and academic integrity. The chapter offers informed guidance for educators to incorporate GenAI in meaningful ways into teaching, learning, and assessment.
Our chapter is open access and free to read online and to download. We are really excited to continue the conversations happening about postplagiairsm and how we can can navigate teaching, learning, and assessment ethically in the age of generative AI.
Sarah Elaine Eaton, PhD, is a Professor and Research Chair in the Werklund School of Education at the University of Calgary, Canada. Opinions are my own and do not represent those of my employer.
In a recent talk I did at the University of Toronto Mississauga, I was chatting with a couple of folks afterwards and they asked if one specific slide was available as an infographic. It wasn’t and I promised to follow up. (This blog post is for you Amanda and Victoria!)
Artificial intelligence tools can generate human-like text and knowledge creation has become increasingly collaborative, questions arise about traditional academic practices. Although many conventions are being reimagined, citing, referencing, and attribution remain important. Attribution — acknowledging those who have shaped our thinking—transcends the mechanical act of citing sources according to prescribed formats. It represents an ethical commitment to intellectual honesty and respect (Eaton, 2023).
Attribution is a cornerstone of the postplagiarism framework. In the postplagiarism era, where the boundaries between human and AI-generated content blur and traditional definitions of authorship are challenged, the practice of acknowledging our intellectual influences becomes more vital, not less (Kumar, 2025). Attribution serves multiple purposes: it honors those who contributed to knowledge development, establishes credibility for the writer, and allows readers to explore foundational ideas more deeply.
Many educators and students mistakenly equate attribution with the technical minutiae of citation styles. I am talking here about the precise placement of commas, periods, and parentheses. While these conventions serve practical purposes in academic writing, they represent only the surface of what attribution entails (Gladue & Poitras Pratt, 2024). At its core, attribution demands that we answer questions such as: How do I know what I know? Who were my teachers? Whose ideas have influenced my thinking?
In this post (a re-blog from the postplagiarism site) I explore attribution as an enduring ethical principle within the postplagiarism framework. We’ll distinguish between citation as mechanical practice and attribution as intellectual honesty, examine how attribution practices might evolve with technology, and consider how we might teach attribution as a value rather than merely a skill (Eaton, 2024). Throughout, we’ll keep returning to a central idea: even as definitions of plagiarism transform, the need to recognize and pay respect to those from whom we have learned remains constant.
Attribution vs. Citation: Understanding the Differences
Understanding the distinction between attribution and referencing is crucial in our discussion of academic integrity in a postplagiarism era. The terms ‘referencing’ and ‘attribution’ are often used interchangeably, but they represent fundamentally different approaches to giving credit where it is due. In the table below, I present an overview of some of the differences.
Table 1
Attribution versus Referencing
Citing and Referencing
First, let’s talk about citing and referencing. Citing is often referred to in-text citation. In APA format, for example, we cite sources in the main body of the text as we write. Then, we produce a list of references, usually with the heading “References” at the end of the paper. (I have modelled this practice throughout). If we follow APA, the sources cited in the body of the text should exactly match the sources in the reference list at the end, and vice versa. So, citing and referencing go hand-in-hand. For the purposes of this post, I’ll use the term ‘referencing’ collectively to refer to both citing and referencing, given that the two are intertwined.
A foundational question about referencing is: How can I learn and demonstrate the technical norms of a prescribed style manual?
Let me give you an example of what I mean. I did my undergraduate and master’s degrees in literature. We used the Modern Language Association (MLA) style guide. When I moved over to Education to undertake my PhD, I had to learn a completely different style, the one prescribed by the American Psychological Association (APA), as that is the style used across much of the social sciences. I often describe having to shift from learning MLA style to APA style as intellectual trauma. I had spent years meticulously learning to be rule-compliant to MLA style. I knew the details of MLA style inside and out. Having to learn APA style meant unlearning everything I’d spent years learning about MLA style. My PhD supervisor marked up drafts of my work with a red pen, noting APA errors everywhere.
I bought the APA style guide (we were using the 5th edition back then) and set out to memorize every detail to ensure that I knew the rules. Citing and referencing are taught and evaluated using style guides, checklists, and technical rubrics to evaluate how well someone has followed the rules. Citing and referencing are essentially about rule compliance.
Attribution
Attribution goes beyond the technical aspects of rule compliance. When we give attribution, we dig deeper into questions about our intellectual lineage. We ask: How do I know what I know? Who did I learn from? Who influenced the those from whom I have learned?
Attribution requires meta-cognitive awareness and evaluative judgement. If you are unfamiliar with these concepts, I recommend the work of Bearman and Luckin (2020), Fischer et al. (2024), and Tai et al. (2018). Collectively, they explain evaluative judgement and meta-cognitive awareness better than I ever could.
(If you’re paying attention, you’ll see that I just combined citing with attribution there… I provided the sources as per the citing rules of APA, and I also talked about how I learned about deeper concepts from some terrific folks who have done deep work on the topic. See, you can combine citing and referencing with attribution. It’s not all or nothing.)
We teach attribution through a shared collective understanding, by establishing communal expectations and through (often informal) relational coaching.
