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Microsoft's prompt engineering techniques 

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Microsoft's prompt engineering techniques 

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Azure OpenAI in Azure AI Foundry Models - Azure OpenAI

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Learn how to use prompt engineering to optimize your work with Azure OpenAI.

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[Answer Key] Anthropic's Prompt Engineering Interactive Tutorial [PUBLIC ACCESS]

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Pedagogical Biases in AI-Powered Educational Tools: The Case of Lesson Plan Generators

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Pedagogical Biases in AI-Powered Educational Tools: The Case of Lesson Plan Generators

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Pedagogical Biases in AI-Powered Educational Tools: The Case of Lesson Plan Generators | Social Innovations Journal

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This paper examines pedagogical biases in AI-powered educational tools, focusing specifically on lesson plan generators. We investigate how these tools may implicitly embed outdated educational approaches that limit student agency and classroom dialogue. Through analysis of 90 lesson plans from commercial lesson plan generators, we found that AI-generated content predominantly promotes teacher-centered classrooms with limited opportunities for student choice, goal-setting, and meaningful dialogue. To mitigate this issue, we further experimented with intentional prompt engineering, which showed promise in significantly enhancing these dimensions in AI-generated lesson plans. We offer practical strategies for educators and developers to mitigate harmful pedagogical biases while promoting contemporary educational values. This work contributes to the critical conversation about how AI tools should be designed and used to support, rather than undermine, the future of education that values student agency and productive classroom dialogue.

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AI Assignment Library @ University of North Dakota

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AI Assignment Library @ University of North Dakota

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UND faculty created the materials in this AI Assignment Library in the context of ChatGPT and other generative AI's immediate impact on higher education. Faculty who submitted their assignments did an intensive workshop on generative AI and the information landscape which informed their assignment design. Then faculty colleagues peer-reviewed each other's work in an assignment charrette. Assignment authors revised their assignment informed by that feedback. Users are invited to search the assignment library for ideas using disciplines, course level, or student learning outcomes. All assignments are designed to advance student learning in the context of the new information environment created by generative AI.

Submissions from 2024

Using Generative AI in Professional Writing, Renee Cardarelle

Submissions from 2023

Optimization of a Bioreactor, Ali Alshami

Sociology Final Project/Presentation, Allyssa Baumbach

AI and Concerns on Bias and Correctness in Engineering, Stacy Bjorgaard

Partner Discussion using an EHR and ChatGPT, Anne Bodensteiner

SIOP Lesson Plan Analysis, Hyonsuk Cho

Is ChatGPT good at establishing validity?, Travis Clark

Exploring Childhood Psychological Disorders through Chat GPT: Writing Fictional Vignettes from a Parent's Perspective, Virginia Clinton-Lisell

Using ChatGPT to Enhance Law School Assessments, Nikola Datzov

Geopolitics of Outer Space, Francisco Del Canto Viterale

Annotated Bibliography Assignment for an Art History Class, Nicole Derenne

Can Artificial Intelligence, specifically Large Language Models (LLMs), enhance human emotional intelligence responses in a small team work environment?, Jason Evans

Selection of materials for a nuclear rocket engine, Marcos Fernandez-Tous

In-class Debate on ChatGPT, Chris Gable

Doctor of Nursing Practice Transformative Leadership AI assignment, Elizabeth Gotay

Using AI for Diagnosis and Treatment Plan, Andrea Guthridge

A & P Informational Literacy, Amanda Haage

Laboratory Project 2 Temporary Surcharge Fill: Compaction Test, I-Hsuan Ho

Family Theory Framework and Case Analysis using AI Tools, Yi-Ping Hsieh

Generative AI-Assisted Pathway Analysis and Interpretation of RNA-Seq Experiment Data, Junguk Hur

Generative AI in Medical Laboratory Science, Shannon Jongeward

Using Generative AI in the Classroom: Fact-Checking ChatGPT as Part of a Capstone Project, Alison Kelly

Promoting Personal Agency in Nursing Students' Ability to Evaluate Evidence, Kaitlyn Kelly

Humans Emulating Machines: Mimicking Predictive Text Algorithms, Gabe Kilzer

Generative Text AI: towards a personal ethics of use, Merie Kirby

Exploring Potential Impact of AI on Social Work Practice, Barbara Kitko

Informal Writing Assignment for HON 101, Hamzat Koriko

Public Health AI Assignment, Amber Lyon-Colbert

AI Costume Design Assignment, Kylie McDonald

Prison Visitation Policy and Procedure Guide, Meghan Mitchell

Sociological Debate Using AI, Sandra Moritz

What does ChatGPT know about toponyms when looking at maps in GEOG 161?, Douglas C. Munski

Topic Proposal Assignment Sheet, Emily Cherry Oliver

Summarizing Tribal Histories Using ChatGPT, Cynthia Prescott

Integrated Health/Physical Education Lesson Plan, Mike Rerick

What are the ethics of AI in relation to the NASW Code of Ethics?, Lea Saga

Organizing a Class Discussion, Patrick Schultz

Find the Distance to the Moon, Dean Smith

Guidelines for Conducting CBPR in American Indian and Alaska Native Communities and the use of Artificial Intelligence, Julie Smith- Yliniemi

Transformed Writing: AI Applications, Brooke Solberg

Introduction to using generative AI in research literature review for graduate students, Joseph Vacek

Checking ChatGPT for Accuracy, Carolyn Williams

Is AI an Actor?, Emily Wirkus

Leadership Paper, Raquel Zeidlik

Chatting as Brainstorming: Drafting Research Questions with Generative AI in an Introductory Composition Course, Jessica Zerr

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UND faculty created the materials in this AI Assignment Library in the context of ChatGPT and other generative AI's immediate impact on higher education. Faculty who submitted their assignments did an intensive workshop on generative AI and the information landscape which informed their assignment design.

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Generative AI and Policy Development: Guidance from the MLA-CCCC Task Force

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Generative AI and Policy Development: Guidance from the MLA-CCCC Task Force

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Advocates developing tiered policies at the institutional, program, and class levels with recommendations.

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Crowd-sourced Google doc with select institutional (university) policies on GenAI.

Created by Lance Eaton.

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Institutional AI Policies & Governance Structures for Higher Education

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Guide to use of GenAI in courses and syllabi

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Guide to use of GenAI in courses and syllabi

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Resources for instructors in designing course policy and activities, including template syllabus language, suggested assignment design.

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Use of Generative Artificial Intelligence in Courses

As with other course features, instructors have the freedom to integrate generative artificial intelligence (GAI) to support their teaching goals. With the recent increase in GAI's popularity, instructors should carefully consider how (if at all) they will permit the use of GAI in their courses. All instructors should communicate any expectations or requirements clearly with students to avoid confusion. It may help to think in terms of three main options that apply to an entire course:

Closed: The use of GAI is prohibited.

Conditional: The use of GAI is permitted when explicitly authorized by the instructor.

Open: The use of GAI is encouraged. Students must adhere to the instructor's expectations.

Considering the Use of GAI in Your Courses

We recommend the following steps as you consider the use of GAI in the Northwestern classroom:

First, consider the technology itself and the larger learning objectives of your class:

Will it contribute to your students' learning?

Does it have a useful role to play?

How do the skills that you are helping students gain relate to GAI?

Once you have identified the ways in which the use of GAI or the absence of GAI may support your learning goals for the class, think about a blanket policy for the class: Will it be closed to student use of GAI, allow use of GAI with certain conditions, or will the class be open to use of GAI as the students see fit? The framework below may help you as you make these decisions:

(Framework inspired by Forbes, M. & Brandauer J. "What's my stance on genAI in this class?" Gettysburg College Johnson Center for Teaching and Learning. Retrieved 8/20/2023 from https://genai.sites.gettysburg.edu/positions-and-policies/)

Next, you might use the ideas below to start a robust discussion about the role of GAI in your class with your students. You should be transparent, explaining the reasoning behind your policy as it relates to the purpose and goals of the course. Flexibility might also be key, as this is a newer area for students and instructors alike.

Finally, as you formulate your own philosophy about the use of GAI in your classes, you should think about specific assignments and whether your blanket policy fits each of them. You should offer clear examples of when GAI can and cannot be used, explain how to cite the use of GAI tools, and discuss the consequences of not citing or inappropriate use of these tools.

Considering GAI Tools

We encourage the community to choose generative AI tools that are:

Supported by the university

Accessible

Accurate

Transparent about their uses of data

Practitioners of good data security

Retain data a short amount of time

Ethical in terms of access to student and instructor data, including refraining from using these data to train their models.

Template Statements for Syllabi

Whatever approach instructors take to GAI, they should document their expectations on syllabi. Doing so is of particular importance as students may encounter vastly different expectations across their courses.

To represent each of the three main options for GAI, instructors might consider statements like these, which are derived from Kellogg School of Management materials:

Closed: You are prohibited from using Generative Artificial Intelligence (GAI) to produce any materials or content related to this course. Any use of GAI will be viewed as a potential academic integrity violation.

Conditional: In this course, you are allowed to use Generative Artificial Intelligence (GAI) on assignments only for the purposes specified in assignment prompts. Any use of GAI should be accompanied by a disclosure at the end of an assignment explaining (1) what you used GAI for; (2) the specific tool(s) you used; and (3) what prompts you used to get the results. Any use of GAI beyond where permitted will be viewed as a potential academic integrity violation.

Open: In this course, you are allowed to use Generative Artificial Intelligence (GAI) in any manner. Any use of GAI should be accompanied by a disclosure at the end of an assignment explaining (1) what you used GAI for; (2) the specific tool(s) you used; and (3) what prompts you used to get the results.

Northwestern's Cook Family Writing Program has compiled more information about and examples of syllabus statements in a Canvas module with GAI resources.

On the Office of the Registrar's syllabus statement page, there is an optional GAI statement listed under the academic integrity statement, as well as a recommended syllabus statement for courses in which GAI use is permitted.

Considerations Before Prohibiting Use of GAI

While instructors are free to prohibit any use of GAI in their courses, they should consider the following implications:

GAI is integrated deeply into many tools and will become increasingly difficult to opt out of.

Students will likely be expected to use GAI in future pursuits (e.g., graduate and professional studies, careers, etc.). Learning effective and responsible ways to use GAI will help prepare them to succeed.

Overly relying on handwritten or oral exams may impact accessibility​.​

Some students may particularly benefit from using GAI tools. For example, students for whom English is not their first language may rely on GAI to enhance their grammar and fluency in academic communications.

​Considerations Before Requiring Use of GAI

Instructors who are interested in experimenting with GAI as a teaching tool should anticipate how they will address potential barriers to equity and success, especially if the use of GAI is required:

Some GAI tools are moving to a paid subscription model or offer a more effective and powerful version as a paid subscription, potentially increasing the cost of enrolling in a course that requires GAI tools or allowing for inequitable access to the best GAI tools.​​

Instructors who incorporate GAI tools into their courses should intentionally develop and follow processes that ensure critical evaluation of those tools' outputs by themselves and their students.

The volume and diversity of news about GAI means that students are likely entering courses with a range of awareness about what it is, let alone its utility and appropriateness for coursework. How instructors engage students about GAI in their courses will depend on the subject and format of those courses. In any dialogue about GAI, students should reflect on their purpose in being at Northwestern and intrinsic motivation for learning. They should consider how using GAI in ways instructors do not intend may circumvent learning that is essential for success at Northwestern and beyond.

Topics to Address

In planning what to discuss with students, instructors should address how these broad interrelated topics apply to their courses:

Technology: Discuss with students the technology itself, including GAI's strengths, limitations, and ethical concerns.

Transparency: Explain course expectations and requirements around GAI, ideally as they relate to the course's learning outcomes.

Usage: Offer clear examples of when and how GAI can, cannot, or must be used. If GAI is banned, explain the reasons, ideally as they relate to the course's learning outcomes.

Citation: If GAI is allowed and/or required, explain how to cite the use of these tools in the course.

Consequences: Discuss the consequences of unauthorized use of GAI tools or a failure to acknowledge permitted use.

