Call for Proposals: Special issue on Postplagiarism and Generativism: Human-AI Hybrid Approaches to Ethical Teaching, Learning, and Assessment

March 17, 2026

Special Issue Call for Papers

Postplagiarism and Generativism: Human-AI Hybrid Approaches to Ethical Teaching, Learning, and Assessment

For publication in the Journal of University Teaching and Learning Practice

Guest editors

Background

Every new technology brings with it societal and moral panic (Orben, 2020). When the Internet first became popular, concerns about plagiarism increased. Even though there is scant empirical evidence that the Internet was actually responsible for increases in rates of plagiarism, the perception that new technology resulted in more academic cheating persisted (Panning Davies & Howard, 2016).

Some plagiarism scholars have been emphatic that the majority of student plagiarism cases are not an intent to deceive, but rather a lack of academic literacy and poor academic practice, and have even advocated for disposing of plagiarism in academic misconduct policies in favour of increased student support (Howard, 1992; Jamieson & Howard, 2021). The idea that plagiarism could be decoupled from academic misconduct seems somewhat unlikely, but by the 2020s it was obvious to some that generative artificial intelligence (GenAI) would have an impact on writing, and by extension, on plagiarism (Mindzak & Eaton, 2021).

In response to these technological shifts, various frameworks have emerged to conceptualize academic integrity in the GenAI era. The postplagiarism framework, first introduced by Eaton (2021, 2023) and since discussed by scholars worldwide (Bali, 2023; Bagenal, 2024; Kenny, 2024), offers one approach. Other perspectives, such as Generativism (Pratschke, 2023), AI Literacy frameworks (Ng et al., 2021; Pretorius & Cahusac de Caux, 2024), and UNESCO’s Guidance for Generative AI in Education (2023), provide complementary or alternative viewpoints on similar phenomena.

Postplagiarism is based on six tenets (Eaton, 2023): (1) human-AI hybrid writing will become the norm; (2) creativity can be enhanced by AI; (3) AI can help to overcome language barriers; (4) we can outsource control of our writing to AI, but we do not outsource responsibility for what is written; (5) attribution remains important; and (6) historical definitions of plagiarism may require rethinking.

Empirical testing of these and related frameworks has shown differing levels of acceptance and application across educational contexts (Kumar, 2025).

Equity, Diversity, Inclusion, and Accessibility in a Postplagiarism Age

As higher education institutions aim to promote social justice through equity, diversity, and inclusion (EDI), GenAI holds the potential to either break down or reinforce barriers related to linguistic, cultural, socioeconomic, and ability differences requires critical examination.

Assessment practices should be designed proactively to enable all students to demonstrate their learning without being unfairly disadvantaged by their personal characteristics or circumstances (Tai et al., 2022). Similarly, McDermott (2024) highlights the importance of considering accessibility, equity, and inclusion in assessment and academic integrity.

GenAI offers opportunities to enhance equity by providing personalized support, overcoming language barriers, and assisting learners with diverse needs. However, without careful implementation, it may exacerbate existing inequities through unequal access to technology, algorithmic biases, or assessment designs that privilege certain ways of knowing and communicating.

In this special edition, we propose to examine the broader question: “How are pedagogies, learning, and teaching approaches evolving in response to GenAI, and what frameworks best support ethical academic practice in a postplagiarism landscape?”

We invite researchers and practitioners to submit their original research papers exploring the transformation of teaching, learning, and assessment in a GenAI age. We welcome both theoretical and empirical contributions, including positions that may present contrasting viewpoints. Potential topics of interest include, but are not limited to:

  • New developments in postplagiarism, generativism, and other emerging frameworks for understanding academic integrity in the GenAI era
  • Empirical studies testing these frameworks in different contexts and disciplines
  • The use of these frameworks to design or reform academic misconduct policies and procedures
  • The relationship between GenAI, academic literacies, and related competencies (e.g., digital literacy, information literacy)
  • Pedagogical approaches that embrace GenAI while maintaining academic integrity
  • Case studies of successful integration of GenAI into teaching, learning, and assessment
  • Critical perspectives on the limitations or challenges of current approaches to GenAI in education
  • Position papers presenting new or alternative frameworks for understanding GenAI in teaching and learning

We particularly encourage submissions that engage in dialogue with existing frameworks, offering either supportive evidence or critical alternatives. Our goal is to foster a robust debate about the future of teaching and learning in a GenAI (and even a post-GenAI) world.

