Academic Integrity includes and extends beyond student conduct. Thanks to Bibek Dahal, MPhil who recently spotted this infographic that I created being used without attribution (in a social media post about ethics and integrity no less!)
I offer many of my materials as Open Access (OA) or Open Educational Resources (OER) and with a Creative license. This doesn’t mean that they should be appropriated and presented as anyone else’s original work, but instead that they can be used without paying any licensing fees or royalties. It also means that citing and referencing the original work is appropriate.
Here’s a citation for this infographic, which anyone is free to use with attribution. This infographic also appears in the introductory chapter of the Second Handbook of Academic Integrity (Springer, 2024):
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.
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.
The landscape of academic integrity continues to evolve. Don’t get me wrong. There are timeless aspects to academic integrity that remain constant, like everyone in the educational eco-system following established expectations that are clearly communicated and supported.
Having said that, our world has changed a lot since COVID-19. Digital learning is pretty much embedded into the educational systems of every high-income county and many others, too.
Our approach to plagiarism and academic misconduct must evolve with new developments in technology. The traditional model—focused on catching and punishing—has reached its limits. With a post-plagiarism framework we can prepare students for their future while honouring their dignity.
Moving Beyond Detection and Punishment
The plagiarism detection industry grew from legitimate concerns about academic misconduct. However, this approach positions students as potential cheaters rather than emerging scholars. Detection software creates an atmosphere of suspicion rather than trust. Students submit work feeling anxious about false positives rather than proud of their learning.
Universities spend millions (billions?) on detection services annually. These resources could support student learning instead. What if we redirected these funds toward writing centers, tutoring programs, and faculty development?
Students as Partners in Academic Integrity
A post-plagiarism approach positions students as partners. They help develop academic integrity policies. They contribute to classroom discussions about citation practices. They mentor peers in proper source use.
Student partnership requires trust. Faculty must believe students want to succeed honestly. Students must trust faculty to guide rather than police. This mutual trust creates space for authentic learning.
Students who participate in policy development understand expectations better. They develop ownership of academic integrity standards. These experiences prepare them for professional environments where ethical conduct matters.
Preserving Dignity in Digital Learning
Technology changes how we learn and create knowledge. AI writing tools now generate sophisticated text. Students need skills to use these tools ethically.
A post-plagiarism approach acknowledges this reality. Rather than banning technology, we teach students to use it responsibly. We help them understand when AI assistance is appropriate and when independent work matters.
Preserving dignity means treating students as capable decision-makers. They need practice making ethical choices about technology use. Our guidance should focus on developing judgment rather than following rules.
Preparing Students for Tomorrow’s Challenges
Today’s students will work in environments transformed by automation and AI. Their value will come from distinctly human capabilities—critical thinking, creativity, collaboration, and ethical reasoning.
Citation skills matter less than attribution. Students need to evaluate sources critically, synthesize diverse perspectives, and contribute original insights. A post-plagiarism framework prioritizes these higher-order skills.
Assessment methods can evolve accordingly. Assignments that ask students to demonstrate their thinking process resist plagiarism naturally. Projects requiring personal reflection or real-world application showcase authentic learning.
A Blueprint for Change
Practical steps toward a post-plagiarism future include:
Redesign assessments to emphasize process over product
Involve students in academic integrity policy development
Teach technology literacy alongside information literacy
Invest in support systems rather than detection systems
Create classroom cultures that value original thinking
This blueprint requires institutional commitment. Faculty need professional development opportunities. Administrators need courage to question established practices. Students need meaningful involvement in governance.
Conclusion
A post-plagiarism framework offers hope. It acknowledges technological reality while preserving educational values. It treats students as partners rather than suspects. It prepares graduates who understand integrity as professional responsibility rather than compliance obligation.
The future of education requires this shift. Our students deserve learning environments that honor their dignity, nurture their capabilities, and prepare them for tomorrow’s challenges. By moving beyond plagiarism detection toward partnership, we create educational experiences worthy of their potential.
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 teaching resource explores an innovative pedagogical approach for developing AI literacy in a postplagiarism era. The document outlines a method of teaching fact-checking skills by having students critically evaluate AI-generated content containing deliberate errors. It provides practical guidance for educators on creating content with strategic inaccuracies, structuring verification activities, teaching source evaluation through a 5-step process, understanding AI error patterns, and implementing these exercises throughout courses. By engaging students in systematic verification processes, this approach helps develop metacognitive awareness, evaluative judgment, and appropriate skepticism when consuming AI-generated information. The resource emphasizes assessing students on their verification process rather than solely on error detection, preparing them to navigate an information landscape where distinguishing fact from fiction is increasingly challenging yet essential.