Strategies for Discussing Uses of GAI with Your Students

Open a conversation about the use of GAI with your students. Students may or may not be familiar with the tools, how to use them, how to cite them, or their promise and limitations. A series of activities with your students might include:

Poll or discussion questions for your students:

What are some of the ways you've explored GAI?

If you could use GAI for one assignment, which one would you choose and why do you think it is a good idea?

Do you think there should be boundaries for this class on the use of GAI? If so, what should they be?

Policy review activity:

Review/discuss specific examples of appropriate or inappropriate use of GAI

Conduct a syllabus survey reviewing important class assignments and policies, including the use of GAI tools

Discuss recent articles about GAI and connections to your course:

Wired explanations of where and when users may encounter the work of GAI in the publication. Could the students write a similar explanation for their work in your class?

Ethan Mollick's piece on "automating creativity." How do students describe their own individual creativity? In what ways do they think GAI may enhance that creativity? And how might introducing their creativity to GAI advance the creativity of those tools?

The students at the University of Sydney co-created a document with general principles for the use of GAI at their institution, which includes a set of scenarios that may help you and your students determine the acceptability of the use of GAI in your course.

Above all, the point of these discussions with your students is to build their investment in their own learning through transparency and trust. View the advice from Elizabeth Lenaghan, a Professor in the Writing Program, about discussing the use of GAI with your students.

Instructors permitting the use of GAI tools should clearly communicate their expectations for attribution and specific citation formats from the guide(s) they prefer. Failure to properly attribute GAI tools may violate the University's academic integrity policy:

Cheating: "Allowing another person or resource (including, but not limited to, generative artificial intelligence) to do one's work and submitting that work under one's own name without proper attribution."

Plagiarism: "Plagiarism includes, but is not limited to, the unauthorized use of generative artificial intelligence to create content that is submitted as one's own."

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Use of Generative Artificial Intelligence in Courses: Artificial Intelligence at Northwestern - Northwestern University

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As with other course features, instructors have the freedom to integrate generative artificial intelligence (GAI) to support their teaching goals. With the recent increase in GAI's popularity, instructors should carefully consider how (if at all) they will permit the use of GAI in their courses.

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Teaching & Learning in the Age of genAI. Gettysburg College Infographic on GenAI Policy for classes.

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A crowd-sourced Google doc with links to university syllabus policies on GenAI. Can be downloaded as a spreadsheet and sorted by course, discipline, institution, etc.

Created by Lance Eaton.

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Syllabi Policies for AI Generative Tools

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Inside Higher Education, "Survey: When Should College Students Use AI? They’re Not Sure" (September 2024)

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Inside Higher Ed | Higher Education News, Events and Jobs

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As generative artificial intelligence tools become more common in schools, workplaces and other settings, colleges and universities are juggling how to prevent misuse of AI in the classroom while equipping students for the next chapters of their lives after higher education.

A May 2024 Student Voice survey from Inside Higher Ed and Generation Lab found that, when asked if they know when or how to use generative AI to help with coursework, a large number of undergraduates don't know or are unsure (31 percent). Among students who did know when to use AI appropriately, that direction came from faculty (31 percent).

Methodology

Inside Higher Ed 's annual Student Voice survey was fielded in May in partnership with Generation Lab and had 5,025 total student respondents.

The field dates could put the data "a little behind the curve already on how schools have adapted and instituted policies," says Chuck Lewis, an English professor at Beloit College and director of its writing program. "I think, even as quickly as this fall, I bet those numbers would change pretty significantly nationally."

The sample includes over 3,500 four-year students and 1,400 two-year students. More than one-third of respondents were post-traditional (attending a two-year institution or 25 or older in age), 16 percent are exclusively online learners and 40 percent are first-generation students.

The complete data set, with interactive visualizations, is available  here. In addition to questions about their  academics, the survey asked students about health and wellness, college experience, and preparation for life after college.

Experts say providing clear and transparent communication about when AI can or should be used in the classroom is critical and requires faculty buy-in and understanding of related tools.

From Fearful to Future-Looking

Only 16 percent of Student Voice respondents (n=817) said they knew when to use AI because their college or university had published a policy on appropriate use cases for generative AI for coursework.

Students aren't floundering in confusion without reason; 81 percent of college presidents, in early 2024, reported that they had yet to publish a policy governing the use of AI including in teaching and research, according to Inside Higher Ed's 2024 presidents' survey.

Similarly, a minority of provosts said, also earlier this year, that their institution had published a policy that governs the use of AI (20 percent), according to Inside Higher Ed's 2024 chief academic officers' report.

When ChatGPT first launched in November 2022, administrators and others working in higher education initially panicked over how students could use the tool for plagiarism.

Slowly, as new generative AI tools have emerged and a growing number of employers have indicated AI skills may be necessary in the workforce, college and university leaders have turned a corner, considering AI as a career development skill or walking back use of AI plagiarism detectors, shares Afia Tasneem, senior director of strategic research at the consulting firm EAB.

"Just a few months later, there was noticeable recognition that this was not a technology that you could just ban and declare victory and go home," says Dylan Ruediger, senior program manager of the research enterprise at Ithaka S+R. "And since then, I've seen most institutions trying to find frameworks for thinking about generative AI as pedagogically useful."

In the Classroom

Student Voice data found if students did know when to use generative AI, it was because at least some of their professors had addressed the issue in class (31 percent) or had included a policy in their syllabus (29 percent).

The biggest challenge in getting students AI ready is getting faculty on board, Tasneem says. A June survey from Ithaka found two in five faculty members were familiar with AI, but only 14 percent were confident in their ability to use AI in their teaching.

"If you look at university policies around student use of generative AI, they will quite often kick that decision to individual instructors and advise students to follow the rules that each instructor gives them," Ruediger says.

Faculty members generally fall into three camps: those who require students to use AI, those who are absolutely prohibiting AI use and those who allow for limited use of AI when appropriate, Tasneem says.

At Beloit College in Wisconsin, the policy is to have no institutional-level policy, says Chuck Lewis, director of the writing program. "Faculty need to develop an informed, transparent and clear policy regarding their own classes and their own pedagogies."

Like many of his colleagues in writing programs, Lewis was confronted early with the potential of AI in writing and how it could be used to circumvent student effort. But Lewis quickly realized that this technology was larger than reproducing writing samples and could also serve as a tool for deeper thinking.

"AI is an opportunity for us to revisit and maybe rethink or reinforce, but at least to rearticulate, all kinds of things that we think we know or believe about, for instance, learning and writing," Lewis says. "It defamiliarizes us, in some sense, with our expectations and our norms. It's an opportunity to go back and think, 'Well, what is it about relationships?' In terms of audience and purpose and whatnot."

One example: In a creative writing course, Lewis and his students debated when it's OK to let technology produce your writing, such as using suggested replies to a text message or email or sending a message to someone on an online dating site.

"If we can step away from this overdetermined, what we think we're doing in the classroom, and think about these other places where we're producing consuming content, it, again, sort of defamiliarizes us with what we want and why."

In the Student Voice survey, learners at private institutions were more likely to say their professors had a policy in the syllabus (37 percent), compared to their peers at four-year publics (31 percent) or two-year publics (24 percent), which Lewis says may be due to the nature of private liberal arts colleges. "It's very consistent with our mission and our brand to be very engaged with student processes."

As colleges and universities elevate generative AI skills as a career competency or a factor that is central to the student experience in higher education, policies remain a challenge.

"As long as individual instructors have ultimate say over how it gets used in their classroom, it's likely that there will be instructors who prefer not to allow the use of generative AI," says Ruediger of Ithaka. "The general turn towards thinking about how to leverage generative AI, that's happened already, and what happens next will largely depend on whether or not people are successful in finding effective ways to use it to actually foster teaching and learning."

Equity Gaps

Student Voice data highlighted awareness gaps among historically disadvantaged student groups.

Forty percent of students at two-year public institutions said they were not sure about appropriate use, compared to 28 percent of public four-year students and 21 percent of private four-year students.

Adult learners (ages 25 and up) were more likely to say they're not aware of appropriate use (43 percent) compared to their traditional aged (18- to 24-year-old) peers (28 percent). First-generation students (34 percent) were also less likely to be confident in appropriate use cases for AI compared to their continuing-generation peers (28 percent).

"I think a bad outcome would be to have knowledge about how to leverage this tool become part of the hidden curriculum," Ruediger says. "It really underscores the need to be transparent and clear, to make sure that it's fostering equitable use and access."

Part of this trend could be tied to the type of institution students are attending, Lewis says, with students from less privileged backgrounds historically more likely to attend two- or four-year institutions that have yet to address AI at the faculty level.

It also hints at larger systemic disparities of who is or is not using AI, says EAB's Tasneem.

Women, for example, are less likely to say they're comfortable using AI, and people from marginalized backgrounds are more likely to say they avoid using tools such as ChatGPT that regurgitate racist, sexist, ageist and other discriminatory points of view, Tasneem added.

Institutional leaders should be aware of these awareness gaps and understand that not using AI can displace groups in the workplace and result in inequities later, Tasneem says.

Around one-quarter of Student Voice respondents said they've researched when they should use generative AI to understand appropriate use in the classroom. Men were most likely to say they've done their own research on appropriate use of ChatGPT (26 percent), while first-gen students, adult learners (20 percent) and two-year students (19 percent) were least likely to say that was true.

Nontraditional students and first-generation learners are more likely to be uncertain about making choices in their higher education experiences, Tasneem says. "They feel like they don't know what's going on, which makes it all the more important for faculty members to be transparent and clear about policies to level the playing field about what's expected and prohibited. No one should have to do research by themselves or be in doubt about AI use."

Put Into Practice

As colleges and universities consider how to deliver policy and inform students of appropriate AI use, experts recommend campus leaders:

Survey Says

A majority of provosts said faculty or staff have asked for additional training related to developments in generative AI (92 percent), and around three-quarters of institutions have offered training to address faculty concerns or questions about AI in the past 18 months, as of May, according to Inside Higher Ed 's 2024 provosts' survey.

Offer professional development and education. To prepare community members for working alongside AI, institutions should be offering workshops and education training, and these should be geared toward students and faculty members, Tasneem says. Only 8 percent of Student Voice respondents (n=413) said they knew of appropriate AI use in their courses because their institution has provided information sessions, trainings or workshops on the subject. "As we learn more and as institutions start using it more for academics and operations, we'll start to see more tailored training, discipline-specific training," she predicts.

Provide sample language. Some colleges have created syllabus templates for professors to adapt and apply to their courses. The University of Washington's center for teaching and learning has three samples for professors who encourage, prohibit or conditionally allow students to use AI.

Identify champions. To encourage hesitant faculty members to engage with artificial intelligence tools, administrators can elevate faculty or staff members who are enthusiastic about the technology to bring their colleagues on board, Ruediger says.

Communicate regularly with students. Appropriate AI use is not a topic that can be covered once and then never revisited, Lewis says. "It can't just be a boilerplate and syllabus-it has to be tied again and again to specific contexts." Faculty should investigate different elements of learning-such as researching, brainstorming and editing-and talk about specific ways AI can be applied to various stages of the process.

Set guiding principles. Application of how AI is used in the curriculum should remain at the professor's discretion, experts agree. But a college- or universitywide policy can reaffirm the institution's values and mission for how to approach AI with ethics, Tasneem says.

Consider academic dishonesty policies. Allowing AI use to be a professor-level decision, while beneficial for teaching and learning, may create some challenges for addressing academic integrity as students navigate differing policies in various courses, Lewis says. "This is about to get much more complicated in terms of the kinds of infractions that are going to come up, because they're going to be much more variable."

Should using generative AI be a part of a student's core curriculum or a career competency? Tell us your thoughts.

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College students uncertain about AI policies in classrooms

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Data from Inside Higher Ed's 2024 Student Voice survey shows that three in 10 students are not clear on when they're permitted to use generative artificial intelligence in their coursework. Higher ed experts say AI policies should be led by faculty members, considering institutional values.

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AUS Department of English - GenAI Ethical Framework

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AUS Department of English - GenAI Ethical Framework

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Approved by the AUS Department of English, this AI Ethical Use Framework is intended to guide responsible policy and practices concerning AI in the department and classrooms.

