Decriminalizing the Language of Academic Integrity

October 2, 2025

The first time I heard about decriminalizing the language and processes we use to address cases of plagiarism and other forms of academic misconduct; I was riveted. It was at an academic integrity conference in Richmond, Virginia and the lead presenter was Dr. James Earl Orr, who presented together with students on how a developmental and supportive approach to academic misconduct case management can help lead students towards academic success while still holding them responsible for their behaviour.  James Earl Orr, writing together with Karita Orr, published an excellent article on using restorative practices to resolve academic integrity violations.

When I was writing the University of Calgary’s academic integrity Handbook for Academic Staff and Teaching Assistants, I took the opportunity to apply what I had learned from listening to Dr. Orr at conferences and reading his work by including a section on how to decriminalize the language we use to talk about academic misconduct.

Academic integrity violations are rarely criminal in nature and yet, much of the language we use when addressing plagiarism and academic cheating is legalistic, setting the stage for criminalizing student behaviour. One step towards taking a more learner-centred approach to misconduct is to decriminalize the language we use to talk about breaches of academic integrity.

Front cover: Student Academic Integrity Faculty Handbook
Front cover of the Student Academic Integrity Faculty Handbook, published by the Taylor Institute for Teaching and Learning at the University of Calgary

The following is an excerpt from the University of Calgary’s academic integrity Handbook for Academic Staff and Teaching Assistants that provides practical guidance on how to do this:

“We know that words matter and the language we use is important. A full list of terms related to academic misconduct is available in our policy. It should be noted that the terms “academic integrity” and “academic misconduct” are not interchangeable.

Academic integrity is about acting ethically in teaching, learning and research contexts. We do not report, investigate or manage cases of academic integrity. We report, investigate and manage cases of academic misconduct.

Academic misconduct is what happens when individuals do not act with integrity. This is currently the language used in our policy and procedures. When speaking and writing about academic misconduct, we can use the terms “breaches of integrity or “violations of integrity” as synonyms for academic misconduct.

At the University of Calgary we take a proactive approach to academic integrity, including in the language we use and in keeping the focus on teaching, learning and fairness to students. In our conversations with students and others, it can be helpful to use the language of integrity that focuses on education and support” (Eaton, 2022, p. 13).

See the table below, which is also an expert from our handbook (with a few minor updates):

The language of academic integrity

Preferred
language
Language
to avoid 
Explanation
Hold responsible Guilt
Guilty

The words “guilt” and “guilty” do not appear anywhere in our
polices or procedures. We do not find students guilty of academic misconduct, but instead we hold them responsible for their
behaviours.
Sanctions
Consequence
Outcome
Punish
Punishment

When disciplinary actions are taken in response to academic
misconduct, we do not use the terms “punish” or “punishment”
in our institutional documents. We opt instead for “sanctions”,
“discipline,” “consequences” or “outcome” which can include educational responses depending on the misconduct.
Hearing Trial 
The University of Calgary does not conduct trials related to
academic misconduct.
In other countries, various forms of academic misconduct can be
considered an offense under the criminal code and students may
be required to attend a criminal trial. That is not the case at the
University of Calgary or anywhere in Canada.
In the case of an appeal, a hearing might occur. In rare cases, an appeal case might escalate to an externally reviewed case in court, but these proceedings are not administered by the university itself.

When I talk about taking a postplagiarism approach to academic integrity I am talking about disrupting historically adversarial and antagonistic approaches to misconduct that pit students against their teachers. It is time to move past crime-and-punishment approaches to student misconduct where students are the villains and teachers are the heroes. When we talk about postplagiarism we talk about social justice and student success as being intertwined, and we focus on students as stewards of the future, who will be best equipped for an increasingly complex world when they understand the importance of ethical decision-making, both in theory and in practice.

Postplagiarism does not mean anything goes, and nor does it mean that we turn a blind eye to misconduct. Postplagiarism is about finding socially just ways to address misconduct include relationally, restoration, and the preservation of dignity and human rights. When we decriminalize language related to student misconduct, we are taking a step towards dignity and   student success.

Our University of Calgary’s academic integrity Handbook for Academic Staff and Teaching Assistants is an open access handbook with a Creative Commons license. This means you can share and adapt the material, providing the original work is properly attributed.

If this is helpful to you, please share this with others.

References and Further Reading

Eaton, S. E. (2022). Student Academic Integrity: A Handbook for Academic Staff and Teaching Assistants. University of Calgary, Taylor Institute for Teaching and Learning Guide Series. https://taylorinstitute.ucalgary.ca/resources/student-academic-integrity-handbook

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. (2025). Global Trends in Education: Artificial Intelligence, Postplagiarism, and Future-focused Learning for 2025 and Beyond – 2024–2025 Werklund Distinguished Research Lecture. International Journal for Educational Integrity, 21(1), 12. https://doi.org/10.1007/s40979-025-00187-6

Orr, J. E., & Hall, J. (2018). Student-led case adjudication: Promoting student learning through peer-to-peer engagement. 25th Annual International Center for Academic Integrity (ICAI) Conference, Richmond, VA.

