{"id":116,"date":"2026-07-30T19:33:15","date_gmt":"2026-07-30T19:33:15","guid":{"rendered":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/?post_type=part&p=116"},"modified":"2026-09-22T02:41:30","modified_gmt":"2026-09-22T02:41:30","slug":"7-academic-integrity-in-the-era-of-ai","status":"publish","type":"part","link":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/part\/7-academic-integrity-in-the-era-of-ai\/","title":{"rendered":"7. Academic Integrity in the Era of AI"},"content":{"raw":"
Candice Vander Weerdt,\u00a0Rachel Rickel, Wendy Sarver, and Emily Guthe<\/div>\r\n
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Introduction: Why is this important?<\/h1>\r\n<\/div>\r\n
\r\n\r\nFollowing the release of ChatGPT in 2022 (Lee et al. 2024) and the quick adoption of generative AI technology in higher education, instructors must realize that students are using various Artificial Intelligence (AI) tools. This shift in access to generative technologies introduces ethical and pedagogical concerns for those in higher education. Workplace partners share that there is an increasing demand for AI literacy among those they employ. Employers expect new hires to be able to effectively leverage AI tools to promote productivity. And while students do seem to have at least baseline knowledge of how to use these AI tools, educators are also seeing an increase in cognitive offloading, when students ask AI to complete the cognitive, critical thinking process for them. This shortcut means that students are bypassing the mental processing required to build new neural connections. Some common generative AI tools include ChatGPT, Claude, Gemini, CoPilot, OpenClaw.\r\n\r\nWhile there is still a lack of coherent policies concerning the implementation in many educational institutions, in a survey of 256 college students there seems to be a hint at higher acceptance rates of generative AI in education among future-teacher track students, although these students were also found to be more reserved in their optimism compared to their non-teacher track college peers (Alvarez et al 2024). Furthermore, in their 2025 study, Gomez et al discover that Generative AI use is widespread in academia for both students and Instructors, but that the quickness of adoption is uneven, with students doing so at record speed and instructors more slowly and deliberately trying to place within the contexts of best pedagogical practices. But even with that, they state \u201cOverall, these findings portray a landscape of increasing acceptance and recognition of the transformative potential of generative AI in higher education, balanced by an awareness of its ethical, methodological, and pedagogical implications.\u201d Indicating that no matter what concerns AI may pose in the sphere of education that adoption in various forms is surely becoming more widely accepted and instead of working to ban educators must change with the times and work out how to soundly integrate into their curriculum and address with students how to use these new tools in an ethical manner.\r\n\r\n<\/div>\r\n
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Anti-AI Movement<\/h2>\r\n<\/div>\r\n
We also see a distinct Anti-AI Movement growing among both students and faculty who are actively resisting the integration of these types of tools. Their resistance is often rooted in ethical and social concerns (e.g., the massive environmental footprint of AI data centers, the millions of gallons of water consumed when cooling the servers, and the immense strain on local energy grids) (Baumer et al., 2025). Instructors must intentionally design policies that teach productive and ethical use while reminding students of the importance of engaging with the learning process.<\/div>\r\n
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Workplace Expectations vs. Student Critical Thinking Skills<\/h2>\r\n<\/div>\r\n
Higher education is facing friction because the workforce expects their employees to have AI literacy skills but if students are using AI to bypass some (or all) of their learning, they graduate lacking the critical thinking skills they need to be successful.\u00a0the process of delegating mental tasks to external tools or systems, thereby reducing the need for active human cognitive engagement<\/div>\r\n
If AI writes a synthesis of an article, then\u00a0we\u2019re\u00a0seeing\u00a0cognitive offloading\u00a0(the process of delegating mental tasks to external tools or systems, thereby reducing the need for active human cognitive engagement),\u00a0so the student\u00a0isn\u2019t\u00a0building the neural pathways required for development.\u00a0Teach students that it is important to use AI as a support, not a substitution.<\/div>\r\n
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Principles of Academic Integrity and Ethics<\/h2>\r\n<\/div>\r\n

Academic integrity includes the ethical standards and behaviors in academia while teaching, conducting research, and in service to the institution and profession (Macfalane, Zhang & Pun, 2012). In essence, academic integrity means one is responsible for their own work and acknowledges the ideas and words of others\u2019 work through references and credit (Campbell & Waddington, 2024). Academic dishonesty has historically been a persistent problem in academia. As early as the 1990s, 13-95% of all college students reported engaging in at least some form of academic dishonesty (McCabe & Trevino, 1993). The changing nature of education, such as the digital learning environment and availability of \u201cinstructor-only\u201d test banks, is often cited as further diminishing academic integrity behavior (Harper et al, 2019; Awdry and Newton, 2019). Some argue that generative AI poses may pose similar risks and add to the commoditization of higher education.<\/p>\r\n