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AI Ethical Framework_AUS DoE_CC BY-NC 4.0

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UNESCO's AI Literacy Framework, released May 2025

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UNESCO's AI Literacy Framework, released May 2025

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https://ailiteracyframework.org/

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UNESCO-Empowering Learners for the Age of AI

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Upskilling teachers to use generative artificial intelligence: The TPTP approach for sustainable teacher support and development

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Upskilling teachers to use generative artificial intelligence: The TPTP approach for sustainable teacher support and development

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Keywords:
generative artificial intelligence (GenAI), artificial intelligence in education (AIED), artificial intelligence, teacher training, professional development

Abstract

There is a growing need to upskill higher education (HE) teachers for the effective and responsible integration of generative artificial intelligence (GenAI) in their classrooms. This case study sought to address this growing need by designing and delivering a training course for educators, focusing on the use of ChatGPT as it was the most commonly used tool at the time. The professional development opportunity lasted 5 weeks and covered critical aspects of GenAI use for teaching and learning. Data collected from participants included discussion board entries, written tasks and focus groups. Findings highlight some of the common practices and concerns HE practitioners had regarding the use of GenAI in their practice. The findings also emphasise the importance of providing teachers with customised GenAI training to facilitate its effective integration in HE contexts. Finally, based on the findings of this study, we propose the TPTP Support System for Teachers, built upon four key areas: teacher training, pedagogical support, testing revamp and practice networks. This system aims to guide institutional efforts to facilitate and support educators as they integrate GenAI in HE.

Implications for practice or policy:

Teacher training is necessary for the effective integration of GenAI in HE contexts.

Institutions should provide support in four key areas to facilitate educators' effective and responsible use of GenAI in HE. The TPTP Support System for Teachers can be leveraged for these planning and support initiatives.

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Upskilling teachers to use generative artificial intelligence: The TPTP approach for sustainable teacher support and development

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generative artificial intelligence (GenAI), artificial intelligence in education (AIED), artificial intelligence, teacher training, professional development There is a growing need to upskill higher education (HE) teachers for the effective and responsible integration of generative artificial intelligence (GenAI) in their classrooms. This case study sought to address this growing need by designing and delivering a training course for educators, focusing on the use of ChatGPT as it was the most commonly used tool at the time.

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AI- mediated sensemaking in higher education students’ learning processes

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AI- mediated sensemaking in higher education students’ learning processes

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Silovola et al, 2025 - AI- mediated sensemaking in higher education students’ learning processes- Tensions,sensemaking practices, and AI-assigned purposes

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Why Generative AI Literacy, Why Now and Why it Matters in the Educational Landscape? Kings, Queens and GenAI Dragons

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Why Generative AI Literacy, Why Now and Why it Matters in the Educational Landscape? Kings, Queens and GenAI Dragons

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https://doi.org/10.55982/openpraxis.16.3.739

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GenAI in Teaching and Learning Toolkit Subtitle: The Least You Need To Know as Educators

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GenAI in Teaching and Learning Toolkit Subtitle: The Least You Need To Know as Educators

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GenAI in Teaching and Learning Toolkit – Simple Book Publishing

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This toolkit is designed to inspire and support educators in understanding and integrating Generative AI (GenAI) tools within their teaching practices. The toolkit covers a wide range of relevant topics, from basic understanding and exploration of GenAI tools to ethical considerations and some practical implications in teaching. The toolkit also offers some resources that educators can delve deeper into the topics. However, the main purpose of the toolkit is to offer a beginner, a novice, someone still reluctant or fear to try or learn about those tools space to reflect and get started with some foundational practical and critical comprehension of GenAI, fostering informed-decision-making in its application within their educational contexts.

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https://www.edtechinsiders.ai/

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GitHub - ncwilson78/System-Prompt-Library: A library of shared system prompts for creating customized educational GPT agents.

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A library of shared system prompts for creating customized educational GPT agents. - ncwilson78/System-Prompt-Library

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AI chatbots like OpenAI's ChatGPT have kicked the hornet's nest in education. From articles declaring the "end of everything" (writing, the English classroom, the entire education system...) to claims that AI will revolutionise education, we've seen the full spectrum of hype.

What we're yet to see much of, however, are practical solutions for dealing with these new technologies. I'm a PhD student, studying AI in education. I'm also a former secondary school teacher, Director of Learning and Teaching, and Head of English. As part of my consulting, I go into schools every week and hear about the challenges of AI and I write a lot about how AI is being adopted in schools. I turned one of my blog posts, Practical Strategies for ChatGPT in Education into a PD which has now run for over 800 educators in secondary and tertiary, online and face to face.

One area where AI is causing a significant stir is assessment. Amidst all of the end-of-everything narrative and fears about students becoming compulsive plagiarists, some schools and universities are trying to adopt sensible approaches to incorporating AI into their assessment tasks.

I've been paying close attention to how people are using AI like ChatGPT. This blog post introduces the AI Assessment Scale, a five-point scale that can help educators determine the level of AI involvement in their assessments.

The AI Assessment Scale: A Closer Look

The AI Assessment Scale ranges from "no AI" to "full AI" and encompasses different levels of AI integration. Here's a breakdown of the five-point scale:

1. No AI: The assessment is completed under supervision, and/or handwritten, and/or under exam conditions. This level is suitable for testing knowledge and comprehension. For example, a traditional multiple-choice exam or an in-class essay written without the use of AI tools.

2. Brainstorming and ideas: AI can be used in the initial stages of the assessment for brainstorming and idea generation, checking ideas, etc. This level is suitable for assessments where students need to demonstrate their writing skills, such as constructing their own essays. For instance, students might use AI tools to help generate ideas for a persuasive essay or research paper.

3. Outlining and notes: AI can be used to outline entire responses or convert notes (or audio transcriptions) into organised ideas. This level is suitable for assessments where the focus is on the final quality of the writing, word choice, and expression. For example, students might use AI tools to turn their handwritten notes into a cohesive essay outline or restructure their notes to create a more logical flow of ideas.

4. Feedback and editing: AI can be used to provide feedback, self-assessment, or editing and revision. This level is ideal when the assessment focuses on the quality of the ideas and understanding, independent of the quality of language and expression. Students might use AI tools to receive instant feedback on their draft essays or identify areas of improvement in their writing.

5. Full AI: AI can be used to generate the entire output. This level is suitable when the outcome of the assessment is judged on the earlier organisation, idea generation, discussion, orals, and other methods. For example, students might use AI to create a comprehensive summary of a group discussion or synthesise research findings into a cohesive report.

Let's take a look at each of those in more detail, with a few examples. A PDF of this information can be downloaded for free at the end of this article.

Benefits and Challenges of Incorporating AI into Assessments

Incorporating AI into assessments could several potential benefits, such as enhancing the creativity, writing quality, and efficiency of feedback processes and self-assessment. However, there are also challenges to consider. Ensuring that AI does not replace students' critical thinking and problem-solving skills is crucial, and something which I know from experience many parents are concerned about. The ethical concerns around AI-generated content and balancing the use of AI with traditional assessment methods must be addressed.

Applying the AI Assessment Scale to Non-Writing Tasks

The AI Assessment Scale could easily be applied to other types of assessments, such as oral presentations, group projects, and problem-solving tasks. Furthermore, it can be applied to other AI applications like image generation, data analysis, and virtual simulations. In each case, educators can use the five-point scale to determine the appropriate level of AI integration.

Think about how this Scale could be applied to a visual arts assessment, for example. Students might use no AI in the initial idea generation, and then supplement their brainstorming with an image generation app to look for inspiration - much in the same way students might already use apps like Pinterest. They could create digital artwork themselves, with the use of AI, or using AI to entirely generate the artwork and then editing it themselves.

Creating Clear Guidelines for AI Use in Assessments

The idea behind the AI Assessment Scale isn't to "catch" students in the act of cheating. In fact, it's getting increasingly difficult to even define what we mean by "cheating". The Scale should be used as a discussion point, and maybe as an addition to a task sheet to clearly indicate how students are permitted or encouraged to use AI for a given task.

It might also be appropriate to break a task down into different elements, and apply different levels of the Scale to different parts of the assessment. For example:

Your initial brainstorming must be done by hand, on paper, using your knowledge and ideas only. (Level 1)

Your ideas can then be refined and developed using AI and a thinking routine like Generate-Sort-Connect-Elaborate. (Level 2)

Once you have developed your initial ideas, your first draft outline should be completed by hand in class. (Level 1)

You may use AI to expand on your outline and suggest improvements to clarity, logic, and the overall structure. (Levels 3 and 4)

Once you have completed your draft writing, you may use AI to proofread and make recommendations. (Level 4)

Using ChatGPT with the Assessment Scale

Finally, here are a few examples of how a student might approach an assessment task using prompts in ChatGPT.

No AI

Sorry, no prompts here!

Brainstorming and ideas
Generate a list of features that a teenager might be looking for in a smartphone app that helps them track their environmental waste.
Here are three of my ideas about <topic>: <copy/paste ideas>. Add onto each of those ideas with three connected ideas each.

Outlining and notes
These are my notes from a lesson on <topic>: <copy/paste notes>. Turn them into an outline for an essay on the question <question> This is a transcript of an audio brain-dump. Turn the transcript into organised notes with headings and subheadings: <copy/paste transcript>
Feedback and editing
Role play: You are my secondary school Year 12 English teacher. You haven't had your morning coffee. Provide some brutally honest feedback about the quality of my arguments: <copy/paste draft> Check the grammar, spelling, and punctuation of this and make recommendations: <copy/paste draft>

Full AI
This is the audio transcript of a group discussion on <topic>. Use it to generate the outline for a PowerPoint, and the outline for a report. Use the outline for the report you created to write the full report. Begin with the first section and wait for our feedback.

If you found this post useful, please share it with your colleagues and consider joining my mailing list for future articles:
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Download a free PDF copy of the scale: I am working with a variety of schools on professional development and the creation of AI policies, including communications with parents and the community. If you would like to talk about AI in your school please use the contact form below:

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The AI Assessment Scale: From no AI to full AI

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AI chatbots like OpenAI's ChatGPT have kicked the hornet's nest in education. From articles declaring the "end of everything" (writing, the English classroom, the entire education system...) to claims that AI will revolutionise education, we've seen the full spectrum of hype. What we're yet to see much of, however, are practical solutions for dealing with...

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"AI detection software is far from foolproof—in fact, it has high error rates and can lead instructors to falsely accuse students of misconduct"

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"AI detection software is far from foolproof—in fact, it has high error rates and can lead instructors to falsely accuse students of misconduct"

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MIT Sloan Teaching & Learning Technologies

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AI Detectors Don’t Work. Here’s What to Do Instead. - MIT Sloan Teaching & Learning Technologies

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As new tools raise concerns around AI academic integrity, instructors can deter misuse through transparent policies, open discussions with students, assignments that engage intrinsic motivation, and inclusive teaching practices.

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We can’t detect our way out of the AI challenge

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We can’t detect our way out of the AI challenge

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We can’t detect our way out of the AI challenge

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Not surprisingly, tools for detecting material written by artificial intelligence have created as much confusion as clarity.

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Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).

Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It's an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools.

Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, models, lectures, references, new LLM capabilities, and tools related to prompt engineering.

Due to high demand, we've partnered with Maven to deliver a new cohort-based course on Prompt Engineering for LLMs (opens in a new tab) .

Elvis Saravia (opens in a new tab) , who has worked at companies like Meta AI and Elastic, and has years of experience in AI and LLMs, will be the instructor for this course.

This hands-on course will cover prompt engineering techniques/tools, use cases, exercises, and projects for effectively working and building with large language models (LLMs).

Our past learners range from software engineers to AI researchers and practitioners in organizations like LinkedIn, Amazon, JPMorgan Chase & Co., Intuit, Fidelity Investments, Coinbase, Guru, and many others.

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Prompt Engineering Guide | Prompt Engineering Guide

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A Comprehensive Overview of Prompt Engineering

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OpenAI quietly bins its AI detection tool – because it doesn’t work

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OpenAI quietly bins its AI detection tool – because it doesn’t work

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OpenAI quietly bins its AI classifer tool

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OpenAI, the creator of the world’s most famous large language model ChatGPT, has halted work on its AI detection tool ‘AI Classifier’.

