Framing Academic Integrity

It can feel personal for us as instructors to come upon acts of academic dishonesty. Such moments “can feel demoralizing, especially for someone whose life work is in the pursuit of academic excellence. It may seem daunting to verify and address the situation” (Mental Health Information for Faculty). The following resources set examples meant to assist faculty in finding ways of defining academic integrity for their own learners as a primary audience.

Building from Values 

Academic Integrity, as set out by the International Center for Academic Integrity (3rd edition handbook, 2019), is a commitment, even in the face of adversity, to six fundamental values:

  1. Honesty
  2. Trust
  3. Fairness
  4. Respect
  5. Responsibility
  6. Courage

Addressing why integrity is important, they offer this statement:

Scholarly communities flourish when community members “live” the fundamental values. To do this, community members must invoke them—making them part of frequent dialogues that invite students, faculty, and administrators to consider the potential for ethical values to inform and improve various aspects of life on campus and beyond. (4)

In opening this section, titled “The Values,” the authors note dialogue as essential in establishing and sustaining integrity, and present each of the 6 values to support and extend discussions with students by noting definitions, core ideas, suggestions for framing the concept with students, and offering select resources.

Nottingham College’s “Use of Artificial Intelligence” policy identifies a seventh core value: Transparency that teachers and students need to address:

Student-facing staff have a responsibility to guide students in the use of AI tools [emphasis added] in accordance with this policy and the related guidance linked to above. Students should be helped to understand what constitutes academic misconduct when using AI tools to produce written work. 

The “Too Good to be True: Student Misuse of AI Tools” section of Nottingham College’s AI: guidelines for teachers, trainers and assessors publication notes that “the main concern around student use of AI tools is a valid one: they are not producing their own work which demonstrates how much they understand the topic and are able to apply that knowledge.”

Acknowledging that debates about education and new technology, about its impacts on integrity and originality, the guide frankly notes that this “is nothing new. History tends to show that after the initial panic, education has been able to adapt and adopt new technologies without too much disruption.” For more on that history, the authors offer the helpful “A Brief History of Academic Integrity Panics about Disruptive Technology,” which concludes with five pedagogical principles we can draw on in responding to this new context: 

  • getting to know the tools, 
  • communicating the purposes/value of educative work, 
  • focusing on process and scaffolding, 
  • recognizing the assistive technology aspects of some AI tools, and 
  • planning for change. 

Building from Others’ Definitions

Academic Integrity at Massachusetts Institute of Technology 

The Academic Integrity at MIT: A Handbook for Students acknowledges that acts of academic dishonesty are frequently unproductive responses to stress, uncertainty, and difficult situations rather than intentional or pre-planned acts, These responses are not uncommon in facing the desirable and often difficult aspects of learning. Further, when students can express difficulties, work toward specifically stated purposes, and learn practices to draw on specific situations, they will work to meet course assignment expectations effectively and ethically.

The following “What is Academic Integrity” segment can work as springboards for faculty shaping syllabus statements, and discussions with students about academic integrity:

Fundamental to the academic work you do at MIT is an expectation that you will make choices that reflect integrity and responsible behavior.

MIT will ask much of you. Occasionally, you may feel overwhelmed by the amount of work you need to accomplish. You may be short of time, working on several assignments due the same day, or preparing for qualifying exams or your thesis presentation. The pressure can be intense. On the Working Under Pressure page, we suggest resources to help you manage your workload and prevent yourself from becoming overwhelmed. However, no matter what level of stress you may find yourself under, MIT expects you to approach your work with honesty and integrity.

Honesty is the foundation of good academic work. Whether you are working on a problem set, lab report, project or paper, avoid engaging in plagiarism, unauthorized collaboration, cheating, or facilitating academic dishonesty.

