Postplagiarism: Understanding the Difference Between Referencing and Giving Attribution

September 5, 2025

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:

Postplagiarism: Understanding the Difference Between Referencing and Giving Attribution – https://postplagiarism.com/2025/09/05/postplagiarism-understanding-the-difference-between-referencing-and-giving-attribution/


Latest IJEI article is out! “Exploring the nexus of academic integrity and artificial intelligence in higher education: a bibliometric analysis” 

August 29, 2025

One of the great joys of being a journal editor is getting to share good news when a new article is published. I am going to make more of an effort to do this on my blog because the International Journal for Educational Integrity is a high quality (Q1) journal with lots to offer when it comes to academic integrity. We accept only about 10% of manuscripts submitted to the journal, so having an article published is a great achievment!

Check out the latest article, “Exploring the nexus of academic integrity and artificial intelligence in higher education: a bibliometric analysis” by Daniela Avello and Samuel Aranguren Zurita.

The image shows a webpage from the International Journal for Educational Integrity, part of Springer Nature. The header includes navigation links for Home, About, Articles, and Submission Guidelines, along with a "Submit manuscript" button. The featured article is titled "Exploring the nexus of academic integrity and artificial intelligence in higher education: a bibliometric analysis" by Daniela Avello and Samuel Aranguren Zurita. It is marked as open access, published on 29 August 2025, and appears in volume 21, article number 24. Citation options are available at the bottom.

Abstract

Background

Artificial intelligence has created new opportunities in higher education, enhancing teaching and learning methods for both students and educators. However, it has also posed challenges to academic integrity.

Objective

To describe the evolution of scientific production on academic integrity and artificial intelligence in higher education.

Methodology

A bibliometric analysis was carried out using VOSviewer software and the Bibliometrix package in R. A total of 467 documents published between 2017 and 2025, retrieved from the Web of Science database, were analyzed.

Results

The analysis reveals a rapid expansion of the field, with an annual growth rate of 71.97%, concentrated in journals specializing in education, academic ethics, and technology. The field has evolved from a focus on the use of artificial intelligence in dishonest practices to the study of its integration in higher education. Four main lines of research were identified: the impact and adoption of artificial intelligence, implications for students, academic dishonesty, and associated psychological factors.

Conclusions

The field is at an early stage of development but is expanding rapidly, albeit with fragmented evolution, limited collaboration between research teams, and high editorial dispersion. The analysis shows a predominance of descriptive approaches, leaving room for the development of theoretical frameworks.

Originality or value

This study provides an overview and updated of the evolution of research on artificial intelligence and academic integrity, identifying trends, collaborations, and conceptual gaps. It highlights the need to promote theoretical reflection to guide future practice and research on the ethical use of artificial intelligence in higher education.

Check out the full article here.

________________________

Share this post: Latest IJEI article is out! “Exploring the nexus of academic integrity and artificial intelligence in higher education: a bibliometric analysis” – https://drsaraheaton.com/2025/08/29/latest-ijei-article-is-out-exploring-the-nexus-of-academic-integrity-and-artificial-intelligence-in-higher-education-a-bibliometric-analysis/

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.


Strategies to Support First Generation College Students with Academic Integrity

August 27, 2025

I started my Bachelor of Arts (Honours) degree 37 years ago this September. (Gosh, that’s a long time ago!) Looking back, I was sooo excited to be a university student… and also terrified. I was the first person in my immediate family to attend university. Now, as a professor, I am committed to helping to create pathways to success for students from equity-deserving groups, those who may be from marginalized or underrepresented groups. Part of this includes academic integrity for these students, who may genuinely have no idea when they start their first year of college what is expected of them.

First-generation college students (also called “first in family” students) are those whose parents did not complete a four-year college degree. These students face unique challenges that some folks in higher education may not fully appreciate. As faculty members, we have both an opportunity and responsibility to create more inclusive and supportive learning environments for these students.

An AI-generated image of A group of students is sitting together in a hallway, engaged in a study session. They are holding notebooks and textbooks, discussing and sharing information. The background shows a bright corridor with natural light coming through large windows.

Understanding the Barriers

First-generation students often navigate college without the cultural capital and informal knowledge that their peers may take for granted. They may be unfamiliar with academic norms, unsure about when and how to seek help, or struggling to balance college demands with family obligations and work responsibilities. These students may also experience heightened anxiety about whether they belong in academic spaces.