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Simple techniques to bypass GenAI text detectors: implications for inclusive education

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Simple techniques to bypass GenAI text detectors: implications for inclusive education

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Abstract

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns. Results show significant reductions in detector accuracy (17.4%) when faced with simple techniques to manipulate the AI generated content. The varying performances of GenAI tools and detectors indicate they cannot currently be recommended for determining academic integrity violations due to accuracy limitations and the potential for false accusation which undermines inclusive and fair assessment practices. However, these tools may support learning and academic integrity when used non-punitively. This study aims to guide educators and institutions in the critical implementation of AI text detectors in higher education, highlighting the importance of exploring alternatives to maintain inclusivity in the face of emerging technologies.

Introduction

Recent developments in Artificial Intelligence (AI) have led to paradigm-shifting applications and technologies which have significantly impacted multiple aspects of society. The 'AI Spring' (Manyika & Bughin, 2019) of the early 2020s and its ongoing consequences have already garnered a great deal of attention in academia and education in a short span of time. At the time of writing, AI is receiving greater adoption both formally and informally among students and teachers and has become a 'hot topic' (Cotton et al., 2023), of which our understanding is only just beginning. Generative AI (GenAI) tools, that is, those which produce an output of some form in response to user input, whether text, audio, image, or video, have attracted the most attention in this area. Despite many proclaiming the potential benefits of these tools in aiding learning, GenAI also presents several risks, including equity and inclusion in education. For example, students and scholars with access to paid GenAI tools may reap more benefits than those who cannot afford subscription fees, thus impacting educational equity. Furthermore, punitive policies against GenAI use at universities and schools may disproportionately affect those who require assistance with academic writing, thereby affecting inclusion. Despite such risks, a consensus on the acceptance of GenAI in some spheres of academia is emerging. For example, many academic publishers have moved towards a position which accepts the use of GenAI in crafting manuscripts, albeit with certain restrictions (Perkins & Roe, 2024a), and studies have shown the rising popularity of using GenAI tools to support academic writing and research (Bedington et al., 2024; Chan, 2023; Perkins & Roe, 2024b; Sobaih, 2024). Simultaneously, a growing number of organisational bodies are now producing guidelines for the ethical use of GenAI in education (Foltynek et al., 2023; Miao & Holmes, 2023).

Regarding inequality and inclusion, the consensus acceptance of GenAI tools in scientific publishing may disadvantage certain groups of students and researchers. For example, barriers in access to the internet, financial barriers in accessing paid GenAI tools (as premium versions often require a subscription), and other issues of access (for example, disability) all play a role in worsening 'digital poverty' (Miao & Holmes, 2023). Further, Bissessar ( 2023) also identifies the digital divide as an important consideration in the use of GenAI tools in classroom settings and Liang ( 2023) recognises that GenAI tools are known for producing structurally biased and ethnocentric worldviews.

This is not the only concern regarding GenAI technology in HE. Shortly after the rapid increase in the popularity of GenAI tools caused by the release of ChatGPT in November 2022, detection tools were released commercially, claiming that textual GenAI outputs could be detected. We refer to these as GenAI text detectors. Many of these detectors claim a high degree of reliability in identifying the presence of AI-generated content in submissions, often giving either a percentage score (i.e. 'this text is 10% AI-generated') or a likelihood score (i.e. 'this text is likely to be AI-generated'). Underlying the principle of these detectors is the assumption that by identifying GenAI text, assessors and educators can ensure fair and secure assessment. Research has shown that the assuredness of fairness and equity is vital for inclusive assessment practices (Morris et al., 2019), and notions of fairness and security suggest that students will not be able to misrepresent authorship and thus gain educational outcomes that they have not earned.

However, research has shown that GenAI text detectors have the potential to be barriers to inclusive assessment practices by disproportionately targeting individuals who do not speak English as their first language, or those with lower English proficiency (Liang et al., 2023). In this study, we use the term 'Non-Native English Speaker' (NNES) to refer to this population. We recognise that there are various terms used to describe individuals who do not speak English as their first language, such as English Language Learner (ELL), English as a Second Language (ESL) speaker, and English as an Additional Language (EAL) speaker, with each term having its own merits and limitations. While NNES may be seen as problematic by some because it categorises people based on what they are not, rather than what they are, we have chosen to use this term in our study because our primary focus is on exploring biases specifically related to these groups, and in this context believe NNES to be the most accurate and direct term to describe our target population.

It has long been known that NNES face challenges when participating in science. These 'manifold costs' include spending a greater amount of effort and time reading, writing, and disseminating work (Amano et al., 2023). Even when NNES do not use GenAI tools, research suggests that they are likely to suffer false accusations. Liang et al ( 2023) found that GenAI text detectors falsely detect NNES writers' outputs as AI-generated due to the lower level of perplexity and lower coherence often found in NNES writing. As a result, linguistic measures, such as perplexity, as indicators of GenAI, are associated with bias against NNES (Liang et al., 2023). Following this, although GenAI detectors are borne from a place of good intention, that is, primarily to limit the ability of individuals to commit AI-enabled academic integrity violations and to understand how students use GenAI, and to ensure fairness and parity in assessment submissions, thus promoting inclusivity and fairness, they have unintended, paradoxical consequences. Most concerning among these is that they can be used to generate false accusations of academic dishonesty, create confusion or heightened anxiety among students, and disadvantage some groups of students (e.g. NNES) in comparison to others.

To assess whether the benefits GenAI text detectors provide to the educational process (ensuring fairness by limiting the scope of AI-enabled authorship misrepresentation) outweigh their potential to drive inequalities (false accusations of NNES, potential for students with technological ability, and financial resources to bypass detection systems), we investigate the efficacy of six GenAI text detectors. We do this by testing content subjected to prompting techniques that alter the standard output of GenAI tools to disguise that they are AI-generated texts. We refer to these manipulation techniques designed to evade detection as 'adversarial techniques' in reference to the combination of methods that have been referred to as adversarial attacks in the extant literature related to attacks on machine learning based tools (Alsmadi et al., 2021; Qiu et al., 2019; Sadasivan et al., 2023; Wang et al., 2023). This contributes to the understanding of the limitations of such AI text detector tools in light of their risks to inclusivity in educational practice, given that prior research has not investigated the impact of adversarial techniques on the ability of AI text detectors to maintain a reliable detection accuracy rate.

Our research takes place during a pivotal time in the release and adoption of AI and GenAI tools in education globally, during the first academic year following the release of the Foundation Model (FM) GPT-4 by OpenAI. This model represented a step-change in the abilities of GenAI to create text that was significantly more difficult to detect by educators (Fleckenstein et al., 2024; Perkins et al., 2023), and thus the technology began to draw greater attention in the public sphere, as well as in educational contexts. At the same time, this research takes place during an era in which there is a high degree of variability in awareness and understanding of GenAI, and a transitional period during which many instructors, students, and administrators have yet to receive training on the uses and limitations of AI tools.

Objectives

The overarching aim of this research is not to vilify the use of GenAI tools or AI text detectors, but to explore the efficacy and sensitivity of these tools to linguistic changes and adversarial prompting techniques, thereby assessing their ability or inability to foster an inclusive system of education. By 'inclusivity' we refer to providing equal opportunities to all who participate in the educational process and ensuring that no group of students is at a disadvantage through a lack of opportunity or unfair assessment process. By measuring the susceptibility of existing AI text detectors to various adversarial techniques and assessing their efficacy, we can draw conclusions about their suitability for use in higher education and their ability to enable inclusive practices by reducing the risk of academic misconduct or detracting from inclusivity through unreliability (thus leading to a higher likelihood of false positives). In doing so, this research aims to serve as a guide for educators and institutions regarding the use of AI text detectors.

To achieve these objectives, this study explores whether AI text detectors can be considered reliable tools which contribute to academic integrity and support in fostering inclusivity and fairness. Despite growing interest in AI text detectors, there is a lack of comprehensive studies that evaluate their performance against various GenAI tools and adversarial techniques, particularly in the context of educational inclusivity and equity. This gap in knowledge hinders educators' ability to make informed decisions about implementing these tools in higher education settings. Our study aims to address this gap by providing a systematic analysis of AI text detectors' efficacy and their potential impact on inclusive education practices.

Specifically, we aim to answer the following questions.

1. 1.
Reliability of AI text detectors: To what extent can we trust the results of AI text detectors to accurately determine the source of a piece of text? What implications does this have for inclusive assessment practices?

2. 2.
Adversarial techniques: Is it possible to disguise AI-generated content using adversarial techniques in prompting strategies and paraphrasing? What are the most effective adversarial techniques for deceiving text detectors? How might this advantage some writers over others?

3. 3.
Comparative analysis of AI tools: Which AI tools provide outputs that are easier or more challenging for detectors to identify? What are the implications of this?

4. 4.
Detector efficacy: Which AI text detectors show more promise in terms of accuracy and reliability? Is it possible to recommend any of them based on their performance?

Literature

Generative AI tools

In relation to GenAI, educators and researchers are currently grappling with the changes brought about by the popularisation and development of Foundation Models (FMs). These models share the fundamental capability of generating human-like texts in response to natural language prompts, a feature that enables a wide range of applications, and this study focuses on the outputs created by three popular FMs: GPT-4 by OpenAI (accessed through the ChatGPT interface), Bard (now known as Gemini) by Google, and Claude 2 by Anthropic. By manipulating FM text output, and evaluating the performance of AI text detection technology, we address an educational problem. Namely, that if such detectors can be evaded, this significantly affects their utility. AI text detectors will then not be able to ensure a fair, equitable protocol in AI-restricted assessments. Furthermore, despite having little utility, these detection technologies may negatively impact inclusion and equity by being biased towards certain student groups, such as Non-Native English Speakers (NNES). FMs are challenging because their output has shown a high level of accuracy when used to generate answers to educational assessment items. In an evaluation of GPT-4, Claude 2, and Bard's performance, Borji and Mohammadian ( 2023) identified GPT-4 as providing correct answers to standardised questions 84% of the time, while Claude 2 and Bard achieved scores of 64.5% and 62.4%, respectively. In the multiple-choice section of the Bar exam, Claude 2 excelled with a 76.5% accuracy rate, surpassing GPT-4's previous record of 75%. For tasks involving multistep reasoning, Fu et al. ( 2022) concluded that Claude 2's performance is comparable to that of GPT-4, with Lin and Chen ( 2023), claiming that Claude 2 slightly outperforms GPT-4. Despite these achievements, Claude 2 lags in scientific writing and quantitative accuracy compared to other models (Chang et al., 2023; Lozić & Štular, 2023; Z. Wu et al., 2023), primarily because of the ability of GPT-4 and Bard to access the internet for information gathering. Studies by Fu et al. ( 2022) and Lin and Chen ( 2023) have shown that Claude 2 can perform comparably to or even slightly outperform GPT-4 in multistep reasoning and dialogue.

Efficacy of GenAI text detection tools

There is a wide range of software available which has been designed to classify whether the text is machine or human-generated, with providers claiming high levels of accuracy in being able to identify whether the text is written by a human or by a GenAI tool (GPTZero, n.d.; Turnitin, 2023). While some of these tools are free and others require either registration or payment, research by Walters ( 2023) has identified that the accuracy of paid-for tools is only slightly higher than that of free versions. However, claims of accuracy are contradicted by studies which demonstrate the varied levels of the detectors' ability to distinguish accurately between AI and human-generated content. (Chaka, 2023a; Gao et al., 2022; Krishna et al., 2023; Orenstrakh et al., 2023; Perkins et al., 2023; Walters, 2023; Weber-Wulff et al., 2023).