We welcome submissions from both established researchers and early-career scholars from diverse academic and cultural backgrounds. All submissions will be peer-reviewed by an international panel of experts. Accepted papers will be published in a special issue of the Journal of University Teaching and Learning Practice.

Types of publications accepted into this Special Issue

The types of publications that are eligible for acceptance into this Special Issue include:

  • Research papers
  • Review articles (e.g., systematic review or meta-analysis)
  • Case studies and evidence-based good practice examples

Developing a high-quality proposal

We recommend the creation of a single document in Word (.doc or .docx) format that contains the following:

  • Proposed article title
  • Proposed authors names, affiliations, and ORCid
  • A clear evidence-based rationale for the line of inquiry proposed
  • Research question(s)
  • Proposed method (for both theoretical and empirical manuscripts)
  • Practice-based implications of the proposed research

The word limit for the proposal is 250 words (not including references) and is designed to give the Editorial Team a sense of the rigour of the manuscript proposed and the possible implications of such research. The Editorial Team may return with an invitation to combine similar manuscripts. Acceptance of proposals does not guarantee acceptance of final manuscripts.

Timeline

  • Proposals due – April 30, 2026
  • Proposal acceptance notifications: May 14, 2026
  • Full articles due: August 31, 2026

Submit your abstract via this online form: https://forms.gle/6sKjc2jkKGWCtGgw7

For further information contact Professor Sarah Elaine Eaton, University of Calgary.

References

Bali, M. (2023, March 3). Are We Approaching a Postplagiarism Era? https://blog.mahabali.me/educational-technology-2/are-we-approaching-a-postplagiarism-era/

Bagenal, J. (2024). Generative artificial intelligence and scientific publishing: Urgent questions, difficult answers. The Lancet, 403(10432), 1118–1120. https://doi.org/10.1016/S0140-6736(24)00416-1

Eaton, S. E. (2021). Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity. Bloomsbury.

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

Orben, A. (2020). The Sisyphean cycle of technology panics. Perspectives on Psychological Science, 15(5), 1143–1157. https://doi.org/10.1177/1745691620919372

Howard, R. M. (1992). A plagiarism pentimento. Journal of Teaching Writing, 11(2), 233–245.

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Posted by Sarah Elaine Eaton, Ph.D.

What Should We Be Assessing in a World with AI? Insights from Higher Education Educators

November 25, 2025

The arrival of generative AI tools such as ChatGPT has disrupted how we think about assessment in higher education. As educators, we’re facing a critical question: What should we actually be assessing when students have access to these powerful tools?

Our recent study explored how 28 Canadian higher education educators are navigating this challenge. Through in-depth interviews, we discovered that educators are positioning themselves as “stewards of learning with integrity” – carefully drawing boundaries between acceptable and unacceptable uses of chatbots in student assessments.

Screenshot of an academic journal article header from Assessment & Evaluation in Higher Education, published by Routledge. The article title reads: “What should we be assessing exactly? Higher education staff narratives on gen AI integration of assessment in a postplagiarism era.” Authors listed are Sarah Elaine Eaton, Beatriz Antonieta Moya Figueroa, Brenda McDermott, Rahul Kumar, Robert Brennan, and Jason Wiens, with institutional affiliations including University of Calgary, Pontificia Universidad Católica de Chile, Brock University, and others. The DOI link is visible at the top: https://doi.org/10.1080/02602938.2025.2587246.

Where Educators Found Common Ground

Across disciplines, participants agreed that prompting skills and critical thinking are appropriate to assess with chatbot integration. Prompting requires students to demonstrate foundational knowledge, clear communication skills, and ethical principles like transparency and respect. Critical thinking assessments can leverage chatbots’ current limitations – their unreliable arguments, weak fact-checking, and inability to explain reasoning – positioning students as evaluators of AI-generated content.