The principles I have advocated for in academic integrity work apply directly here. Existing systems in higher education can create barriers for students who don’t arrive with certain forms of privilege. As I have argued elsewhere, there can be no integrity without equity (Eaton, 2022). When we fail to address systemic barriers, we perpetuate conditions that disadvantage particular student groups.

Practical Strategies for Faculty

These strategies may work for many different student, not just first gen ones, but I would argue that we can be especially attentive to first generation students by taking the following into account:

Make the Implicit Explicit

Academic culture is filled with unspoken rules and expectations. What seems obvious to those of us who have spent years in higher education may be completely foreign to first-generation students. As I have learned from my work on equity and academic integrity, “if the system is invisible to you, that is because it was created for you” (Eaton, 2022, p. 6).

Provide detailed rubrics and examples of successful work: Create comprehensive rubrics that clearly articulate expectations for each level of performance. Include examples of student work that demonstrate different quality levels, with annotations explaining what makes each example effective or ineffective. Consider providing both excellent examples and common mistakes to help students understand the full range of expectations.

Explain the purpose behind assignments, not just the requirements: Help students understand the learning objectives and real-world applications of their coursework. For instance, explain how a research paper develops critical thinking skills, information literacy, and written communication abilities that transfer to professional contexts. This contextual understanding helps students engage more meaningfully with their learning.

Use plain language in syllabi and course materials: Avoid unnecessary jargon and academic terminology that may be unfamiliar to first-generation students. When discipline-specific terms are essential, define them clearly. Review your syllabus annually to identify language that might be confusing or intimidating to newcomers to higher education.

Clarify expectations for participation, email communication, and office hour visits: Explicitly teach students how to write professional emails, including appropriate subject lines, greetings, and tone. Explain what constitutes meaningful class participation beyond simply speaking up. Describe what happens during office hours and provide specific examples of productive topics for discussion.

Build Genuine Relationships

Connection matters. First-generation students benefit tremendously from feeling that faculty care about them as individuals. This mirrors what we know from academic integrity research: students are less likely to engage in misconduct when they believe their instructors care about them (Eaton, 2022).

Learn students’ names and use them regularly: Make a conscious effort to learn and use student names from the first week of class. Consider using name tents, seating charts, or other strategies to help with this process. Using names creates a sense of belonging and demonstrates that you see students as individuals rather than anonymous faces in a crowd.

Share your own educational journey when appropriate: If you were a first-generation student yourself, consider sharing relevant aspects of your experience. Even if you weren’t, you can share challenges you faced and how you overcame them. This vulnerability helps normalize struggle and shows students that difficulty doesn’t indicate inadequacy.

Create opportunities for peer interaction and collaboration: Design activities that help students connect with one another, such as think-pair-share exercises, small group discussions, or collaborative projects. These connections can provide crucial academic and social support throughout their college experience.

Be approachable and normalize help-seeking behavior: Explicitly tell students that asking questions is a sign of engagement, not weakness. Share examples of productive questions from past students. Make yourself available through multiple channels and respond to student inquiries promptly and warmly.

Schedule regular check-ins, particularly with students who seem to be struggling: Proactively reach out to students who have missed classes, submitted late work, or seem disengaged. A simple email expressing concern and offering support can make a significant difference. Consider mid-semester individual conferences with all students to discuss their progress and address any concerns.

Address Financial and Time Pressures

Many first-generation students work multiple jobs or have family caregiving responsibilities. Our course design should acknowledge these realities without compromising academic rigor. In some research that I did with a graduate student on mental wellbeing and academic integrity, we found that students experiencing stress may be more vulnerable to academic misconduct (Eaton & Turner, 2020), making supportive course design even more crucial.

Avoid requiring expensive textbooks when alternatives exist: Explore open educational resources (OERs), library reserves, or older editions of textbooks. If expensive materials are necessary, provide information about rental options, used book sources, or financial aid resources that might help students afford them.

Consider the timing of assignment due dates and major exams: Avoid scheduling major assignments during times when students are likely to face additional stressors, such as midterms week or right before holidays when many students increase their work hours. Provide advance notice of all major assignments and deadlines.

Offer multiple pathways to demonstrate learning: Design assessments that allow students to showcase their knowledge in different ways. This might include options for oral presentations instead of written papers, creative projects alongside traditional exams, or multiple smaller assignments rather than a few high-stakes evaluations.