Detection tool biases against NNES have been highlighted as a potential issue of bias by Fröhling and Zubiaga ( 2021) and Liang et al. ( 2023) because of their reliance on standardised linguistic metrics such as perplexity and burstiness, which can disadvantage NNES. Liang et al. ( 2023), identified that GPT-based detection tools misclassified over half of the samples from NNES, with an average false positive rate of 61.3%. However, research produced by GPTZero challenges Liang's findings and demonstrates that GPTZero can accurately determine the human written status of text when produced by an NNES (Tian, 2023). This claim is repeated in research produced by Turnitin who also shows no differences in how NNES writing is classified (Adamson, 2023). However, on a broader scale, questions remain, especially as OpenAI's AI classifier, although trained on varied human textual patterns, did not include training on NNES-generated text (Elkhatat et al., 2023; OpenAI, 2023). Owing to the inability of this classifier to accurately detect the output of GenAI tools, OpenAI withdrew this tool from use in July 2023 (OpenAI, 2023). Research by Originality.AI ( 2023) acknowledges that all AI text detectors have their limitations. These include lagging behind in training against recent GenAI tools, which have reached a level of complexity in producing content, making it very difficult to distinguish between AI and human-generated content, and they are susceptible to being bypassed by using adversarial techniques. These problems underscore the need for educational institutions to balance the use of AI detection tools with accommodating AI-produced materials (Perkins et al., 2023).

Adversarial techniques as a method of evading GenAI text detection

A few previous studies have investigated how the accuracy of AI text detectors can decrease following manipulation of the textual output of GenAI tools. Mitchell et al. ( 2023) identified that DetectGPT correctly detected 70.3% of model-generated sequences from GPT2-XL. However, after altering the content using an Automated Paraphrasing tool (APT), the detection rate decreased to 4.6%. Similar results of major drops in accuracy rates following the application of APTs and translation tools were identified by Weber-Wulff et al. ( 2023).

A key technique for reducing detection accuracy involves the deliberate incorporation of errors in AI-generated text (Perkins et al., 2023). This technique exploits the natural occurrence of minor mistakes in human writing, such as typographical errors, grammatical inconsistencies, and stylistic irregularities. By mimicking these imperfections, AI-generated content can effectively mislead detectors into classifying them as human-authored content. Another approach, as discussed by Liang et al. ( 2023), involves tailoring the complexity of a text to mirror the style typically found in human-produced academic works. This method focuses on adjusting language use and vocabulary to align closely with human writing styles. Thus, AI-generated content can blend more seamlessly with human-authored texts, thereby evading detection more effectively.

The role of prompting GenAI models ('prompt engineering') has also been emphasised as important when evading the detection of machine-generated text. As observed in studies by Elkhatat et al. ( 2023), carefully crafted prompts can guide GenAI tools in generating text that not only aligns with the desired content but also strategically incorporates elements that challenge the detection capabilities of AI detectors. This involves a nuanced understanding of the capabilities of AI and the limitations of detectors, ensuring that the generated content remains undetectable while retaining its intended meaning and coherence. Other techniques for evading the identification of machine-generated text have also been presented by Solaiman et al. ( 2019), Sadasivan et al. ( 2023), and Ippolito et al. ( 2020). These include "recursive paraphrasing attacks", which utilise automated, network-based paraphrasing tools to reduce the accuracy of detectors on watermarked texts, and "spoofing attacks" in which an adversarial human deliberately writes a passage falsely detected as AI-generated without having access to the inner workings of the detection methods. Lancaster ( 2023) proposed that watermarking AI-generated text may be a potential solution; however, the attacks discussed above demonstrate that even LLMs protected by watermarking schemes are vulnerable to manipulation of this type.

Research gap

Despite the growing interest and concern surrounding the reliability of AI text detectors and the effectiveness of adversarial techniques in deceiving them, there are no comprehensive comparative analyses that systematically evaluate the performance of various AI text detectors against different GenAI tools, nor are there any studies that do this while exploring the potential consequences from an inclusivity and equity perspective. Existing research points to a lack of holistic understanding of the interplay between AI text detectors, GenAI models, and adversarial techniques (Anderson et al., 2023; Chaka, 2023b; Elali & Rachid, 2023; Elkhatat et al., 2023; Liang et al., 2023; Orenstrakh et al., 2023; Perkins et al., 2023; Weber-Wulff et al., 2023). This research gap impedes our ability to provide robust evidence-based recommendations for educators regarding the use of AI text detectors. Gaining a more nuanced perspective on how accurate AI text detectors are in real-world settings means that we can highlight their potential benefits, limitations, and biases and therefore support a more inclusive educational environment in this new GenAI-infused era.

To support this goal, this study explores the effectiveness of different adversarial techniques in adjusting the output of GenAI tools to evade detection by AI text detectors. In doing so, we address the need for a standardised framework and taxonomy to compare and evaluate the performance of AI text detectors against GenAI tools and adversarial techniques (Abd-Elaal et al., 2022). With a growing number of GenAI tools available, each having unique characteristics, strengths, and weaknesses, students attempting to evade detection may employ various techniques to manipulate AI-generated text, making it less likely to be identified by text detectors. Understanding these manipulation techniques can help demonstrate the true effectiveness of AI text detectors in more realistic settings, where students actively try to avoid detection. This knowledge can inform recommendations for higher education institutions and academics regarding whether and how to effectively use these tools to support academic integrity. In addition, these insights can guide software developers in creating more robust detection methods to counter evasion techniques and further enhance the reliability of AI text detectors.

Methodology

Overview

This study employs an experimental design in which we use three popular GenAI tools to generate short samples of text (n = 15). Altered versions of the original samples are created by applying six adversarial techniques (n = 89). Ten human-written samples are used as controls. All the developed samples (n = 114) are tested against seven popular AI text detectors to determine the effect of adversarial techniques on the accuracy of AI text detectors (n = 805). Sample creation and testing were conducted in September and October 2023.

All prompts used to generate the samples, as well as the samples themselves, are publicly available at Mendeley Data. (Perkins, Roe, et al., 2024a).

Sample generation

GenAI sample generation

First, we generated an initial set of 15 text samples using three GenAI tools (GPT4, Claude 2, and Bard), with five samples created using each tool. All samples were created using the same five prompts designed to emulate a range of tasks in which GenAI tools may be used. These prompts requested the development of the following outputs.

Mini (short form) university essay testing AI's ability to construct coherent, argumentative, or exploratory work within an HE setting.

Professional blog post to assess whether GenAI-generated content can demonstrate professionalism, industry knowledge, and expertise while maintaining reader engagement.

Cover letter to apply for an internship designed to test GenAI's ability to design tailored content specific to a position and the suitability and motivation of the applicant.

Middle-school level comparative analysis task designed to test GenAI's use of language specific to that of a younger author requiring clarity and simplicity.

Magazine article intended to test for content and tone in a journalistic manner that may appeal to a broad audience.

The prompts were designed to create outputs which covered a range of different topics, linguistic styles, and complexity levels to provide insights into the versatility and consistency of AI detectors in producing results across different forms of text.

Ten control samples were created by the human authors (researchers in this project). The human authors are all proficient English speakers, comprising academic staff who described English as their primary language, and Vietnamese undergraduate NNES students who study at a UK-oriented international university in English.

Adversarial sample generation

All original 15 AI-generated samples were modified using six different adversarial techniques. Except for a separate APT tool to paraphrase the outputs, all adversarial techniques were applied using the same FM with which the samples were originally generated. This maintains the likely process of a user interacting with their chosen FM. This was performed by copying the sample to a new chat window in each tool and then using prompting strategies to request the text to be adjusted according to the requirements of each adversarial technique.

All adversarial techniques were applied following a standardised protocol. The testers were given a standard prompt format for application to each of the 15 samples. These prompts include specific techniques designed to improve the performance of GenAI tools. Examples include the use of delimiters (Sweenor & Ramanathan, 2023) and prompt chaining (T. Wu et al., 2022). Delimiters (in this case, hash marks and titles) were used to identify to ChatGPT where instructions ended, and the content for adjustment began. If a suitable output was not obtained on the first attempt, prompt chaining was used to request specific adjustments to meet the required output. Researchers were restricted to making up to five requests for new prompts to obtain suitable outputs. If a suitable output could not be created within these five iterations, the sample was not considered for testing. Any potential ambiguities in prompts were addressed by requesting the GenAI tool to ask any required questions before providing output. At no point were the text samples manually edited: all adjustments to the original samples were carried out using the relevant GenAI tool.

The format used for each adversarial technique is presented in Table 1.

Suitable outputs could not be obtained in one case, resulting in 89 samples of AI-generated text in which adversarial techniques had been applied. When creating the samples for testing, we considered using few-shot prompting techniques (in which samples are shown to the AI model, and then it is asked to imitate the process) to guide the AI models in generating more human-like text. However, we ultimately chose not to employ this method to minimise the influence of external text on the final output. By allowing the AI tools to apply adversarial techniques based on their own underlying datasets and processes, we aimed to demonstrate how each tool would uniquely interpret and execute these techniques, thereby providing a more authentic representation of their capabilities. Table 2 highlights key points related to the application of each technique.

Summary of sample generation

Owing to limitations inherent in some of the AI text detectors used, the length of the samples requested in all cases was restricted to approximately 500 words. This resulted in the final length of the samples for testing falling between 350 and 625 words.

Table 3 summarises the different samples generated.

Protocol

Testing protocol

Al samples created were tested against seven AI text detectors which had been previously identified in literature (Chaka, 2023a; Gao et al., 2022; Krishna et al., 2023; Orenstrakh et al., 2023; Perkins et al., 2023; Weber-Wulff et al., 2023) as being somewhat effective at successfully identifying GenAI content. The detectors were chosen based on a review of existing studies (Chaka, 2023a; Gao et al., 2022; Krishna et al., 2023; Orenstrakh et al., 2023; Perkins, 2023; Weber-Wulff et al., 2023) as follows:

Turnitin AI detector (Turnitin.com, 2023)

GPTZero (Tian & Cui, 2023)

ZeroGPT (ZeroGPT.com, n.d.)

Copyleaks (Copyleaks.com, n.d.)

Crossplag (Crossplag.com, n.d.)

GPT-2 Output Detector (OpenAI, n.d.)

GPTKit (GPTKit.com, n.d.)

To test the samples, the text was either copied and pasted onto online software tools or uploaded where this option was available. The tools used free versions of the software, where possible. However, Turnitin's AI detection software was used as part of the license held by the lead author's institution, and both GPTKit and Copyleaks required credits to be purchased to run the required number of samples. All 114 samples were tested using each of the six detection tools, resulting in a total number of 805 tests. Eight samples could not be tested using some of the tools, resulting in a total of 797 valid tests. The reasons for these failures were not explicitly provided by the software due to their proprietary nature. However, based on our observations, we speculate that these issues may have been caused by various factors including text length limitations, formatting incompatibilities or temporary server issues. These occasional failures in processing samples are a limitation of using such tool and highlight the potential challenges educators might face when implementing these detectors in real-world scenarios. The unpredictability in tool performance further underscores the need for caution when considering their use in academic integrity processes.

The results provided by the AI detection tools were recorded and interpreted based on a modified version of the protocol designed by Weber-Wulff et al. ( 2023) for comparison with the existing literature. All test results were classified on a scale as either Positive (P) or Negative (N) based on the accuracy of the tool in determining whether the output was written by a human or generated by a GenAI tool. The classification is presented in Table 4.

Interpretation protocol

As the AI text detectors all display their results differently, with some providing quantitative statements ("X% probability for AI") and some qualitative statements ("most likely human written"), we needed a way to standardise the output to allow for comparability. Therefore, we recorded the results based on the interpretation protocols shown in Table 5.

Accuracy and error analysis

After classifying the outcomes of the tools as (partially) true/false positives/negatives, we assessed the accuracy of the tools using three separate methods, as identified by Weber-Wulff et al. ( 2023). These included a binary classification in which the accuracy was calculated as the ratio of correctly identified cases to all cases, a semi-binary approach which allowed for the results of partially correct results to be awarded half scores, and a logarithmic approach in which scores increased as accuracy rates increased. We also report the mean values of the combined methods.

The calculations used for each test are presented in Table 6.