The Nuanced Territory of Writing Assessment

Writing skills proved far more controversial. Educators accepted chatbot use for brainstorming (generating initial ideas) and editing (grammar checking after independent writing), but only under specific conditions: students must voice their own ideas, complete the core writing independently, and critically evaluate any AI suggestions.

Notably absent from discussions was the composition phase – the actual process of developing and organizing original arguments. This silence suggests educators view composition as distinctly human cognitive work that should remain student-generated, even as peripheral tasks might accommodate technological assistance.

Broader Concerns

Participants raised important challenges beyond specific skill assessments: language standardization that erases student voice, potential for overreliance on AI, blurred authorship boundaries, and untraceable forms of academic misconduct. Many emphasized that students training to become professional communicators shouldn’t rely on AI for core writing tasks.

Moving Forward

Our findings suggest that ethical AI integration in assessment requires more than policies, it demands ongoing conversations about what makes learning authentic in technology-mediated environments. Educators need support in identifying which ‘cognitive offloads’ are appropriate, understanding how AI works, and building students’ evaluative judgment skills.

The key insight? Assessment in the AI era isn’t about banning technology, but about distinguishing between tasks where AI can enhance learning and those where independent human cognition remains essential. As one participant reflected: we must continue asking ourselves, “What should we be assessing exactly?”

The postplagiarism era requires us to protect academic standards while preparing students for technology-rich professional environments – a delicate balance that demands ongoing dialogue, flexibility, and our commitment to learning and student success.

Read the full article: https://doi.org/10.1080/02602938.2025.2587246

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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.

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Posted by Sarah Elaine Eaton, Ph.D.

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.

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2025-2026 Postplagiarism Speaker Series: Navigating AI in Education

September 26, 2025

Join us for an innovative speaker series exploring how artificial intelligence is transforming education, academic integrity, and the future of learning. The 2025-2026 Postplagiarism Speaker Series brings together leading researchers and educators from around the world to examine how we can navigate the integration of AI tools in educational settings while maintaining ethical standards and fostering authentic learning. This series is hosted by the Centre for Artificial Intelligence Ethics, Literacy, and Integrity (CAIELI), University of Calgary.

What is Postplagiarism? Postplagiarism (Eaton, 2023) refers to our current era where artificial intelligence has become part of everyday life, fundamentally changing how we teach, learn, and create. Rather than viewing AI as a threat to academic integrity, the postplagiarism framework offers practical approaches for embracing AI as a collaborative tool while preserving the values of authentic learning and ethical scholarship.

Series Highlights: This multi-part series features international experts who will share research-based insights and practical strategies for educators, administrators, and policymakers. Topics include foundational concepts of postplagiarism, assessment redesign, policy development, and innovative teaching approaches that prepare students for an AI-integrated world.

Who Should Attend:

  • Faculty and instructors across all disciplines
  • Educational administrators and policymakers
  • Graduate students in education
  • Academic integrity professionals
  • Anyone interested in the future of education and AI

Format: Each session combines research presentations with practical applications, offering attendees actionable insights they can implement in their own educational contexts. Sessions are hybrid so participants can attend either in person or online. Sessions are open to the public and free for everyone to attend.

The series showcases the groundbreaking postplagiarism framework developed at the University of Calgary, which has gained international recognition and been translated into multiple languages. Participants will gain cutting-edge knowledge about navigating the challenges and opportunities of generative AI in education.

Time: All sessions are held 12:00 p.m. (noon) to 13:00 Mountain time. Please convert to your local time zone.

Session 1 : Postplagiarism Fundamentals: Integrity and Ethics in the Age of GenAI

A promotional image for an AI Speaker Series event hosted by the University of Calgary. The background is orange with a geometric pattern. The text reads: 'AI Speaker Series, Sept 17, Dr. Sarah Eaton, University of Calgary, Postplagiarism Fundamentals: Integrity and Ethics in the Age of GenAI.' A blurred image of Dr. Sarah Eaton appears on the right. The CAIELI (Centre for Artificial Intelligence Ethics, Literacy and Integrity) logo is in the bottom left corner.