Orr, J. E., & Orr, K. (2023). Restoring honor and integrity through integrating restorative practices in academic integrity with student leaders. Journal of Academic Ethics, 21, 55–70. https://doi.org/10.1007/s10805-021-09437-x

Orr, J. E., & Orren, S. (2018, March 4). The Development & Implementation of a Campus Academic Integrity Education Program. 25th Annual International Center for Academic Integrity (ICAI) Conference, Richmond, VA.

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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.


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/


How Not to Respond: 5 Mistakes Professors Make After Misconduct Rulings

May 28, 2025

Academic misconduct cases can leave professors feeling frustrated, especially when outcomes don’t align with their expectations. These emotions are understandable and how faculty respond to disappointing rulings can impact their professional standing; relationships with colleagues and students; and future effectiveness in addressing misconduct.

Here are five common mistakes professors make when they disagree with academic misconduct decisions—and better approaches to consider.

1. Venting to Students About the Decision

The Mistake: Discussing the case details or expressing frustration about the ruling with other students, either in class or informal settings.

Why It Backfires: This behavior undermines institutional authority, creates an uncomfortable environment for students, and may violate confidentiality requirements. Students lose confidence in the system and may question whether they’ll receive fair treatment.

Better Approach: Process your concerns through appropriate channels. If you need to discuss the case, speak with department chairs, ombudspersons, or trusted colleagues who understand confidentiality requirements.

2. Making Public Complaints on Social Media or Forums

The Mistake: Posting about the case on social media, academic forums, or other public platforms, even when avoiding specific names.

Why It Backfires: Public complaints damage professional relationships and institutional reputation. Even anonymous posts can often be traced back to their authors. This approach also models poor conflict resolution for students and colleagues.

Better Approach: Use internal grievance procedures or professional development opportunities to address systemic concerns. Focus energy on improving processes rather than criticizing past decisions.

3. Refusing to Participate in Future Misconduct Proceedings

The Mistake: Declining to serve on academic integrity committees or refusing to report suspected misconduct because of disagreement with previous outcomes.

Why It Backfires: Withdrawal from the process eliminates your voice from future decisions and reduces the system’s effectiveness. This stance also shifts additional burden to colleagues who continue participating.

Better Approach: Stay engaged while working to improve the system. Use your experience to advocate for clearer guidelines, better training, or procedural improvements that address your concerns.

4. Treating the Student Differently in Future Interactions

The Mistake: Allowing disappointment about the ruling to affect how you interact with the student in subsequent courses, recommendations, or professional settings.

Why It Backfires: This behavior constitutes unprofessional conduct and potential retaliation. It undermines the educational mission and creates legal risks for both you and the institution.

Better Approach: Maintain professional boundaries and treat all students equitably. If you find it difficult to interact objectively with the student, consider recusing yourself from situations where bias might affect your judgment.

5. Bypassing Established Processes

The Mistake: Going directly to senior administrators, board members, or external parties without following institutional procedures for investigations, appeals, or grievances.

Why It Backfires: Skipping proper channels damages relationships with immediate supervisors and colleagues. It also reduces the likelihood that your concerns will receive serious consideration, as decision-makers prefer to see that established processes were followed.

Better Approach: Work through designated channels first. Document your concerns clearly and present them through official appeal mechanisms. If these prove insufficient, seek guidance from faculty governance bodies or professional organizations.

Moving Forward Constructively

Disagreement with academic misconduct decisions stems from genuine concern for educational standards and fairness. Channel this concern into productive action by focusing on prevention, process improvement, and professional development rather than relitigating past cases.

Consider these constructive alternatives: participate in policy review committees, mentor colleagues on documentation practices, advocate for faculty training on academic integrity, or contribute to scholarship on effective misconduct prevention.

The goal is not to eliminate disagreement with misconduct decisions—different perspectives strengthen academic integrity systems. The goal is to express disagreement in ways that improve outcomes for everyone involved while maintaining the professional standards that serve our educational mission.

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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.


UHaveIntegrity: A Strengths-Based Approach to Academic Integrity at the University of Calgary

May 9, 2025
AltText: The image shows a closed laptop with a honeycomb-patterned cover on a wooden surface. On top of the laptop, there is a rectangular sticker that reads "#UHaveIntegrity" with the "integrity" part in red text. The sticker also includes a small logo for the University of Calgary.

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

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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/

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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.