In addition, error analysis was conducted to assess false accusations and undetected cases, which are particularly relevant in ensuring an inclusive and non-discriminatory environment. Instances of AI detectors falsely accusing students of academic misconduct are not uncommon and cause concern regarding inclusivity, fairness, and ethical practice in education. Therefore, it is important to establish a threshold at which the AI-generated content detected by these tools is sufficient to level an accusation or to take disciplinary actions. For this calculation, we used a threshold value of 60% to represent a value higher than chance, and to recognise the low likelihood of a partially negative or unclear value resulting in a false accusation of a student (FAS):

Another type of error, which is the potential for AI detectors to fail to identify AI-produced texts, can lead to students who use AI for unauthorised content generation being awarded similar credit as honest ones. The likelihood of undetected cases (UDC) is calculated as follows:

Results

Baseline testing

The results of our baseline testing of 15 AI-generated samples and 10 human samples are shown in Table 7. This test aims to establish the abilities of AI detection tools to determine the authorship criteria of a given sample before any adversarial techniques are applied.

We wish to highlight that these results were accurate at the time of testing, but because of the evolution of both the FMs and AI text detection tools, these comparative results may not necessarily represent the current ability of any of the listed tools. The pace of growth and development in this field means that research must move quickly to keep up with new capabilities, while AI technologies and detection techniques maintain a cat-and-mouse like game of progression and detection. In our study we have attempted to provide sufficient documentary data of the dates, times, and techniques that we have used to answer our research questions, while pointing out that the results may not remain stable for a significant period of time. We encourage other researchers in this field to adopt similar practices of open data sharing, allowing for ongoing verification and extension of findings in this fast-moving area of study.

Testing shows that the AI detectors tested have a mean accuracy rating of only 39.5% when evaluating unmanipulated AI-generated content. Importantly, regarding the human-written control samples, only 67% of the tests were accurate, leading to significant concerns regarding the potential for false accusations from these tools.

Regarding the performance of individual AI detectors, Copyleaks displayed the highest sensitivity, being able to detect 64.8% of AI-generated texts, followed closely by Turnitin at 61%. The worst-performing detector was GPTZero, with a considerably lower accuracy rating of approximately 26%. Before the content was subjected to any manipulation, the GenAI tool that produced the most easily detectable text was Bard, with 76.9% of its outputs correctly identified as AI generated by the detector tools.

Accuracy of GenAI text detectors following the application of adversarial techniques

Next, the performance of the AI detectors was assessed based on their average accuracy rate when detecting manipulated outputs. Table 8 shows the accuracy of the tools when faced with non-manipulated AI content, the accuracy following the application of adversarial techniques, and the percentage drops in accuracy. All reported figures are the mean values.

A comparison of the tools showed reductions in accuracy with variations ranging from 1.5% to more than 42% when the outputs were subjected to adversarial techniques (mean value 17.4%). Copyleaks was the most accurate (58.7%), whereas GPT Kit was the least accurate (4.5%).

Turnitin, one of the most widely used platforms in HEIs, demonstrated the highest drop in accuracy (42.1%) when testing the manipulated output. As a result, despite having the second-highest accuracy in baseline testing, this detector only ranked 5th place out of the seven AI detectors in terms of accuracy following the application of adversarial techniques.

The effectiveness of each technique in reducing the ability of the AI-generated text to be detected is shown in Table 9, ranked by the overall percentage drop in accuracy after the application of each technique.

Regarding techniques that have a major impact on reducing text detectability, outputs produced with spelling errors (12.9%) or higher burstiness (15.9%) were almost undetectable. However, we recognise that an examination of many of the samples produced using the SE technique resulted in an output that would be very unlikely to be submitted by a student. Although they evaded detection, they would very likely receive poor marks in a real-world setting because of the high number of errors. Increasing the complexity of texts was the least effective adversarial technique, with only a marginal drop of 2% in the detectability rate. Overall, the application of adversarial techniques resulted in a 17.5% drop in accuracy compared to non-manipulated content.

Error analysis

Table 10 presents an analysis of the errors produced by AI detectors with a focus on false accusations against human-written samples and the proportion of undetected machine-generated samples.

Considerable disparities were observed across detectors in terms of false accusations. However, four of the seven detectors did not misclassify any of the human-written samples. Notably, despite detecting the highest proportion of manipulated text, Copyleaks possessed the highest likelihood of producing false results, with 50% of human-generated samples misidentified as AI-written. Regarding undetected cases, Table 10 also reveals a major risk to academic integrity from the use of adversarial techniques, with six out of seven detectors having a UDC ratio exceeding 50%, and even the highest-performing tool (Copyleaks) failing to identify 39% of AI-generated cases.

Comparative performance of GenAI tools

As discussed earlier, the GenAI tools (Bard, Claude 2, GPT-4) chosen to generate the samples and apply adversarial techniques possess inherent differences in their capabilities, which stem from the different architectures used, and sizes of the training datasets employed. Table 11 highlights some noticeable features regarding the differences between these tools and their potential impact on how well they were able to reduce the detectability of the original samples.

Overall, we observed that the reduction in detectability was highest when applying adversarial techniques using Bard, with a mean reduction in accuracy of 38.8%. Claude 2 and GPT-4 ranked very closely, at 8% and 7.6%, respectively. However, this must be considered in light of the high accuracy of the AI detectors in correctly identifying the output from Bard in the original AI-generated samples. Therefore, in these cases, adversarial techniques had an oversized impact on reducing the detectability of the Bard output. It is also notable that the three GenAI tools exhibited unique patterns in performance when applying different adversarial attacks. For example, outputs from Bard and Claude 2 showed reductions in detectability when all techniques were applied, whereas applying certain techniques in GPT-4 resulted in an output that was easier to detect. This suggests that the choice of the GenAI tool has an impact on which adversarial techniques might be chosen if reducing the detectability of the text is the goal.

Discussion

Accuracy and vulnerabilities of GenAI text detectors as a barrier to inclusivity

In our baseline testing protocol of both non-manipulated AI-generated samples tested alongside the human-written control samples, we see an initially lower-than-expected average accuracy rating for the detection of AI-generated content, coupled with a substantial rate of false accusations in the human-written control samples. When the AI-generated samples were subjected to manipulation, significant vulnerabilities in accurately detecting text were observed. If the goal of implementing AI detection tools as part of an overall academic integrity strategy is to support academic staff in identifying where machine-generated content has been used and has not been declared, these inaccuracies may lead to a false sense of security and a broader reduction in assessment security. As assessment security is a key component in ensuring inclusive, equitable, and fair opportunities for learners, this is problematic. The varying degrees of reduction in accuracy following the application of adversarial techniques also point to the broader issue of inconsistency and unpredictability in the current AI detection capabilities. The effectiveness of these techniques varies dramatically across detectors, suggesting that the internal algorithms and heuristics of these detectors are tuned differently and react distinctively to similar inputs. Therefore, the results even within an institution may differ depending on the tool being employed and how it is being used.

Effectiveness of adversarial techniques

When exploring individual adversarial techniques, we see that relatively simple manipulations of content (such as the addition of spelling errors and increases in burstiness) are highly effective in evading detection, highlighting the limitations of AI text detectors in distinguishing between human-like irregularities in text production and actual human writing. The least effective technique, 'increase complexity', had a minimal impact on detectability, which implies that dense and complex text alone is not a sufficient criterion for AI-generated content to pass undetected.

The results also indicated a significant variance in the detectability of content produced by different GenAI tools, with the output from Bard being easier to detect than Claude 2 and GPT-4. This suggests that not all GenAI outputs are equally recognisable by AI detectors, which has implications for their use in educational settings. The lower detectability of the content generated by Claude 2 and GPT-4 could make them more appealing to those who intend to circumvent academic honesty policies. Claude 2's superior performance in evading detection across all categories suggests that its outputs might align more closely with the nuanced and variable patterns of human writing or that it may be better at producing more human-like text in its original outputs.

Comparison with prior studies

To contextualise our findings within the existing literature, we compared our results to those of previous studies. In comparison to Weber-Wulff ( 2023) et al.'s testing of detection tools, we noted that AI detectors performed significantly worse in our study, even with unedited text. In their analysis, all detectors achieved accuracy rates of less than 80%, but five scored above 70%. However, in our baseline testing, no detectors achieved such high accuracy. Furthermore, while Weber-Wulff et al. ( 2023) found that Turnitin's detector was the most accurate, our results placed Turnitin at number two, with Copyleaks showing greater accuracy. The application of machine translation in their study led to a 20% reduction in accuracy, whereas human manual editing reduced accuracy to approximately 50%, and machine translation (paraphrasing) reduced the overall accuracy to 26%. Our results showed similar impacts, with paraphrasing as a technique that reduced accuracy by 21%. In reference to the findings of Elkhatat et al. ( 2023) and Perkins et al ( 2023), we also noted a high degree of variability and low consistency when dealing with GPT-4 content. Our findings also corroborate Chaka's ( 2023a, 2023b) and Walters' ( 2023) research, demonstrating that Copyleaks appears to be the most accurate of the current generation of GenAI text detectors (despite a high FAS rate), with Turnitin also performing relatively well compared to other detectors. These results contribute to the body of knowledge about the variable accuracy of these tools and highlight the potential for inequity in educational assessments across cohorts if detection software is used.

Implications of adversarial techniques for inclusive education in the age of AI

The effectiveness of adversarial techniques in decreasing the likelihood of detecting machine-generated text raises concerns about both academic integrity and inclusivity. The current ability of GenAI tools to generate content that closely resembles human writing poses a significant threat to the fairness and authenticity of academic assessment. If students were to use AI tools to produce their academic work using these adversarial techniques, this would undermine the educational value and compromise the integrity of the assessment process, thereby benefiting some groups of students over others, especially those with access to paid GenAI tools and technical and procedural knowledge. Simultaneously, the fact that detectors may pose additional risks to NNESs that may produce text with less burstiness suggests that the risks of AI detection outweigh the potential benefits by exacerbating the existing inequities in academia. For instance, students with access to advanced AI tools and knowledge of adversarial techniques could gain an unfair advantage over their peers, further widening extant digital inequalities and the digital divide (Lutz, 2019).

The high level of variability between different detectors suggests that if institutions or academics take an individualistic approach to using these technologies (e.g. different academic staff at the same university using different GenAI detection technologies), the rates of efficacy would vary. Educators therefore need to be cognizant of the fact that while we focus on problematising text-detection software from a perspective of inclusivity, too much focus on GenAI as an emerging risk to academic integrity (Cotton et al., 2023; Perkins, 2023) may lead us to forget that extant threats to inclusivity and educational equity, such as contract cheating or 'traditional' plagiarism, have not disappeared.

Error analysis provided insights into the risk of undetected cases and false accusations. With the rate of false accusations at 15%, considering the major impact that this could have on student outcomes, we consider this to be a major concern for student equity. Although some detectors did not have any false accusations, this appeared to come at the cost of a higher UDC ratio, indicating that many instances of AI-generated content could go unnoticed, potentially providing an unfair advantage to dishonest students who can apply these adversarial techniques in a matter of seconds to hide the true source of text.

The use of GenAI text detectors in education

While GenAI tools have great promise in enhancing academic practices for both students and teachers alike, and most publishing houses now permit the transparent use of such tools (Perkins & Roe, 2024a; STM, 2023), there are circumstances in which the detection of GenAI texts remains necessary, such as in assessment practices which do not allow for the additional use of these tools or in computer-aided examinations. However, the fact that AI detection tools can be easily manipulated or bypassed using relatively simple adversarial techniques calls into question their viability as tools for maintaining assessment security.

Overall, our results demonstrate the challenges of current AI text detection tools being able to accurately determine whether a given piece of text was created by a human or a GenAI tool. This ability is further reduced when adversarial techniques are used to obscure the nature of a sample. If the goal of any given HEI was to use AI text detectors solely to determine whether a student has breached academic integrity guidelines, we would caution that the accuracy levels we have identified, coupled with the risks inherent in false accusations, means that we cannot recommend them for this purpose. This is not because of the demonstrated abilities of any one tool tested, as we recognise that developers are continuously updating these tools, and the detection of AI-generated content when subject to adversarial techniques is likely to improve. However, simultaneously, advances are being made in the development of more capable FMs that can produce more human-like content, resulting in a constant arms race between FMs and AI text detectors, with student inclusivity paying the price.

We believe that there will be a continued adjustment to GenAI-related learning and assessment practices within HE, with a move towards more acceptance of GenAI-enabled writing. Despite their shortcomings, AI text detectors may be part of a broader process of enabling discussions with students regarding the effective use of GenAI tools in academic writing. Students need experience and practice in learning how to work with GenAI tools, whether they are text-based or multimodal in nature. Being able to demonstrate to students how GenAI text may be integrated with their own writing is a task that these software tools may help with; however, this would only be possible if a non-punitive approach was taken to help students understand how GenAI tools can be used to support their own learning experiences.

