When we talk about academic integrity in universities, we often focus on preventing plagiarism and cheating. But what if our very approach to enforcing these standards is unintentionally creating barriers for some of our most vulnerable students?
My recent research explores how current academic integrity policies and practices can negatively affect neurodivergent students—those with conditions like ADHD, dyslexia, Autism, and other learning differences. Our existing systems, structures, and policies can further marginalize students with cognitive differences.
The Problem with One-Size-Fits-All
Neurodivergent students face unique challenges that can be misunderstood or ignored. A dyslexic student who struggles with citation formatting isn’t necessarily being dishonest. They may be dealing with cognitive processing differences that make these tasks genuinely difficult. A student with ADHD who has trouble managing deadlines and tracking sources is not necessarily lazy or unethical. They may be navigating executive function challenges that affect time management and organization. Yet our policies frequently treat these struggles as potential misconduct rather than as differences that deserve support.
Yet our policies frequently treat these struggles as potential misconduct rather than as differences that deserve support.
The Technology Paradox for Neurodivergent Students
Technology presents a particularly thorny paradox. On one hand, AI tools such as ChatGPT and text-to-speech software can be academic lifelines for neurodivergent students, helping them organize thoughts, overcome writer’s block, and express ideas more clearly. These tools can genuinely level the playing field.
On the other hand, the same technologies designed to catch cheating—especially AI detection software—appear to disproportionately flag neurodivergent students’ work. Autistic students or those with ADHD may be at higher risk of false positives from these detection tools, potentially facing misconduct accusations even when they have done their own work. This creates an impossible situation: the tools that help are the same ones that might get students in trouble.
Moving Toward Epistemic Plurality
So what’s the solution? Epistemic plurality, or recognizing that there are multiple valid ways of knowing and expressing knowledge. Rather than demanding everyone demonstrate learning in the exact same way, we should design assessments that allow for different cognitive styles and approaches.
This means:
Rethinking assessment design to offer multiple ways for students to demonstrate knowledge
Moving away from surveillance technologies like remote proctoring that create anxiety and accessibility barriers
Building trust rather than suspicion into our academic cultures
Designing universally, so accessibility is built in from the start rather than added as an afterthought
What This Means for the Future
In the postplagiarism era, where AI and technology are seamlessly integrated into education, we move beyond viewing academic integrity purely as rule-compliance. Instead, we focus on authentic and meaningful learning and ethical engagement with knowledge.
This does not mean abandoning standards. It means recognizing that diverse minds may meet those standards through different pathways. A student who uses AI to help structure an essay outline isn’t necessarily cheating. They may be using assistive technology in much the same way another student might use spell-check or a calculator.
Call to Action
My review of existing research showed something troubling: we have remarkably little data about how neurodivergent students experience academic integrity policies. The studies that exist are small, limited to English-speaking countries, and often overlook the voices of neurodivergent individuals themselves.
We need larger-scale research, global perspectives, and most importantly, we need neurodivergent students to be co-researchers and co-authors in work about them. “Nothing about us without us” is not just a slogan, but a call to action for creating inclusive academic environments.
Key Messages
Academic integrity should support learning, not create additional barriers for students who already face challenges. By reimagining our approaches through a lens of neurodiversity and inclusion, we can create educational environments where all students can thrive while maintaining academic standards.
Academic integrity includes and extends beyond student conduct; it means that everyone in the learning system acts with integrity to support student learning. Ultimately, there can be no integrity without equity.
Read the whole article here: Eaton, S. E. (2025). Neurodiversity and academic integrity: Toward epistemic plurality in a postplagiarism era. Teaching in Higher Education. https://doi.org/10.1080/13562517.2025.2583456
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.
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.
This week I did an invited presentation for the European Network for Academic Integrity (ENAI) Integrity for All Working Group.
As part of my presentation, I shared this bibliography of resources that I’ve worked on over the past several years on academic integrity as it relates to equity, diversity, inclusion, accessibility, and decolonization. These topics have become increasingly important to me over the past half decade and it is more important now than it ever has been to elevate the importance of these topics, along with human rights and social justice, when addressing matters of student conduct.
This bibliography contains a list of academic integrity articles, presentations, and resources that focus on these topics.
I’ve done my best to prepare this list according to APA 7 conventions, but please forgive any errors.
I aim to make as much of my content open access. If there is anything on this list that you cannot access, please contact me directly and I’ll see what I can do.
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:
Posted by Sarah Elaine Eaton, Ph.D. 
Learning, Teaching and Leadership 
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