Date: September 17, 2025

Description: Join us to learn about the award-winning postplagiarism framework that has been translated into half a dozen languages and has received worldwide attention. Postplagiarism  refers to an era in human society in which artificial intelligence is part of everyday life, including how we teach, learn, and interact daily. (Eaton, 2023). Learn more about the six tenets of postplagiarism and how you can apply them to support students’ success.

Speaker: Dr. Sarah Elaine Eaton, University of Calgary

Bio: Sarah Elaine Eaton is a Werklund Research Professor at the University of Calgary. She researches academic integrity and ethics in educational contexts. Her work on postplagiarism marks her most important contribution to research, pedagogy and advocacy.

Check out the recording here.

Get a copy of the slides here.

Session 2: Smart or Shallow? Postplagiarism, Trust, and the Future of Learning with GenAI

A promotional image for the AI Speaker Series at the University of Calgary featuring Dr. Rahul Kumar from Brock University. The event is scheduled for October 1 and will discuss "Smart or Shallow? Postplagiarism, Trust and the Future of Learning with GenAI." The image includes a blurred-out photo of Dr. Rahul Kumar in a suit, standing outdoors with greenery in the background. The bottom section mentions CAIELI (Centre for Artificial Intelligence Ethics, Literacy and Integrity).

Date: October 1, 2025

Description: In the postplagiarism era, GenAI compels educators to confront a fundamental choice: should it be trusted as a complementary tool that enhances learning, or as a competitive tool that undermines it? This talk explores how cognitive and affective trust explain the differences among student, educator, and employer perspectives on AI’s role in education. Drawing on David C. Krakauer’s distinction between cognitive artifacts, Kumar argues that academic integrity now requires more than attribution or authorship. It requires deliberate pedagogical practices that guide learners to use AI in ways that enhance, rather than diminish, human intelligence.

Speaker: Dr. Rahul Kumar, Brock University

Bio: Dr. Rahul Kumar is an Assistant Professor, Department of Educational Studies, Brock University. In his research he focuses on the disruptive force of GenAI on education. Its effect on academic integrity and how to cope with it. Though most of his work has focused on higher education, he has also undertaken research projects on how secondary school teachers are dealing with GenAI in their classrooms and schools.

Register here: https://workrooms.ucalgary.ca/event/3939984

Session 3: Assessment in a Postplagiarism era: The AI Assessment Scale as a framework for academic integrity in an AI transformed world

Date: October 15, 2025

Description: Developments in Generative AI are leading us closer to the concept of ‘postplagiriaism’, with traditional concepts of academic integrity being fundamentally challenged by these technologies. This lecture explores how the AI Assessment Scale (AIAS) offers a pragmatic response to this upcoming paradigm shift, moving beyond futile attempts at AI detection towards thoughtful assessment redesign. In a world where AI-generated content is becoming indistinguishable from human work, the AIAS (Perkins et al., 2024) provides a five-level framework that acknowledges this new reality whilst maintaining academic authenticity.

Rather than treating AI as a threat to be policed, the AIAS embraces it as a tool to be thoughtfully integrated where appropriate. From ‘No AI’ assessments that preserve foundational skill development, to ‘AI Exploration’ tasks that prepare students for an AI-saturated workplace, this framework offers educators practical strategies for the postplagiarism landscape. This talk will demonstrate how institutions can move from an adversarial ‘catch and punish’ mentality to a collaborative approach that recognises both learning integrity and technological advancement. The session will challenge traditional academic integrity paradigms and offer actionable insights for this new era of university assessment.

Speaker: Dr. Mike Perkins, British University Vietnam

Bio: Dr. Mike Perkins heads the Centre for Research & Innovation at British University Vietnam, Hanoi. He is an Associate Professor and leads GenAI policy integration and trains Vietnamese educators and policymakers on this topic. Mike is one of the authors of the AI Assessment Scale, which has been adopted across more than 250 schools and universities worldwide, and translated into 20+ languages. His research focuses on GenAI’s impact on education, and has explored various areas within this field. This has included AI text detectors, attitudes to AI technologies, and the ethical integration of AI in assessments through the AI Assessment Scale. His work bridges technology, education, and academic integrity.