GenAI and the future of inclusive education

Committing to inclusive methods of teaching, learning, and assessment demands a proactive and critical approach to integrating emerging AI technologies. Educators must continuously evaluate and adapt their practices to ensure that the adoption of new digital tools enhances rather than compromises inclusivity. This process requires ongoing scrutiny of how AI-related technologies impact diverse learners and a willingness to reimagine traditional educational paradigms in the face of rapid technological change. Similar to blended learning and communities of enquiry that contribute to the reorientation of the traditional classroom structure (Hilliard & Stewart, 2019), the presence of multiple forms of media on the Internet prompts us to reconsider the reliability and usefulness of source materials in the practice of academic argumentation (Radia & Stapleton, 2009). GenAI tools offer an opportunity to reconsider the traditional notions of misconduct and the potential barriers and inequities that a punitive approach to detection can face, particularly when relying on novel technologies.

New forms of academic fraud, such as online diploma mills, are growing (Roe & Perkins, 2023), and complex cases of fraud, such as contract cheating, continue to create unequal opportunities and outcomes (Curtis & Clare, 2024; Daly & Ryan, 2024); thus, efforts to promote inclusive forms of participation in higher education cannot solely focus on GenAI, as traditional, long-standing issues in equality of assessment have not gone anywhere, and some are even increasing.

Therefore, we need to foster alternative approaches to educational assessment which recognise the growing ubiquity of GenAI and account for obstacles to inclusive assessment designs. This requires fundamentally rethinking how we assess student learning, moving away from the traditional assessment methods that are easily compromised by AI tools. We would recommend that educators consider how GenAI tools can be more deeply integrated into learning and assessment so that students gain a deeper understanding of the practical and ethical applications of these technologies prior to moving into the workforce. Strategies such as the AI Assessment Scale (Furze et al., 2024; Perkins et al., 2024a; Roe et al., 2024), where the expected use of AI in any given assessment is clearly stated, could help guide educators in adapting assessment practices to align with the AI era, potentially leading to increased student outcomes and pedagogical redesign.

In this study, we sought to answer four fundamental questions. Namely, how reliable AI text detection technologies are, whether adversarial techniques reduce the accuracy detectors, which detectors are most and least effective, and whether any of these technologies demonstrate sufficient accuracy to warrant their deployment in education. Our findings demonstrate that AI text detection technologies are not highly reliable and are vulnerable to multiple adversarial techniques. Furthermore, we found that while some detectors are more accurate than others by orders of magnitude, none reach a level of accuracy above 73.9% for unedited output. In answering our final question, our findings do not support an overall benefit to users of AI text detection technologies considering the potential risks of false positives and their potential to disadvantage certain student groups.

Conclusion

The results of this study revealed that the average accuracy of AI text detectors in identifying non-manipulated AI-generated content was 39.5%, with a 67% accuracy rate for human-written control samples. When adversarial techniques were applied to the AI-generated samples, the average accuracy of the detectors dropped further to 22.14%, with some techniques, such as adding spelling errors and increasing burstiness, proving highly effective in evading detection. Error analysis also highlighted the risk of false accusations and undetected cases. These findings underscore the limitations of current AI text-detection tools in accurately determining the authorship of a given piece of text, particularly when faced with deliberate attempts to obscure the nature of the sample.

This study has several methodological limitations that should be considered when interpreting the findings and assessing their generalisability, which stem from both the choices made in the experimental design and the rapidly evolving nature of the AI landscape investigated. First, the scope of our research was restricted to a relatively small number of samples, three GenAI tools, and seven AI detectors which do not encompass the entire spectrum of possible writing styles, types of manipulation, or available tools to generate and detect samples. The pace at which both GenAI tools and AI text detectors are developed, and existing ones are updated means that our results are valid only for a specific snapshot in time. Therefore, the findings here should be viewed as indicative rather than exhaustive, and subsequent research would benefit from a broader evaluation of the currently available detection software, as well as considering how some of the newest FMs, such as Claude 3.5 Sonnet, ChatGPT-4o, and Gemini Pro 1.5, perform on these types of writing tasks.

Second, our experimental design did not mirror the iterative process that is likely to occur during the use of GenAI-supported academic writing and editing, particularly if there is a deliberate attempt to evade detection. In practice, students are likely to apply several of the techniques we have discussed and engage in continuous software-aided and manual refinement and adjustments, meaning that the ability of AI text-detection tools to detect this type of hybrid text would likely be even lower than demonstrated in the study. Future methodological designs should attempt to replicate the iterative nature of writing to provide more accurate assessments of AI detectors in academia as opposed to experimental settings.

Finally, some of the samples generated after applying adversarial techniques for testing may not accurately represent the quality of work that students would submit in a real-world setting. Although these samples evaded detection by software tools, they are likely to evoke suspicion from human markers because of their poor quality, strange phrasing choices, and excessive errors. This was particularly the case for some of the samples generated by either Claude 2 or Bard when asked to add spelling errors. Again, a broader sample of real-life cases would help explore this in more detail.

Overall, these findings demonstrate that the use of GenAI text-detection software has important ramifications for inclusivity, equality, and integrity in the AI era. Such tools are based on the assumption of assuring assessment fairness, which is a key principle in inclusive assessment design (Morris et al., 2019). However, our results showed that detectors are highly sensitive to the application of adversarial techniques, which may represent a more pragmatic view of how GenAI tools are used in academic writing. The implications of this are that, first, those with technological sensibility and resources, as well as inclination, will be able to disguise GenAI content relatively easily for the purpose of misrepresenting authorship. This reduces the equality of assessment (or publication of research) and thus, advantages some groups over others, acting as a barrier to inclusion. Furthermore, the potential for such tools to unfairly penalise students who write at a lower level of perplexity (i.e. a less complex textual structure) (including NNESs or lower-proficiency English speakers) suggests that, at the present time, the use of GenAI detection software for the identification of academic misconduct may produce barriers to inclusive assessment, rather than reduce them.

The major implication of these findings for educators and administrators is that the use of AI text detectors should not be implemented uncritically; users must consider the impacts and limitations of AI text detection technologies before using them for assessment or educational practices. Secondly, the results of such technologies should not be used for punitive actions or in accusations against students without a high degree of certainty, and those in the position to evaluate the results from such technologies need to consider the ease with which detectors can be evaded, and their potential to inequitably impact certain student populations. Finally, the findings imply that educators must radically reconsider their assessment structures and practices in light of new technology, given that current efforts to detect GenAI content are unlikely to be successful.

Data availability

The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/datasets/xv6fk2mmh9/3.

Abbreviations

AI:

Artificial intelligence

NNES:

Non-native English speaker

ESL:

English as a second language

APT:

Automated paraphrasing tool

FAS:

False accusation of a student

SE:

Add spelling errors (adversarial technique)

IC:

Increase complexity (adversarial technique)

PR:

Paraphrase (adversarial technique)

GenAI:

Generative AI

FM:

Foundation model

ELL:

English language learner

EAL:

English as an additional language

HEI:

Higher education institution

UDC:

Undetected cases

IB:

Increase burstiness (adversarial technique)

DC:

Decrease complexity (adversarial technique)

Acknowledgements

The authors would like to acknowledge the contributions of Viet Anh Nguyen, who assisted with reviewing the literature, providing samples of human written text, and performing tests.

Author information

Authors and Affiliations

1. British University Vietnam, Hanoi, Vietnam

Mike Perkins, Binh H. Vu, Darius Postma, Don Hickerson, James McGaughran & Huy Q. Khuat

2. James Cook University Singapore, Singapore, Singapore

Jasper Roe

Contributions

MP conceptualized the study, designed the methodology, and was a major contributor in writing and revising the manuscript. JR contributed to the study conceptualization, and writing of the manuscript. BHV contributed to the data curation, investigation, and review and editing of the manuscript. DP contributed to the investigation and data curation. DH contributed to the investigation and data curation. JM contributed to the investigation. HKQ contributed to the investigation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mike Perkins.

Ethics declarations

Competing interests

Not applicable.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used Claude 3 and GPT-4 to develop draft text, revise wording and adjust text throughout the manuscript. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Simple techniques to bypass GenAI text detectors: implications for inclusive education - International Journal of Educational Technology in Higher Education

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This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns.

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Generative AI technologies, unlike previous technologies, can mimic human language and create new content. This rapidly evolving technology can have wide-reaching effects.

We have as our ultimate goal in developing this AI literacy framework the advancement of teaching and learning, fostering personal growth, and inspiring committed action in the service of all members of our communities. We should use this technology to enhance learning and our human capacities, not replace them. We intentionally pursue a human-centered approach.

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Anatolia College Libraries Guide for AI for Education

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Anatolia College Libraries Guide for AI for Education

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Anatolia College Libraries: Artificial Intelligence for Education: Home

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Anatolia College Libraries: Artificial Intelligence for Education: Home

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Students' Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language

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Students' Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language

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It is generally accepted that technology is a great facilitator of the language learning and teaching process, and two emerging technological trends include the use of digital tools and artificial intelligence (AI) ( Jomaa et al., 2024; F. Xiao et al., 2025). Many different digital tools are available, and language learners and educators use them in a variety of ways. Digital tools may include a range of items, including digital flashcards, individual and group games, presentation software, learning management systems, various language learning platforms, social media, and many others. Digital tools used by students may include quiz applications to support vocabulary acquisition (Quizlet, Quizizz, Kahoot), puzzle applications to support vocabulary (web-based minigames, interactive jigsaw puzzles, and card design games), and grammar learning and platform applications (learning management systems such as DynEd, Edmodo, Moodle, Virtual Room) to support reading and writing development ( Lim & Toh, 2024). One popular digital tool used in language learning is Google Translate. It has been shown that EFL (English as a foreign language) learners use Google Translate for autonomous language learning through a combination of translation, text-to-speech synthesis, and automatic speech recognition ( van Lieshout & Cardoso, 2022).

While digital tools have been used for several decades, generative AI (GenAI) tools, including ChatGPT, are relatively newer. ChatGPT is "a powerful text-generating dialogue system" that "generates human-like responses to inputs from users" ( Mehta, 2023). Since ChatGPT was made public in November of 2022, it "quickly went viral on social media as users shared examples of what it could do[:]...everything from travel planning to writing fables to cod[ing] computer programs" ( Marr, 2023). The ways that teachers and students use GenAI tools are still developing.

ChatGPT shows many potential benefits to student learning. It can provide a personalized educational experience that is often impossible to achieve in a traditional classroom setting, which may be too expensive for many ( Y. Xiao & Zhi, 2023). The chatbot can act as a personal tutor or mentor that can answer questions, give easy-to-understand explanations of complex concepts, and engage students in conversations on various educational topics ( Baidoo-Anu & Owusu Ansah, 2023; George et al., 2023). AI chatbots can be used as conversational partners to correct students' mistakes in a target language. The independent learning opportunities that ChatGPT and other chatbots provide may be superior to the kind of independent learning that students have already been practicing with the internet for years. AlAfnan et al. ( 2023) mentioned that the answers to questions that are put to ChatGPT are obtained more quickly and succinctly than from search engines. This efficiency allows students to save time and energy ( Zhao et al., 2024). Kohnke et al. ( 2023) have shown the usefulness of ChatGPT to assist reading comprehension, and their research showed that ChatGPT can explain terms to the students, and students can ask for explanations in their first language (L1), as well as definitions, examples, and sample sentences. Li et al. ( 2023), in their investigation of ChatGPT use among language learning communities on YouTube, found a positive reception to incorporating ChatGPT into language learning. The YouTube communities reported that ChatGPT can provide concise responses, which can help language learners save time. Furthermore, ChatGPT can offer relevant examples, practice questions, and explain vocabulary, grammar, and cultural nuances. It was also reported that ChatGPT can benefit not only EFL learners but also help students who want to learn languages with high resources on the internet.

