I am trying out SSRN. I feel like this is something I should have known about long ago. Last year, one of the doctoral students whom I supervise, Myke Healy, posted a paper about academic integrity in secondary schools on SSRN. (It’s a really good ready, by the way.)
On my recent travels, I was listening to Malcolm Gladwell’s podcast, Revisionist History. In one of episodes (I forget which one exactly), Gladwell raves about SSRN. I mean, gushes.
I thought to myself, “Well, it seems the universe is asking me to pay attention to SSRN.” So, I did.
I got working on a paper that had been sort of lingering for a couple of years. (Yes, a couple of years. Good work takes time!) I unpacked the ideas, developed the argument, referenced people whose contributions influenced and shaped my thinking and got it formatted.
So, I’ve now posted my first paper on SSRN: Eaton, S. E. (2025). A Wraparound Approach to Academic Integrity: Centering Students in the Postplagiarism Era (April 20, 2025). SSRN. https://doi.org/http://dx.doi.org/10.2139/ssrn.5223911
I’m not really sure what happens next. There doesn’t really seem to be a place for folks to comment on the paper, though you can download it and add it to your library. I guess the next step is to submit it to a journal and go from there.
If you use SSRN and have tips on how to make the most of it, feel free to share. I’m learning as I go and I’m all ears.
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.
Artificial intelligence (AI) has moved from a futuristic concept to an everyday reality. Rather than viewing AI tools like ChatGPT as threats to academic integrity, forward-thinking educators are discovering their potential as powerful teaching instruments. Here’s how you can meaningfully incorporate AI into your classroom while promoting critical thinking and ethical technology use.
Making AI Visible in the Learning Process
One of the most effective approaches to teaching with AI is to bring it into the open. When we demystify these tools, students develop a more nuanced understanding of the tools’ capabilities and limitations.
Start by dedicating class time to explore AI tools together. You might begin with a demonstration of how ChatGPT or similar tools respond to different types of prompts. Ask students to compare the quality of responses when the tool is asked to:
Summarize factual information
Analyze a complex concept
Solve a problem in your discipline
Have students identify where the AI excels and where it falls short. Hands-on experience that is supervised by an educator helps students understand that while AI can be impressive and capable, it has clear boundaries and weaknesses.
From AI Drafts to Critical Analysis
AI tools can quickly generate content that serves as a starting point for deeper learning. Here is a step-by-step approach for using AI-generated drafts as teaching material:
Assignment Preparation: Choose a topic relevant to your course and generate a draft response using an AI tool such as ChatGPT.
Collaborative Analysis: Share the AI-generated draft with students and facilitate a discussion about its strengths and weaknesses. Prompt students with questions such as:
What perspectives are missing from this response?
How could the structure be improved?
What claims require additional evidence?
How might we make this content more engaging or relevant?
The idea is to bring students into conversations about AI, to build their critical thinking and also have them puzzle through the strengths and weaknesses of current AI tools.
Revision Workshop: Have students work individually or in groups to revised an AI draft into a more nuanced, complete response. This process teaches students that the value lies not in generating initial content (which AI can do) but in refining, expanding, and critically evaluating information (which requires human judgment).
Reflection: Ask students to document what they learned through the revision process. What gaps did they identify in the AI’s understanding? How did their human perspective enhance the work? Building in meta-cognitive awareness is one of the skills that assessment experts such as Bearman and Luckin (2020) emphasize in their work.
This approach shifts the educational focus from content creation to content evaluation and refinement—skills that will remain valuable regardless of technological advancement.
Teaching Fact-Checking Through Deliberate Errors
AI systems often present information confidently, even when that information is incorrect or fabricated. This characteristic makes AI-generated content perfect for teaching fact-checking skills.
Try this classroom activity:
Generate Content with Errors: Use an AI tool to create content in your subject area, either by requesting information you know contains errors or by asking about obscure topics where the AI might fabricate details.