Register here: https://workrooms.ucalgary.ca/event/3925369

Session 4: Designing for Integrity: Learning and Assessment in the Postplagiarism Era

Date: November 19, 2025

Description: In the postplagiarism era, where generative AI and related technologies are embedded in how ideas are produced and shared, academic integrity must be reimagined. Rather than treating plagiarism as a violation to be detected and punished, integrity becomes something to be intentionally cultivated through the design of both learning and assessment. This talk will explore how postplagiarism challenges traditional notions of authorship, originality, and attribution, inviting educators to move beyond rule enforcement toward fostering creativity, responsibility, and agency. I will discuss how aligning learning activities with authentic, meaningful assessment can reduce plagiarism incentives while preparing students for ethical participation in a world where human and AI contributions are intertwined. Participants will be encouraged to rethink not just how we assess, but why—and to envision integrity as a shared, evolving value in the age of AI.

Speaker: Dr. Soroush Sabbaghan, University of Calgary

Bio: Dr. Soroush Sabbaghan is an Associate Professor and the Educational Leader in Residence in Generative AI at the University of Calgary’s Taylor Institute for Teaching and Learning. His work centres on human-centred design and the creation of human–AI collaborative environments, exploring the ethical, theoretical, and pedagogical implications of generative AI across K–12 and higher education. Drawing on research, teaching, and international collaborations, he examines how AI is reshaping notions of authorship, originality, and scholarly practice. Soroush is the editor of Navigating Generative AI in Higher Education: Ethical, Theoretical and Practical Perspectives, a collection that invites educators to critically engage with AI while maintaining care, dignity, and agency as core values. In his work, he encourages institutions to move beyond compliance-based approaches toward fostering creativity, responsibility, and adaptability in a hybrid human–AI world—principles that are at the heart of the postplagiarism era.

Register here: https://workrooms.ucalgary.ca/event/3939986

Session 5: Teaching Postplagiarism Tenets Through AI-Enhanced Creative Problem-Solving Model

Date: January 14, 2026

Description: While the concept of postplagiarism has gained increasing attention in the past two years, much of the discussion remains focused on writing tasks, leaving a gap in understanding how this framework applies to broader creative processes. Even less focus has been placed on strategies for teaching its tenets. This presentation bridges that gap by applying the Creative Problem Solving (CPS) model, enhanced with narrow AI tools such as chatbots, to explore how postplagiarism can be taught and understood in diverse creative contexts. By mapping the stages of CPS to postplagiarism’s key tenets, the session reveals nuanced connections between the two frameworks and offers what may be one of the earliest structured models for explaining current human–AI co-creation practices.

Speaker: Fuat Ramazanov, Acsenda School of Management

Bio: Fuat Ramazanov is the Program Director at Acsenda School of Management and a doctoral student at the University of Calgary. His doctoral research examines undergraduate students’ perceptions of the interplay between human and AI creativity throughout the creative process. A strong advocate for teaching for creativity, Fuat promotes approaches that cultivate creative thinking skills in students. His interests include innovative approaches to teaching, pedagogy in the age of AI, and the theory and application of postplagiarism framework.

Register here: https://workrooms.ucalgary.ca/event/3939987

Session 6: From Policy to Practice: A Postplagiarism Readiness Framework for AI Integration in Higher Education

Date: January 28, 2026

Description: This workshop introduces a readiness framework based on the six tenets of postplagiarism to critically assess institutional policies guiding faculty in using generative artificial intelligence. Participants will explore how the framework can be applied as a diagnostic tool to evaluate whether existing policies provide sufficient guidance, identify gaps, and support ethical, transparent, and future-ready integration of AI into teaching and learning. The session will combine conceptual grounding with practical analysis, offering participants strategies to strengthen policy and practice alignment in the age of AI.