In everyday conversations, we often reference where we learned ideas. We say, “As my grandmother always said…” or “I read in an article that…” These informal attribution practices demonstrate how instinctively we connect ideas to their sources. Citing and referencing formalizes socialized practices that have extended across various cultures for centuries.
When we give attribution, we show gratitude for the conversations, texts, and teachings that have formed our understanding. This perspective shifts attribution from a defensive practice (avoiding plagiarism accusations) to an affirmative one (acknowledging the intellectual debt we owe to others who have generously shared their knowledge with us).
Acknowledging Others’ Work in the Age of GenAI
Generative AI tools have disrupted our traditional understandings of authorship and attribution. These technologies create new questions about intellectual ownership and acknowledgment practices that our citing and referencing systems weren’t designed to address. GenAI models produce outputs based on massive training datasets containing human-created works. When a student uses ChatGPT to draft an essay, the resulting text represents a complex blend of sources that even the AI developers cannot fully trace. This opacity challenges our ability to attribute ideas to their original creators (Kumar, 2025).
The collaborative nature of AI-assisted writing further blurs authorship boundaries. Who deserves credit when a human prompts, edits, and refines AI-generated text? The distinction between tool and co-creator is difficult to establish. This is another tenet in the postplagiarism framework.
In work led by my colleague, Dr. Soroush Sabbagan, we found graduate students wanted agency in how they integrate AI tools while maintaining academic integrity (Sabbaghan and Eaton (2025). The graduate students who participated in our study, “Participants also emphasized the importance of combining their own expertise and judgment with the AI’s suggestions to create truly original research.” (Sabbaghan & Eaton, 2025, p. 18).
The postplagiarism framework offers helpful guidance by distinguishing between control and responsibility. Although students may share control with AI tools, they retain full responsibility for the integrity of their work, including proper attribution of all sources, both human and machine. Ultimately, the goal isn’t to prevent AI use but to cultivate ethical practices for learning, working, and living.
As Corbin et al (2025) have noted, AI presents wicked problems when it comes to assessment. I would extend their idea further by saying that AI presents wicked problems for plagiarism in general. There are no absolute definitions of plagiarism, but if we think about citing, referencing, and giving attribution as ways of preventing or mitigating plagiarism, then AI has certainly complicated everything. These are problems that we do not have all the answers to, but disentangling the difference between rule-based referencing and attribution as a social practice of paying our respects to those from whom we have learned, might be one step forward as we enter into a postplagiarism age.
The ideas I’ve shared here are not intended to be exhaustive, but rather to help folks make sense of some key differences between referencing and giving attribution and to recognize that citing and referencing are deeply connected to rule compliance and technical rules, whereas giving attribution can at times be imprecise, but may in fact be more deeply-rooted in a desire to give respect where it is due.
As I have tried to model above, it does not have to be all or nothing. Referencing can exist in the absence of any desire to respect others for the work they have created and attribution can be given orally or in any variety of ways that may not comply with a technical style guide. When we are working with students, it can be helpful to unpack the differences and talk about why both are need in academic environments.
There is more to say on this topic, but I’ll wrap up here for now. Thanks again to Amanda and Victoria, who nudged me to write down and share ideas that I have been talking about for a few years now.
References
Bearman, M., & Luckin, R. (2020). Preparing university assessment for a world with AI: Tasks for human intelligence. In M. Bearman, P. Dawson, R. Ajjawi, J. Tai, & D. Boud (Eds.), Re-imagining University Assessment in a Digital World (pp. 49–63). Springer International Publishing. https://doi.org/10.1007/978-3-030-41956-1_5
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2025). The wicked problem of AI and assessment. Assessment & Evaluation in Higher Education, 1–17. https://doi.org/10.1080/02602938.2025.2553340
Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 1–10. https://doi.org/10.1007/s40979-023-00144-1
Eaton, S. E. (2024). Decolonizing academic integrity: Knowledge caretaking as ethical practice. Assessment & Evaluation in Higher Education, 49(7), 962-977. https://doi.org/10.1080/02602938.2024.2312918
Fischer, J., Bearman, M., Boud, D., & Tai, J. (2024). How does assessment drive learning? A focus on students’ development of evaluative judgement. Assessment & Evaluation in Higher Education, 49(2), 233–245. https://doi.org/10.1080/02602938.2023.2206986
Kumar, R. (2025). Understanding PSE students’ reactions to the postplagiarism concept: a quantitative analysis. International Journal for Educational Integrity, 21(1), 9. https://doi.org/10.1007/s40979-025-00182-x
Sabbaghan, S., & Eaton, S. E. (2025). Navigating the ethical frontier: Graduate students’ experiences with generative AI-mediated scholarship. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00454-6
Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: enabling students to make decisions about the quality of work. Higher Education, 76(3), 467–481. https://doi.org/10.1007/s10734-017-0220-3
Note: This is a re-blog. See the original post here:
One of the great joys of being a journal editor is getting to share good news when a new article is published. I am going to make more of an effort to do this on my blog because the International Journal for Educational Integrity is a high quality (Q1) journal with lots to offer when it comes to academic integrity. We accept only about 10% of manuscripts submitted to the journal, so having an article published is a great achievment!