Fact-Finding Mission: Provide this content to students with the explicit instruction to identify potential errors and verify information. You might structure this as:
Individual verification of specific claims
Small group investigation with different sections assigned to each group
A whole-class collaborative fact-checking document
Source Evaluation: Have students document not just whether information is correct, but how they determined its accuracy. This reinforces the importance of consulting authoritative sources and cross-referencing information.
Meta-Discussion: Use this opportunity to discuss why AI systems make these kinds of errors. Topics might include:
The difference between pattern recognition and understanding
Why AI might present incorrect information with high confidence
These activities teach students not just to be skeptical of AI outputs but to develop systematic approaches to information verification—an essential skill in our information-saturated world.
Case Studies in AI Ethics
Ethical considerations around AI use should be explicit rather than implicit in education. Develop case studies that prompt students to engage with real ethical dilemmas:
Attribution Discussions: Present scenarios where students must decide how to properly attribute AI contributions to their work. For example, if an AI helps to brainstorm ideas or provides an outline that a student substantially revises, how could this be acknowledged?
Equity Considerations: Explore cases highlighting AI’s accessibility implications. Who benefits from these tools? Who might be disadvantaged? How might different cultural perspectives be underrepresented in AI outputs?
Professional Standards: Discuss how different fields are developing guidelines for AI use. Medical students might examine how AI diagnostic tools should be used alongside human expertise, while creative writing students could debate the role of AI in authorship.
Decision-Making Frameworks: Help students develop personal guidelines for when and how to use AI tools. What types of tasks might benefit from AI assistance? Where is independent human work essential?
These discussions help students develop thoughtful approaches to technology use that will serve them well beyond the classroom.
Implementation Tips for Educators
As you incorporate these approaches into your teaching, consider these practical suggestions:
Start small with one AI-focused activity before expanding to broader integration
Be transparent with students about your own learning curve with these technologies
Update your syllabus to clearly outline expectations for appropriate AI use
Document successes and challenges to refine your approach over time
Share experiences with colleagues to build institutional knowledge
Moving Beyond the AI Panic
The concept of postplagiarism does not mean abandoning academic integrity—rather, it calls for reimagining how we teach integrity in a technologically integrated world. By bringing AI tools directly into our teaching practices, we help students develop the critical thinking, evaluation skills, and ethical awareness needed to use these technologies responsibly.
When we shift our focus from preventing AI use to teaching with and about AI, we prepare students not just for academic success, but for thoughtful engagement with technology throughout their lives and careers.
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
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
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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.
How lovely to wake up this morning and see an article about postplagiarism in Times Higher Education. Karen Kenny from Exeter University writes about the six tenets and then extends the discussion to assessment.
It seems that the idea of postplagiarism is catching on. Dr. Rahul Kumar and I have launched a postplagiarism online community, where we share blogs, news, articles, and translations of the work into other languages. You can check out all our resources over on our other site.
This blog has had over 3.7 million views thanks to readers like you. If you enjoyed this post, please ‘Like’ it using the button below or share it on social media. Thanks!
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.
Generative AI (GenAI) is transforming teaching, learning, and assessment in higher education.
Learn to integrate GenAI effectively while maintaining academic integrity and enhancing student agency.
Dr. Sarah Eaton shares innovative strategies that promote critical thinking and original scholarship. Explore how GenAI reshapes academic practices and discover proactive approaches to leverage its potential.
This session equips educators, administrators, and policymakers to lead purposefully in a dynamic academic landscape.
Speaker bio
Sarah Elaine Eaton is a Professor and research chair at the Werklund School of Education at the University of Calgary (Canada). She is an award-winning educator, researcher, and leader. She leads transdisciplinary research teams focused on the ethical implications of advanced technology use in educational contexts. Dr. Eaton also holds a concurrent appointment as an Honorary Associate Professor, Deakin University, Australia.
More Details
Date: January 29, 2025
Time: 12:00 – 13:00 Mountain time
This talk is free and open to the public, but there are only 20 seats available to join us in person! We can also accommodate folks online.
This blog has had over 3.7 million views thanks to readers like you. If you enjoyed this post, please “like” it or share it on social media. Thanks!
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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