Speaker: Dr. Beatriz Moya, Pontificia Universidad Católica de Chile

Bio: Dr. Beatriz Moya is an assistant professor at the Institute of Applied Ethics and the Pontificia Universidad Católica de Chile in Santiago, Chile. In her research she focuses on the intersection of academic integrity, educational leadership, and the Scholarship of Teaching and Learning (SoTL).

Register here: https://workrooms.ucalgary.ca/event/3939988

Session 7: A Transformative Model for Learning Academic Integrity in the Postplagiarism Era

Date: February 11, 2026

Description: The Postplagiarism framework has gained considerable attention, reshaping the landscape of academic integrity and ethical decision-making in education and research. This paradigm shift encourages educators to embrace the potential of artificial intelligence integration in transforming the competencies students need for their careers and communities. In this presentation I focus on two of the postplagiarism tenets:  enhanced human creativity and the disappearance of language barriers. I will showcase preliminary findings of my doctoral research on academic integrity. As we recognize students’ diversity, these two postplagiarism-tenets provide a framework for fostering creative communication and accessible educational environments while disappearing potential obstacles within a transformative model for learning academic integrity.

Speaker: Bibek Dahal, MPhil, University of Calgary

Bio: Bibek Dahal, MPhil is a PhD Candidate in higher education leadership, policy, and governance at the Werklund School of Education, University of Calgary, Canada. Bridging Southern epistemologies and justice-centered transformative research, his scholarship focuses on academic integrity and ethics in global higher education. His doctoral study investigates a transformative model for learning academic integrity in international higher education.

Register here: https://workrooms.ucalgary.ca/event/3945787

Session 8: Designing Authentic Assessment in the PostPlagiarism GenAI Era: Making Judgement Visible

Date: February 25, 2026

Description: GenAI shifts academic integrity from detection to design by asking educators to assign work only students can do. Aimed at educators, this workshop presents the 3Cs framework (construct, collaborate, create), developed in secondary classrooms and adapted for teacher education. Sharma will introduce amplified intelligence as a lens and centre capability-agnostic design so tasks remain valid as GenAI tools evolve. Practice is anchored in six postplagiarism tenets: hybrid human and AI writing becomes normal, creativity is enhanced, language barriers diminish, control may be delegated but responsibility cannot, attribution remains important, and definitions of plagiarism are evolving. Participants examine how purpose sets permissions for GenAI use and translate that stance into prompts, checkpoints, and reflections that make process as visible as product. Classroom-tested examples provide assessment patterns and syllabus language for disclosure, verification, and boundaries. Outcomes: visible judgement, honoured student agency, reduced outsourcing.

Speaker: Dr. Sunaina Sharma, Assistant Professor, Brock University

Bio: Dr. Sunaina Sharma, is an Assistant Professor in the Department of Educational Studies at Brock University, Ontario, Canada, specializing in secondary education and curriculum development. With 23 years of experience as a secondary teacher and 10 years as a program leader, she is deeply committed to creating a space for secondary students and educators to share their voices. Her recent research examines Ontario secondary school teachers’ responses to the proliferation of generative artificial intelligence (GenAI), focusing on their questions, concerns, and instructional needs. Dr. Sharma’s research on digital technology and student engagement underscores that engagement arises not from the digital tools themselves but from students’ ability to construct knowledge through their use. Her work contributes to ethical GenAI adoption and advances effective pedagogical practices in educational settings.

Register here: https://workrooms.ucalgary.ca/event/3945789

Session 9: The SETA Framework for Integrity Education in the Postplagiarism Era

Date: March 4, 2026

Description: Technological  Innovations are fuelling the call for changes in how students are taught and evaluated at all levels of the system. One visible change is a new focus on academic integrity, as the use of generative AI tools such as large language models (LLMs) have rendered the traditional discourse regarding plagiarism as obsolete, inadequate for the new environment. This presentation focuses on the  support, education for integrity, teaching and learning, and assessment (SETA) framework. It identifies the various elements that are necessary in educating students for academic integrity within the GenAI-enabled environment. It focuses on the role of policies and their importance in creating the context within which academic integrity education takes place, and includes the punitive element which is necessary in instances where students choose to act contrary to the requirements of the academy.  This discussion includes data gathered from students and librarians who participated in a 20 hour training for the development of academic integrity from across the Caribbean.

