On March 28, 2026, I had the pleasure of joining educators from across Canada for the National Day of Learning, hosted by Let’s Talk Science. This one-day, nation-wide professional learning event brought together K–12 teachers, post-secondary educators, and policy leaders to explore some of the most pressing issues shaping education today, with artificial intelligence high on the agenda.
I was invited to deliver a session titled “Interfacing with the Future: Wearable AI and Academic Integrity for K–12 and Higher Ed.” What follows are a few reflections and key ideas from that conversation, hosted by Dr. Alec Couros.
Moving into the Postplagiarism Era
One of the central ideas framing my talk is postplagiarism. In this reality, artificial intelligence is no longer an external tool that students occasionally use, but rather, it is embedded into everyday life and learning.
Students are already engaging with AI in ways that challenge traditional notions of authorship, originality, and academic work. The question is no longer if students will use AI, but how.
This shift requires a corresponding change in how we think about academic integrity. Detection and surveillance, long relied upon as primary strategies, are no longer sufficient. Instead, we must rethink how we design learning environments that foster integrity from the ground up.
From Tools to Wearables: How AI is Advancing
A key focus of my presentation was the rapid evolution from AI tools to AI wearables — particularly smart glasses and other forms of cosmetically invisible interfaces. The talk was based, in part, on our recent article in Canadian Perspectives on Academic Integrity.
Wearable technologies integrate AI directly into our physical experience of the world. Rather than pulling out a device, users can access real-time information, transcription, and prompts seamlessly through their field of vision.
This shift introduces both opportunities and tensions:
Enhanced presence: Wearables allow users to maintain eye contact and engagement without device distraction.
Efficiency gains: Tasks such as note-taking or translation can be automated in real time.
At the same time, these benefits come with real challenges including information overload, privacy concerns, and technical limitations. More importantly for educators, they fundamentally disrupt assumptions about what it means to “know” something independently.
New Technology ≠ Cheating
One of the most important messages I emphasized is this: new technology does not automatically equal academic misconduct.
If a tool is permitted, then its use is not cheating. The real issue lies in unauthorized use or misuse in ways that create unfair advantage.
We must also remain attentive to equity and accessibility. Some wearable technologies may be used as accommodations, making it essential that our integrity policies are inclusive and nuanced rather than rigid and punitive.
Designing for Integrity (Not Surveillance)
Rather than doubling down on detection, I encourage educators to shift their focus toward designing for integrity.
This means:
Prioritizing assessment validity: If an AI system can complete a task without genuine understanding, then the task itself needs to be rethought.
Moving beyond “gotcha” approaches: Surveillance-based strategies erode trust and are increasingly ineffective.
Building a culture of integrity: Integrity is not enforced; it is cultivated through meaningful learning experiences.
Bridging K–12 and Post-Secondary Education
Another key theme was the gap between K–12 and post-secondary expectations.
In K–12 environments, students are often encouraged to explore technology as part of their learning. In contrast, post-secondary institutions frequently operate under the assumption that students already understand complex academic integrity rules.
As AI continues to evolve, this gap becomes more pronounced. We need stronger alignment across educational sectors to ensure that students are supported, rather than being set up for failure, as they transition between systems. (Myke Healy has a great paper on the topic of GenAI in the K-12 context that is worth reading.)
Looking Ahead
If there is one takeaway from this experience, it is this: wearable AI is not a future scenario. It is already here.
As educators, we are being called to respond not with fear, but with thoughtful, research-informed approaches. The challenge is not simply to manage technology, but to reimagine teaching, learning, and assessment in ways that remain meaningful in an AI-integrated world.
Events like the National Day of Learning remind me of the power of community. Bringing educators together to share ideas, ask difficult questions, and explore new possibilities is essential as we navigate this rapidly changing landscape.
Thank you to Let’s Talk Science and to Dr. Alec Couros for the opportunity to be part of this important conversation, and to all the educators who continue to lead with curiosity, courage, and care.
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 this case, the court dismissed the lawsuit, noting that the university’s academic integrity policy explicitly applied to students, but not faculty. Although the legal reasoning is sound, the ethical implications are profound.
Role Modeling: Faculty are the standard-bearers of scholarly conduct. When instructors fail to uphold integrity, it undermines the credibility of the entire educational process. This is an idea repeated over and over again in the Second Handbook of Academic Integrity and one that emerged in the Comprehensive Academic Integrity Framework: academic integrity includes, and extends beyond student conduct.
Trust and Fairness: Students trust that their learning environment is built on fairness. A double standard, where plagiarism policies apply only to students, erodes that trust. As I have written about elsewhere, trust has been a central theme of academic integrity for decades and is a foundation for education and it applies not only to students, but to faculty and administrators as well.
Institutional Reputation: Universities thrive on public confidence in their academic rigour. Ignoring faculty misconduct risks reputational damage far beyond the classroom.
What should change?
Institutions need comprehensive integrity policies that apply to everyone—students, faculty, and administrators. These policies should include clear definitions, reporting mechanisms, and consequences for violations. Academic integrity is a shared responsibility, and everyone in the learning community is accountable.
Recommendations for Higher Education Institutions
Expand and Unify Policy Scope: Ensure academic integrity policies explicitly apply to faculty, staff, and administrators, not just students.
Develop Reporting Mechanisms: Create confidential, transparent processes for reporting and investigating faculty misconduct.
Mandatory Training: Require regular integrity training for faculty, emphasizing ethical scholarship and teaching practices, as well as research ethics.
Institutional Culture: Promote integrity as a shared value through leadership messaging, recognition programs, and open dialogue.
Accountability Framework: Include consequences for faculty breaches in contracts and performance evaluations.
Call to Action
Academic integrity is a foundation of higher education. If we expect students to be honest in their work, then faculty must be held to the same (if not higher) standards. Universities and colleges should act now to close the policy gap, embed integrity in institutional culture, and hold everyone accountable. If we want students to take integrity seriously, faculty must lead by example. Anything less is hypocrisy.
References
Eaton, S. E. (2024). Comprehensive Academic Integrity (CAI): An Ethical Framework for Educational Contexts. In S. E. Eaton (Ed.), Second Handbook of Academic Integrity (pp. 1–14). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-54144-5_194
Eaton, S. E. (2025). Think Piece: Trust as a foundation for ethics and integrity in educational contexts. Critical Studies in Teaching and Learning (CriSTaL), 13(SI2), 4–7. https://doi.org/10.14426/cristal.v13iSI2.3057
Christensen Hughes, J., & Eaton, S. E. (2022). Academic misconduct in Canadian higher education: Beyond student cheating. In S. E. Eaton & J. Christensen Hughes (Eds.), Academic integrity in Canada: An enduring and essential challenge (pp. 81–102). Springer. https://doi.org/10.1007/978-3-030-83255-1
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.
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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