Here is a downloadable .pdf of this teaching activity:
In a postplagiarism era, one of the most valuable skills we can teach students is how to critically evaluate AI-generated content. This can help them to cultivate meta-cognition and evaluative judgement, which have been identified as important skills for feedback and evaluation (e.g., Bearman and Luckin, 2020; Tai et al., 2018). Gen AI tools present information with confidence, regardless of accuracy. This characteristic creates an ideal opportunity to develop fact-checking competencies that serve students throughout their academic and professional lives.
Creating Content with Strategic Errors
Begin by generating content through an AI tool that contains factual inaccuracies. There are several approaches to ensure errors are present:
Ask the AI about obscure topics where it lacks sufficient training data
Request information about recent events beyond its knowledge cutoff
Pose questions about specialized fields with technical terminology
Combine legitimate questions with subtle misconceptions in your prompts
For example, ask a Large Language Model (LLM), such as ChatGPT (or any similar tool) to ‘Explain the impact of the Marshall-Weaver Theory on educational psychology’. There is no such theory, at least to the best of my knowledge. I have fabricated it for the purposes of illustration. The GenAI will likely fabricate details, citations, and research.
Structured Verification Activities
Provide students with the AI-generated content and clear verification objectives. Structure the fact-checking process as a systematic investigation.
First, have students highlight specific claims that require verification. This focuses their attention on identifying testable statements versus general information.
Next, assign verification responsibilities using different models:
Individual verification where each student investigates all claims
Jigsaw approach where students verify different sections then share findings
Team-based verification where groups compete to identify the most inaccuracies
Require students to document their verification methods for each claim. This documentation could include:
Sources consulted
Search terms used
Alternative perspectives considered
Confidence level in their verification conclusion
Requiring students to document how they verified each claim can help them develop meta-cognitive awareness about their own learning and experience how GenAI’s outputs should be treated with some skepticism and gives them specific strategies to verify content for themselves.
Teaching Source Evaluation: A 5-Step Process
The fact-checking process creates a natural opportunity to reinforce source evaluation skills.
As teachers, we can guide students to follow a 5-step plan to learn how to evaluate the reliability, truthfulness, and credibility of sources.
Step 1: Distinguish between primary and secondary sources. (A conversation about how terms such as ‘primary source’ and ‘secondary source’ can mean different things in different academic disciplines could also be useful here.)
Step 2: Recognize the difference between peer-reviewed research and opinion pieces. For opinion pieces, editorials, position papers, essays, it can be useful to talk about how these different genres are regarded in different academic subject areas. For example, in the humanities, an essay can be considered an elevated form of scholarship; however, in the social sciences, it may be considered less impressive than research that involves collecting empirical data from human research participants.
Step 3: Evaluate author credentials and institutional affiliations. Of course, we want to be careful about avoiding bias when doing this. Just because an author may have an affiliation with an ivy league university, for example, that does not automatically make them a credible source. Evaluating credentials can — and should — include conversations about avoiding and mitigating bias.
Step 4: Identify publication date and relevance. Understanding the historical, social, and political context in which a piece was written can be helpful.
Step 5: Consider potential biases in information sources. Besides bias about an author’s place of employment, consider what motivations they may have. This can include a personal or political agenda, or any other kind of motive. Understanding a writer’s biases can help us evaluate the credibility of what they write.
Connect these skills to your subject area by discussing authoritative sources specific to your field. What makes a source trustworthy in history differs from chemistry or literature.
Understanding Gen AI Error Patterns
One valuable aspect of this exercise goes beyond identifying individual errors to recognizing patterns in how AI systems fail. As educators, we can facilitate discussions about:
Why AI presents speculative information as factual
How AI systems blend legitimate information with fabricated details
Connect these skills to your subject area by discussing authoritative sources specific to your field. What makes a source trustworthy in history differs from chemistry or literature.
Practical Implementation
Integrate these fact-checking exercises throughout your course rather than as a one-time activity. Start with simple verification tasks and progress to more complex scenarios. Connect fact-checking to course content by using AI-generated material related to current topics.
Assessment should focus on the verification process rather than simply identifying errors. Evaluate students on their systematic approach, source quality, and reasoning—not just error detection.
As AI-generated content becomes increasingly prevalent, fact-checking skills are an important academic literacy skill. By teaching students to approach information with appropriate skepticism and verification methods, we prepare them to navigate a postplagiarism landscape where distinguishing fact from fiction becomes both more difficult and more essential.
References
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
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
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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