Speaker: Dr. Ruth Baker-Gardner, University of the West Indies, Jamaica

Bio: Dr. Ruth Baker-Gardner is the foremost voice on academic integrity in the Caribbean. She is a lecturer in librarianship at the University of the West Indies, Mona Campus in Jamaica. Dr. Baker-Gardner is author of Academic Integrity in the Caribbean which was awarded the Principal’s Research Award for outstanding Publication in the Book Category. She was also awarded the International Center for Academic Integrity Exemplar for Academic Integrity Award and the European Network for Academic Integrity Outstanding Researcher Award. Her latest publication Academic Integrity meets Artificial Intelligence, the Case of the Anglophone Caribbean, examines the region’s readiness for artificial intelligence use by examining its academic integrity structures and practices.

Register here: https://workrooms.ucalgary.ca/event/3945790

Session 10: Postplagiarism Perspectives: Comparative Insights from K-12 and Postsecondary Research

Date: March 18, 2026

Description: As generative AI technologies reshape educational landscapes, academic integrity must be reconceptualized across both K–12 and postsecondary contexts. Drawing from two doctoral research studies, this presentation explores the complex interplay between technological adoption, ethical formation, and institutional change.

The first study examines K–12 administrators navigating pedagogical and ethical uncertainties introduced by human-AI collaboration. Employing the Technology Acceptance Model, Innovation Diffusion Theory, and the 4M Framework, this research explores how administrators balance AI integration with pedagogical values during ‘AI arbitrage’, the liminal space where early adopting students outpace institutional adaptation.

The second study explores how CPA-accredited accounting programs embed ethical competencies through assessment, particularly regarding AI-enabled misconduct. Employing Rest’s Four-Component Model of Morality, Biggs’ Constructive Alignment, and an Integrity–Assessment Alignment Matrix, this research examines professional ethics education amid technological disruption.

Together, anchored in Eaton’s postplagiarism concept, these complementary theoretical lenses provide comprehensive analytical approaches to understanding educational transformation across the learning continuum.

Speakers: Naomi Paisley & Myke Healy

Bio: Naomi Paisley is a Chartered Professional Accountant (CPA) with over 20 years of experience in accounting, audit, and taxation. She currently teaches at the Southern Alberta Institute of Technology (SAIT), where she develops and delivers curriculum in financial reporting, assurance, and Canadian tax. Naomi is also a co-author of nationally adopted Canadian auditing and accounting textbooks and collaborates on the integration of evolving standards, DEI, ESG, and Indigenous perspectives in accounting education. As a doctoral candidate in the EdD program at the University of Calgary’s Werklund School of Education, her research explores how CPA-accredited undergraduate accounting programs prepare students for ethical challenges in the profession, particularly considering AI-enabled misconduct. Her study uses Rest’s Four-Component Model of Morality and Biggs’ Constructive Alignment to analyze ethics education and assessment practices in accounting programs. Naomi’s work supports the alignment of academic integrity initiatives with the expectations of the accounting profession and CPA Canada.

Myke Healy is an educational leader with over 20 years of experience in K-12 teaching and administration. He currently serves as Assistant Head – Teaching & Learning at Trinity College School, where he leads academic strategy and faculty development. Myke holds an M.Ed. in assessment and evaluation from Queen’s University and annually facilitates AI-focused modules at the Canadian Accredited Independent Schools (CAIS) Leadership Institute. As a doctoral candidate in the EdD program at the University of Calgary’s Werklund School of Education, his research examines how K-12 administrators navigate generative AI and academic integrity challenges during technological adoption. His study uses the Technology Acceptance Model, Innovation Diffusion Theory, and the 4M Framework to analyze AI integration and postplagiarism concepts in secondary education. Myke presents nationally and internationally on AI in education and serves on the Ontario College of Teachers’ accreditation roster, the board of eLearning Consortium Canada, and instructs leadership and assessment courses at Queen’s University.

Register here: https://workrooms.ucalgary.ca/event/3945792

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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.

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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/

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