Fortunately, common institutional academic integrity policies may already be poised to account for academic dishonesty infractions.\u00a0At its root, dishonest AI use\u00a0includes\u00a0failure to follow\u00a0ethical procedures in applying critical thinking and cognitive effort\u00a0through\u00a0academic exercises and assessments. These actions, in principle, are already disallowed by current academic integrity policies. We have compiled a list of common definitions within an academic integrity policy and how each item may\u00a0directly relate\u00a0to unethical generative AI use.\u00a0(Table 1 below)<\/p>\r\nTable 1\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
Unethical Behavior<\/span><\/span><\/td>\r\nDefinition<\/span><\/span>\u00a0<\/span><\/td>\r\nRelation to AI<\/td>\r\n<\/tr>\r\n
Cheating<\/span><\/span>\u00a0<\/span><\/td>\r\nUsing or attempting to use or possessing any aid, information, resources, or means in the completion of any graded course content such as, but not limited to, an academic assignment, quiz, examination, paper, portfolio, project, thesis, dissertation, or assessment (collectively defined as \u201cassessment\u201d) that are not explicitly permitted by the\u00a0<\/span><\/span>instructor, or<\/span>\u00a0<\/span>facilitating cheating by another student<\/span>.\u00a0\u00a0<\/span><\/span>\u00a0<\/span><\/td>\r\nUsing an AI tool to generate answers during a closed-book exam.<\/span><\/span>\u00a0<\/span><\/td>\r\n<\/tr>\r\n
Plagiarism<\/span><\/span><\/td>\r\nPresenting as one\u2019s\u00a0<\/span><\/span>own work<\/span>, the ideas, the representations, or the words of another person\/source without proper attribution<\/span>.\u00a0<\/span><\/td>\r\nSome results from generative AI may not include proper citations or references for information and ideas;\u00a0<\/span><\/span><\/span>Pasting raw AI-generated text directly into an assignment without\u00a0<\/span><\/span>disclosing<\/span>\u00a0<\/span>the use of an AI tool<\/span><\/span><\/td>\r\n<\/tr>\r\n
Fabrication<\/span><\/span><\/td>\r\nFalsification, invention, or manipulation of any information, citation, data, or method.<\/span><\/span>\u00a0<\/span><\/td>\r\nGenerative AI practices hallucinations or illustrative citations or references when returning data or information.<\/span><\/span><\/td>\r\n<\/tr>\r\n
Unauthorized<\/span>\u00a0<\/span>Collaboration<\/span><\/td>\r\nWorking with another individual or individuals in any phase of or in the completion of an individual academic assessment without explicit permission from the instructor to complete the work in such a manner<\/span><\/span>\u00a0<\/span><\/td>\r\nWithout permission from the instructor, individuals using AI may be using other individuals<\/span>\u2019<\/span>\u00a0<\/span><\/span>work<\/span>\u00a0<\/span><\/span>inappropriately.\u00a0<\/span><\/span>\u00a0<\/span><\/td>\r\n<\/tr>\r\n
Misrepresentation<\/span><\/span>\u00a0<\/span><\/td>\r\nFalsely representing oneself or information<\/td>\r\nWithout acknowledging the use of AI, students may misrepresent their own writing or critical thinking skills in academic assessments.\u00a0<\/span><\/span><\/td>\r\n<\/tr>\r\n
Gaining an unfair advantage<\/span><\/span>\u00a0<\/span><\/td>\r\nCompleting an academic assessment through use of information or means not available to other students or engaging in any activity that interferes with another student\u2019s ability to complete his or her academic work<\/span><\/span><\/td>\r\nUsing generative AI against the\u00a0<\/span><\/span>instructor's<\/span>\u00a0<\/span>policy or intention may lead to cognitive off-loading, preventing students from learning and growing through practice.\u00a0<\/span><\/span>\u00a0<\/span><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n
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How to Implement AI Expectations in Courses<\/h2>\r\n<\/div>\r\n

A large part of instructor expectations of AI use should be shaped by clear communications between both parties. Thus, having clear guidelines outlined in both an instructor\u2019s syllabus, and even on future assignment sheets, can play a role in shaping student use and understanding of what can be considered acceptable. See below some detailed examples of syllabus statements for AI use in various courses at Cleveland State University.<\/p>\r\n\r\n

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Example from a Writing Intensive Graduate Nursing Course:<\/strong><\/h4>\r\n<\/div>\r\n
\r\n\r\nUse of AI to generate content for written assignments (papers, discussion posts) and presentation is NOT allowed. All written assignments must be submitted to undetectable AI prior to submission for grading. Assignments with over 20% must be rewritten prior to submission. Papers submitted with over 20% AI in Turnitin will be returned to the student to rewritten.\r\n\r\n<\/div>\r\n
\r\n\r\nUse of AI programs such as Grammarly for proofreading, grammar, and checking APA formatting IS allowed.\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n
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Example of AI Syllabus Statements from the First-Year Writing Program at CSU\u00a0<\/span><\/strong><\/h4>\r\n
\r\n\r\nWhen it is OKAY to use AI in this course<\/strong>\r\n\r\n<\/div>\r\n
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