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 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.
I have been doing a lot of travelling lately, giving talks on postplagiarsm and academic integrity in the age of generative artificial intelligence. Recently I was at the Calgary airport and ask I was going through the security screening process, I took out my laptop and placed it in the bin to be screened. A staff member pointed to my laptop and asked, “Are you a professor at the University of Calgary?!”
She recognized the laptop sticker. It says #UHaveIntegrity, which is the slogan for our academic integrity campaign at the University of Calgary.
I replied, “Yes! Yes, I am! Are you a student?” She replied yes, that she was a majoring in political science.
It was most inspiring moment I have ever had going through airport security!
Shifting the Conversation
Traditional academic integrity messaging often starts from a deficit model, emphasizing what students should not do and the consequences of misconduct. This approach inadvertently positions students as potential cheaters rather than developing adults.
The #UHaveIntegrity campaign reframes this conversation. We acknowledge and celebrate students as whole human beings with existing ethical foundations. Our role as educators shifts from policing to supporting their continued development.
From Classroom to Career
Academic integrity transcends assignment submissions and exam protocols. It forms the foundation for ethical decision-making that extends beyond graduation. The research literature demonstrates that students who develop strong ethical frameworks during their education carry these principles into their professional lives (e.g., Guerrero-Dib et al., 2020; Tammeleht et al., 2022).
When we recognize that students already have integrity, we create space for authentic dialogue about ethical challenges rather than simply enforcing rules. Students become active participants in their ethical development rather than passive recipients of policy statements.
Supporting Student Success
The #UHaveIntegrity campaign represents our commitment to supporting student learning and academic success. By starting from a position of trust, we establish educational environments where:
Students feel empowered to ask questions about citation and collaboration
Errors become learning opportunities rather than character judgments
Discussions about integrity focus on growth rather than compliance
Moving Toward Postplagiarism
The #UHaveIntegrity campaign exemplifies what we call postplagiarism pedagogy—an educational approach that moves beyond rule-based instruction to consider how learning, writing, and collaboration can happen ethically in the age of generative AI.
Postplagiarism does not mean ignoring source citation or academic honesty. Instead, it acknowledges that students develop as writers in a world where information flows differently than in previous generations. ChatGPT was released almost two and half years ago, in November 2022. Here we are in 2025 and our historical norms around citing and referencing are inadequate in the age of remix, mashup, and co-creation with GenAI.
By starting from the premise that students have integrity, educators can engage in richer conversations about:
How knowledge creation occurs in digital environments
Why proper attribution matters in different contexts
How collaboration and individual work intersect in contemporary scholarship
In a small-scale study led by my colleague, Dr. Soroush Sabbaghan, we interviewed ten graduate students about their use of GenAI. They told us that they want and need guidance and support to use GenAI ethically. They also wanted agency to use GenAI tools to help them do their research. They wanted GenAI tools to help them amplify their own voices and discover new perspectives. Although our study was small, the findings are worthy of consideration. You can check out the article here if you are interested.
Moving Forward Together
The sticker on my laptop serves as a daily reminder of our responsibility as educators. It’s up to us educators to create learning environments that nurture the integrity students already possess, providing them with the knowledge and skills to navigate increasingly complex ethical landscapes.
The next time you encounter academic integrity challenges in your classroom, remember: your students have integrity. The question is not about instilling values they lack, but supporting their application of existing values to new academic contexts.
#UHaveIntegrity is more than a hashtag. It is our University of Calgary commitment to educational partnerships built on integrity and mutual respect.
University of Calgary Academic Integrity Week 2025
This year at the University of Calgary, we’ll mark Academic Integrity Week from October 14-17. Our themes are artificial intelligence and engaging students as partners in academic integrity. We are excited to engage with students on these important topics!
References
Guerrero-Dib, J. G., Portales, L., & Heredia-Escorza, Y. (2020). Impact of academic integrity on workplace ethical behaviour. International Journal for Educational Integrity, 16(1), 2. https://doi.org/10.1007/s40979-020-0051-3
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
Tammeleht, A., Löfström, E., & Rodríguez-Triana, j. M. J. (2022). Facilitating development of research ethics and integrity leadership competencies. International Journal for Educational Integrity, 18(1), 1–23. https://doi.org/10.1007/s40979-022-00102-3
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.
Posted by Sarah Elaine Eaton, Ph.D. 
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