Build flexibility into attendance policies when appropriate: While maintaining reasonable expectations, consider policies that account for the realities of students who may face transportation issues, work conflicts, or family emergencies. Provide clear guidelines about how to communicate absences and make up missed work.

Connect students with campus resources for emergency financial assistance: Learn about your institution’s emergency funding programs and don’t hesitate to refer students who are struggling financially. Many students are unaware these resources exist or feel uncomfortable accessing them without encouragement from faculty.

Provide Proactive Academic Support

Don’t wait for students to ask for help. The cultural norm of self-advocacy may not be familiar to first-generation students, and they may interpret struggling as evidence that they don’t belong. As research on academic integrity and mental health during COVID-19 has shown, students may experience significant anxiety about academic expectations and performance (Eaton & Turner, 2020).

Introduce campus support services early and repeatedly: Don’t just mention writing support and student success workshops once in your syllabus. Regularly remind students about these resources and explain specifically how they can help with course assignments. Consider inviting representatives from student services to visit your class.

Provide feedback throughout the semester, not just at the end: Offer low-stakes opportunities for students to receive feedback on their work before major assignments are due. This might include draft submissions, peer review sessions, or brief conferences about work in progress.

Connect students with tutoring, writing centers, and peer support programs: Make specific referrals rather than general suggestions. For example, “Based on your draft, I think working with the writing center on thesis development would be helpful. Here’s how to make an appointment, and I recommend mentioning that you’re working on argument structure.”

Offer study strategies and time management guidance: Many first-generation students have never been taught effective study techniques. Provide concrete strategies for reading academic texts, taking notes, preparing for exams, and managing large projects over time.

Explain how to read and interpret feedback on assignments: Students may not understand how to use your comments to improve their work. Consider providing examples of how to revise based on feedback or scheduling brief meetings to discuss your comments on major assignments.

Challenge Deficit Thinking

Resist viewing first-generation students through a deficit lens. Instead, recognize the strengths, resilience, and diverse perspectives they bring to your classroom. This aligns with advocacy for decolonizing academic practices and embracing multiple ways of knowing (Eaton, 2022).

Value different forms of knowledge and experience: Acknowledge that students bring valuable perspectives from their work, family, and community experiences. Create opportunities for students to connect course content to their lived experiences and cultural backgrounds.

Incorporate diverse voices and perspectives in your curriculum: Include authors, researchers, and case studies that reflect diverse backgrounds and experiences. This helps all students see themselves reflected in the curriculum while exposing everyone to broader perspectives.

Create assignments that allow students to draw on their backgrounds: Design projects that invite students to explore topics relevant to their communities or to apply course concepts to contexts they know well. This validates their experiences while helping them see the relevance of academic content.

Resist conflating struggle with inability: Normalize the learning process and help students understand that confusion and difficulty are natural parts of intellectual growth. Share examples of how struggle leads to deeper understanding.

Advocate for institutional changes that support student success: Use your voice in departmental and institutional committees to push for policies and practices that better serve first-generation students. This might include advocating for more flexible scheduling, expanded financial aid, or improved support services.

Practice Cultural Humility

Acknowledge that our own educational experiences may differ significantly from those of our students. Be willing to learn about their perspectives and challenges.

Ask students about their needs rather than making assumptions: Use anonymous surveys or informal check-ins to understand what your students are experiencing. Their insights can help you adjust your teaching to better meet their needs.

Be open to feedback about your teaching practices: Create opportunities for students to provide honest feedback about what’s working and what isn’t. Consider mid-semester evaluations or regular pulse checks to gauge student understanding and engagement.

Recognize the limits of your own knowledge and experience: Be honest about what you don’t know about first-generation student experiences and commit to learning more. Attend professional development sessions, read relevant research, and seek out colleagues who have expertise in this area.

Collaborate with student affairs professionals who specialize in first-generation student support: Build relationships with staff in student success centers, counseling services, and first-generation student programs. They can provide valuable insights and serve as resources for your students.

Beyond Individual Action

Individual faculty efforts are essential, but systemic change is equally important. As I have argued in my work on equity in academic integrity, we must advocate for institutional transformation and systemic change (Eaton, 2022).

Within your department and institution, push for professional development focused on supporting first-generation students, policies that address food insecurity and housing instability, expanded financial aid and emergency funding programs, mentoring programs connecting students with faculty, staff, and successful peers, and recognition systems that value inclusive teaching practices.