The MIT handbook’s landing page features a action-oriented, proactive Do and Don’t suggestions for students under these headings, each of which can be adapted by instructors setting out their own integrity policies and suggested practices:

  • Plagiarism
  • Unauthorized
  • Collaboration
  • Cheating and 
  • Facilitating Academic Dishonesty

Academic Integrity at the University of Minnesota

The Academic Integrity online short course for undergraduate students is set up as an interactive textbook with the first section focusing on defining academic integrity and scholastic dishonesty, and the second section contextualizing these values in authentic scenarios. The online course aims to support students in gaining skills and concepts for creating ethical academic work that requires citations, accurate paraphrasing, summarizing, and quoting, as well as evaluation of source materials. Further features include:

  • After completing both modules, learners have the opportunity to take a quiz in Canvas for a certificate of completion.
  • While the short course is designed for early-undergraduate students, it is also a good resource for students who may not be familiar with academic integrity in the North American context. 
  • The short course can also serve as a refresher for instructors, and a companion instructor guide includes lesson plans and discussion questions to support talking with students about academic integrity within a discipline, and instructions for integrating segments of courses into courses.

Community Standards

The Office for Community Standards (OCS) homepage is a gateway to resources for instructors and students, and provides easy access to the Board of Regents Student Conduct Code Policy. These five key links within the site are good starting places for instructors reexamining current strategies for introducing and integrating academic integrity policies and practices:

  1. Student Conduct Codes 
  2. Promoting Academic Integrity in Your Course - sets out practices, campus resources, and advice from students.
  3. Avoiding Scholastic Dishonesty - a companion web page for students with suggestions for establishing honesty-focused practices, gaining writing and learning support from a range of campus-based support services.
  4. Responding to Scholastic Dishonesty - provides both why and how to report dishonesty, and links to specific policies and procedures developed by some UMN colleges.
  5. Finally, the site provides information about Academic Integrity Matters (AIM), a community-based, restorative justice-informed process “offered to students at the end of the disciplinary process for scholastic dishonesty when they accept responsibility for violating the Student Conduct Code.”

Academic Integrity in a generative AI Landscape 

For teachers beginning to think about integrating GenerativeAI into course activities and assignments, and considering the extent of this first excursions as well as how you’ll create new integrity policies and GenerativeAI practices into the courses you teach, we have selected three orienting essays, and a section setting out advice regarding detection software.

Orienting Essays

  • “The Case for Slow-Walking Our Use of Generative AI,” by James Lang for The Chronicle of Higher Education (requires setting up a free Chronicle account, or for a quick check, there’s a google document version). For slow-walking, Lang links AI consideration to four long-standing principles for creating effective assignments and assessments: variety, transparency, sequencing, and reflection.
  • “UMN students' perspectives on Generative AI,” a November 2023 report summarizing student focus groups’ concerns about (1) losing practice/instructions in critical thinking via use of GenAI, (2) inconsistent messaging about acceptable AI use, and (3) AI embedding existing cultural biases, and role AI will play in career futures. 
  • Leon Furze’s “The AI Assessment Scale: Version 2” offers a “starter friendly” categorization of Generative AI policy/practice options across 5 categories - No AI, AI-Assisted Idea Generation and Structuring, AI-Assisted Editing, AI Task Completion with Human Evaluation, and Full AI. This post also links to a just-published supporting article. 

Strategies, Ideas and Examples for gen AI-Aware Assessments

CEI’s public and regularly updated resources include the “Generative AI Teaching and Learning Resources” google document, which is also part of CEI’s “Teaching with GenAI” website.

In addition to the three assessment-focused resources listed in this section, 

  • Monash University, as part of its “Generative AI and Assessment” webpage, offers suggestions for updating assignment prompts, creating alternative assessments, targeting higher order thinking, using formative assessment, as well as future-focused and alternative assessments.
  • A webpage focused on “30 Ideas for Generating AI-Resilient Assessments” features strategies, ideas and examples for designing ChatGPT and AI-proof assignments and assessments that promote critical thinking, creativity, and human interaction among students” in graduate and undergraduate courses, and in multiple formats, in online and inperson campus and community, field and laboratory learning spaces
  • The crowdsource collection “101 creative ideas to use AI in education” (available as a webpage from which you can download a PDF) crosses disciplines in its university-oriented, teacher-created examples. The document does not include a Table of Contents; however, the landing page for each section includes descriptive titles for each idea.

Detection Software

As GenerativeAI capacities expand, the capabilities of detection software platforms to “call out plagiarism” or identify fakes has declined. Teachers and students can leverage the tools associated with each of these platforms to check for text matches.