Artificial intelligence has created new opportunities in higher education, enhancing teaching and learning methods for both students and educators. However, it has also posed challenges to academic integrity.
Objective
To describe the evolution of scientific production on academic integrity and artificial intelligence in higher education.
Methodology
A bibliometric analysis was carried out using VOSviewer software and the Bibliometrix package in R. A total of 467 documents published between 2017 and 2025, retrieved from the Web of Science database, were analyzed.
Results
The analysis reveals a rapid expansion of the field, with an annual growth rate of 71.97%, concentrated in journals specializing in education, academic ethics, and technology. The field has evolved from a focus on the use of artificial intelligence in dishonest practices to the study of its integration in higher education. Four main lines of research were identified: the impact and adoption of artificial intelligence, implications for students, academic dishonesty, and associated psychological factors.
Conclusions
The field is at an early stage of development but is expanding rapidly, albeit with fragmented evolution, limited collaboration between research teams, and high editorial dispersion. The analysis shows a predominance of descriptive approaches, leaving room for the development of theoretical frameworks.
Originality or value
This study provides an overview and updated of the evolution of research on artificial intelligence and academic integrity, identifying trends, collaborations, and conceptual gaps. It highlights the need to promote theoretical reflection to guide future practice and research on the ethical use of artificial intelligence in higher education.
Sarah Elaine Eaton, PhD, is a Professor and Research Chair in the Werklund School of Education at the University of Calgary, Canada. Opinions are my own and do not represent those of my employer.
One of the tenets of postplgiarism is that artificial intelligence technologies will help us overcome language barriers and understand each other in countless languages (Eaton, 2023).
We already have apps that translate text from photos taken on our phones. These apps help when travelling in countries where you don’t speak the language. Now we have applications extending this idea further into wearable technology.
Wearable technology has existed for years. We wear fitness gadgets on our wrists to track steps. AI technology will become more embedded into the software that drives these devices.
New wearable devices have emerged quickly, with varying levels of success. One example was introduced about a year after ChatGPT was released. The company was called Humane and the device was powered by OpenAI technology.
The Humane pin was wearable technology that included a square-shaped pin and a battery pack that attached magnetically to your shirt or jacket. It was marketed as enabling users to communicate in just about any language (Pierce, 2023). To Star Trek fans, the resemblance to a communicator badge was unmistakable.
The device retailed for $700 US and required a software subscription of $24 USD per month, which provided data coverage for real-time use through their proprietary software based on a Snapdragon processor (Pierce, 2023). The device only worked with the T-Mobile network in the United States. Since I live in Canada and T-Mobile isn’t available here, I never bought one.
Like others, I watched with enthusiasm, hoping the product would succeed so it could expand to other markets. Pre-order sales indicated huge potential for success. By late 2023, the Humane pin was heralded as “Silicon Valley’s ‘next big thing'” (Chokkattu, 2025a). (I can’t help but wonder if the resemblance to a Star Trek communicator badge was part of the allure.)
When tech enthusiasts received the product in 2024, the reviews were dismal. One reviewer gave it 4 out of 10 and called it a “party trick” (Chokkattu, 2024). (Ouch.) The Humane pin did not live up to its promises. Less than a year after its release, the device was dead. HP acquired the company and retired the product at the end of February 2025.
Tech writer Julian Chokkattu declared the device was e-waste and suggested it could be used as a paperweight or stored in a box in the attic. Chokkattu (2025b) says, “In 50 years, you’ll accidentally find it in the attic and then tell your grandkids how this little gadget was once—for a fleeting moment—supposed to be the next big thing.”
Learning from Failure: The Promise Remains
The failure of the Humane AI Pin does not invalidate the vision of AI-powered real-time translation. The device failed because of execution problems—poor battery life, overheating, an annoying projector interface, and limited functionality (Chokkattu, 2024). The core AI translation capabilities were among the features that actually worked.
Real-time translation represents one of the most compelling applications of generative AI. When the technology works seamlessly, it can transform human communication. The Humane pin showed us what not to do: create a standalone device with too many functions, none executed well.
The future of AI translation likely lies not in dedicated hardware but in integration with devices we already use. Our smartphones, earbuds, and smart glasses will become the vehicles for breaking down language barriers. The underlying AI models continue to improve rapidly, and the infrastructure for real-time translation grows more robust.
The Humane pin’s failure teaches us that good ideas require good execution. But we should not abandon the goal of using AI to help humans understand each other across languages. That goal remains as important as ever in our increasingly connected world. The technology will improve, the interfaces will become more intuitive, and the promise of the postplagiarism tenet—that language barriers will begin to disappear—will eventually be realized.
The Humane AI pin may be dead, but we should keep our hope alive that AI technology will help us overcome language barriers and provide new opportunities for communication.
Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19(1), 1–10. https://doi.org/10.1007/s40979-023-00144-1
Sarah Elaine Eaton, PhD, is a Professor and Research Chair in the Werklund School of Education at the University of Calgary, Canada. Opinions are my own and do not represent those of my employer.
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