The Mental Health Connection

There are connections between academic stress and mental health concerns (Eaton & Turner, 2020). For first-generation students, this stress may be compounded by family pressures, financial worries, and feelings of not belonging. Our rapid review of literature during COVID-19 found that students experienced “amplification of students’ anxiety and stress during the pandemic, especially for matters relating to academic integrity” (Eaton & Turner, 2020, p. 37).

Faculty should be attentive to signs of student distress and prepared to connect students with appropriate campus resources. Creating supportive classroom environments can help mitigate some of these stressors. When students feel valued and supported, they are more likely to seek help when needed rather than struggling in isolation.

A Personal Commitment

Supporting first-generation students requires ongoing commitment to equity and inclusion. It means examining our own practices and assumptions, being willing to change course when needed, and advocating for students both inside and outside our classrooms.

The work of creating equitable educational environments is never finished. As I have written elsewhere, “A commitment to allyship is a life’s work, demonstrated throughout our daily ethical practice as educators, leaders, researchers, and human beings” (Eaton, 2022, p. 6). When we commit to supporting first-generation students, we strengthen our entire academic community and move closer to the ideals of fairness and inclusion that should guide higher education.

As educators, we have the power to significantly impact student success. The question is how we use that power to dismantle existing barriers or to create pathways for all students to thrive. 

References

Eaton, S. E. (2022). New priorities for academic integrity: equity, diversity, inclusion, decolonization and Indigenization. International Journal for Educational Integrity, 18(10), 1-12. https://doi.org/10.1007/s40979-022-00105-0

Eaton, S. E., & Turner, K. L. (2020). Exploring academic integrity and mental health during COVID-19: Rapid review. Journal of Contemporary Education Theory & Research, 4(1), 35-41. http://doi.org/10.5281/zenodo.4256825

________________________

Share this post: Strategies to Support First Generation College Students with Academic Integrity https://drsaraheaton.com/2025/08/27/strategies-to-support-first-generation-college-students-with-academic-integrity/

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 Good Ideas Meet Poor Execution: The Humane AI Pin and the Future of Language Translation

May 18, 2025

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.)

A person wearing a light blue dress shirt and a dark blue suit jacket. The shirt has a button labeled 'A7' on the collar. Attached to the collar is a small, square electronic device with a screen displaying an icon of a circular arrow, indicating a loading or refresh symbol. The background features an out-of-focus world map.

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.

Live long and prosper.

References

Chokkattu, J. (2024, April 11). Review: Humane Ai Pin. https://www.wired.com/review/humane-ai-pin/

Chokkattu, J. (2025a, February 22). The Humane Ai Pin Will Become E-Waste Next Week. Wired. https://www.wired.com/story/humane-ai-pin-will-become-e-waste-next-week/

Chokkattu, J. (2025b, February 28). What to Do With Your Defunct Humane Ai Pin. Wired. https://www.wired.com/story/what-to-do-with-your-humane-ai-pin/

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 

Pierce, D. (2023, November 9). Humane officially launches the AI Pin, its OpenAI-powered wearable. The Verge. https://www.theverge.com/2023/11/9/23953901/humane-ai-pin-launch-date-price-openai 

Note: This is a re-post of a piece originally posted on the Postplagiarism blog.

________________________

Share this post: When Good Ideas Meet Poor Execution: The Humane AI Pin and the Future of Language Translation – https://drsaraheaton.com/2025/05/18/when-good-ideas-meet-poor-execution-the-humane-ai-pin-and-the-future-of-language-translation/

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.


Teaching Fact-Checking Through Deliberate Errors: An Essential AI Literacy Skill

April 23, 2025

Abstract

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:

Introduction

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:

  • Pattern matching versus genuine understanding
  • How training data limitations affect AI outputs
  • The concept of AI ‘hallucination’ and why it occurs
  • 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

Edwards, B. (2023, April 6). Why ChatGPT and Bing Chat are so good at making things up. Arts Technica. https://arstechnica.com/information-technology/2023/04/why-ai-chatbots-are-the-ultimate-bs-machines-and-how-people-hope-to-fix-them/

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

Disclaimer: This content is crossposted from: https://postplagiarism.com/2025/04/23/teaching-fact-checking-through-deliberate-errors-an-essential-ai-literacy-skill/

________________________

Share this post: Teaching Fact-Checking Through Deliberate Errors: An Essential AI Literacy Skill – https://drsaraheaton.com/2025/04/23/teaching-fact-checking-through-deliberate-errors-an-essential-ai-literacy-skill/

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.