The potential benefits of digital tools and AI use in education are exciting for many and promise to revolutionize education in the near future. However, as many experienced language teachers have undoubtedly noticed, technology can complicate the learning process. A familiar example for many language educators is machine translation (MT) technologies such as Google Translate ( Shadiev et al., 2024). While MT provides many benefits in aiding understanding and expression in a foreign language at an efficient speed, students can use MT technologies to feign language skills that they do not possess, particularly in writing. For example, a student in an English for Academic Purposes (EAP) course could write an assigned essay in their first language (L1), use MT to translate it, and submit the translated product as their work ( Almusharraf & Bailey, 2023). Ducar and Schocket ( 2018) go as far as to suggest that teachers may view foreign language "students' reliance on [MT] [as] an absolute evil", though they rightly advise teachers to learn to work within the reality of MT's existence as it is unlikely to disappear. Similarly, ChatGPT and other GenAIs can be used to create text with minimal effort on the part of the student, which many teachers would consider academically dishonest. Indeed, Egloff ( 2024) stated that the ability of "AI tools [to] generate full essays at the click of a button is a major cause of alarm and source of anxiety for educators".

Educators are concerned about ChatGPT's impact on academic honesty, since its misuse may interfere with learning and assessment ( Alghannam, 2024; Liu & Wang, 2024; Tram et al., 2024). Farrokhnia et al. ( 2024) claimed that ChatGPT could be a threat to academic integrity, perpetuating discrimination in education, democratizing plagiarism, and decreasing high-order cognitive skills. Sullivan et al. ( 2023) conducted a research review on ChatGPT and academic integrity. Their systematic search indicated that the most common themes that arose in the data collected were general concerns about academic integrity and cheating. ChatGPT is designed to generate responses based on prompts, and students could submit generated responses as their own work and ideas ( Cotton et al., 2024). As chatbots become more advanced and sophisticated, there is a possibility that AI tools will be used more frequently, and students may use chatbots to cheat on assessments, thereby harming students' education, development, and growth ( Qadir, 2022). AlAfnan et al. ( 2023) stated that students may have chatbots write assignments for them to avoid a late submission grade or a zero grade.

Clearly, the stakes for uncontrolled AI use in education are high. Yet before educators rush to create policies to control its use or begin to have conversations with students to raise their awareness about appropriate AI use, it would be advisable to find out what the students themselves think about GenAI use in education. Understanding how rural, STEM-oriented students-especially those in an EMI setting-perceive the use of AI tools in academic writing and how these perceptions challenge prevailing narratives of AI use in an EFL context will be helpful. Given the current popularity of GenAI tools, this study is crucial. It aims to explore Ecuadorian EFL students' perceptions of GenAI tools, specifically ChatGPT, within the context of academic writing. Through a structured survey, this study investigates students' definitions of AI-related academic misconduct, their motivations for using AI in their writing, and their perceptions of its impact on their learning process. Additionally, it examines students' views on AI detection tools, institutional responses to AI-based dishonesty, and the potential role of AI as a support mechanism for academic writing. The findings seek to provide information to educators and policymakers to assist the development of policies and practices to effectively integrate AI literacy into the curriculum while fostering academic integrity and critical thinking in an increasingly AI-driven educational landscape. This paper is a revised and expanded version of the paper entitled "Students' Perceptions of Generative AI Use in Academic Writing", which was presented at the 17th International Conference "Innovation in Language Learning", Florence, Italy, 7-8 November 2024 ( Nelson et al., 2024).

2.1. Academic Dishonesty and Plagiarism

Kibler ( 1993) defined academic dishonesty (AD) as "...forms of cheating and plagiarism that involve students giving or receiving unauthorized assistance in an academic exercise or receiving credit for work that is not their own." AD is any act that tarnishes the integrity of educational systems. Examples of AD include "lying, cheating on exams, copying or using other people's work without permission, altering or forging documents, buying papers, plagiarism, purposely not following the rules, altering research results, providing false excuses for missed tests and assignments, making up sources, and so on" ( Lambert et al., 2003). Cheating is more likely to happen when the means of opportunity, incentives, and rationalization are present ( Humbert et al., 2022). Academic pressure also increases academic cheating behavior, and there is a correlation between greater opportunity and greater desire to cheat or take shortcuts, especially when there is low supervision ( Puspitosari, 2022). Heavy academic workload, combined with time constraints, may lead to cheating ( Abbas et al., 2024). Sources of academic pressure include fear of academic failure, academic procrastination, the demand for good grades, academic competition, and academic dissatisfaction ( Lhutfi et al., 2021). Additionally, the desire to save time, combined with easy access to the internet, may tempt students to find materials and copy, especially if students believe there will be little to no punishment for being caught ( Park, 2017).

Pecorari ( 2013) defines plagiarism as "the reproduction or paraphrasing, without acknowledgement, from public or private (i.e., unpublished) material (including material downloaded from the internet) attributable to, or which is the intellectual property of, another including the work of students" (p. 9). Plagiarism involves the appropriation of existing material to create new text, which constitutes a violation of academic integrity. A special case of plagiarism that is relevant in EFL and ESL settings is cross-language plagiarism. In cross-language plagiarism, a text is found in one language, copied, machine-translated into a target language, and presented as one's own work ( Dinneen, 2021). For instance, a researcher could have a paper translated from Indonesian into English, edit the English version several times, and submit the plagiarized paper to local publishers ( Ratna et al., 2017). With the number of available texts on the internet combined with free MT services, it is easy to make an "original" study ( Zubarev et al., 2022). Alotaibi and Joy ( 2021) stated that cross-language plagiarism is difficult to detect as each language has its own structure.

2.2. Academic Dishonesty, Technology, and AI-Related Plagiarism

The prevalence of plagiarism has increased with the rise of the internet ( Carter et al., 2019). Urlaub and Dessein ( 2022) stated that mobile technologies have led to the popularity of Google Translate, which has transformed language communication. When Google Translate first became available, its use raised concerns in educational settings about whether it could hinder students' ability to produce written work without relying on it. ChatGPT is being compared to Google Translate when it first premiered. ChatGPT is changing how plagiarism can be carried out ( Azoulay et al., 2023). When a student uses ChatGPT to write an essay and submits the results as their own work, it constitutes "contract cheating" akin to submitting a custom paper that was written by someone else ( Anson, 2022). Some researchers argue that academic dishonesty is not new nor inherently caused by technological advancements. Instead, academic dishonesty (driven by new technologies) is like "old wine in new bottles", and digital tools simply facilitate previously existing behaviors ( Selwyn, 2008). Regardless, the misappropriation or claiming of someone else's intellectual property, ideas, or writing as original constitutes plagiarism in higher education ( Carroll, 2013). Academic writing requires citing the original author's ideas, concepts, and theories, and ChatGPT is not transparent about where its sources come from, making it difficult to credit authors, and this lack of transparency may lead to challenges with academic integrity ( Koos & Wachsmann, 2023). Many teachers believe that students frequently use AI for assignments ( Delello et al., 2025).

The vast amount of information available online, combined with the ability to share and manipulate information digitally, has significantly increased opportunities for plagiarism ( Chiang et al., 2022). There are new concerns about ChatGPT and AI-assisted cheating Lo ( 2023). Teachers are now facing "AI-related plagiarism" or AI-giarism ( Ghounane et al., 2024). AI-giarism was coined by Chan ( 2023), and it involves "the unethical practice of using artificial intelligence technology, particularly generative language models, to generate content that is plagiarised either from original humanauthored work or directly from AI generated content, without appropriate acknowledgement of the original sources or AI's contribution" (p. 4).

Henry ( 2023) suggested that ChatGPT can "even generate authentic-sounding personal reflections on a given scenario", and it is difficult to determine whether the author of a document is a human or a chatbot ( Chatterjee & Dethlefs, 2023; Salvagno et al., 2023). Brainard ( 2023) stated that ChatGPT can create well-informed reports, essays, and even scientific manuscripts. Mijwil et al. ( 2023) prompted ChatGPT to write a science article, and they concluded that the technology behind ChatGPT will only continue to grow and will become more responsive and human-like. Even ChatGPT users have reported that the tool can be misused for educational tasks ( Haque et al., 2022). Although ChatGPT's writing style lacks nuance, style, and originality ( Salvagno et al., 2023), it is still like letting a student have a friend take the exam on their behalf ( Stokel-Walker, 2022). Thorp ( 2023) warned us that many AI-generated texts will soon appear in scientific literature, which is dangerous as trust in science decreases. Dergaa et al. ( 2023) suggested that generative AI will impact all stages of the scientific publishing process, given the fact that chatbots can generate all the parts of a manuscript, like the research questions, hypotheses, and methodology. Chen ( 2023) stated that ChatGPT can be used to help overcome language barriers, such as translating Chinese text into English. Since English is the primary language used in the sciences, and many scientists do not speak English as their first language, these authors may be less proficient in writing journal articles.

Students may have ChatGPT generate a paper for them instead of writing it themselves. In doing so, students misrepresent their work, and they can damage the credibility and reputation of both the student and the educational institution ( Leong & Bing, 2025). Academic dishonesty is such a grave issue because it ultimately "defrauds those who may eventually depend upon [graduates' supposed] knowledge and integrity" ( Pavela, 1997). While students may have a clear understanding of traditional plagiarism, they may have less understanding of the implications of AI-generated content in academic work ( Chan, 2025). Teachers need to prevent cheating in all its forms to ensure that students develop into ethical professionals ( DiVall & Schlesselman, 2016). Unfortunately, teachers may struggle to determine whether a text was created by a student or ChatGPT, making it difficult to evaluate students' comprehension accurately ( Perkins, 2023). This begs the question-can we detect AI-generated content?

Teachers and students may be familiar with tools, such as Turnitin, that detect copy-and-paste-type plagiarism from texts that are available on the web. Unfortunately, detecting generative artificial intelligence content is not that simple, and there is disagreement about the reliability of existing AI detectors. Alexander et al. ( 2023) stated that neither humans nor AI detectors are reliable when it comes to determining if AI wrote a text. Gao et al. ( 2023) stated that when a person copies and pastes a generated abstract and then edits it, thereby making it a human-AI-generated product, human reviewers will only correctly identify abstracts as being generated by ChatGPT 68% of the time. Gorichanaz ( 2023) stated that using AI to rewrite itself can deceive AI detectors because the person can ask ChatGPT to change sentence types and lengths, inserting typographical errors on purpose. Clark et al. ( 2021) found that untrained evaluators could not distinguish between human- and GPT3-generated texts (stories, news articles, and recipes). In their study, Khalil and Er ( 2023) had ChatGPT generate 50 essays, which they then sent to a plagiarism detection software. Of the essays inspected, the software considered 40 essays to contain a high level of originality-a similarity score of 20% or less. Foster ( 2023) submitted GPT-4 writings to Turnitin's AI detection model. By repeatedly changing the prompts, the author created a prompt that resulted in a ChatGPT output, which Turnitin inspected as 'original'. With time and effort, it is possible for a 100% AI-generated essay to score 0% plagiarism on Turnitin's AI detection. AlAfnan and MohdZuki ( 2023) stated that Turnitin has announced that they have been working on AI detection software. However, as Turnitin develops its software, Open AI develops newer and more advanced versions of ChatGPT. Rudolph et al. ( 2023) stated that teachers worry that students will not complete their own written assignments independently because ChatGPT can generate text without alarming plagiarism detection, while other researchers like Ladha et al. ( 2023) stated that AI detectors can accurately identify AI-generated text. There is disagreement about AI detection software accuracy ( Uzun, 2023). Research on AI-text detectors and classifiers is still growing, which leaves L2 educators with few resources to face the challenge of AI-assisted plagiarism ( Ibrahim, 2023).

2.3. Students' Perceptions of Plagiarism and AI in Academic Dishonesty

Studies on students' perceptions of plagiarism have revealed their understanding of plagiarism and authorship, particularly concerning the internet. In the UK, undergraduate students self-reported moderate internet-based plagiarism over the past year ( Selwyn, 2008). In contrast, students in Israel perceived online information as part of the public domain ( Baruchson-Arbib, 2004), a view that differs significantly from Hungarian students, who acknowledged the importance of responsible use and proper citation to avoid academic misconduct ( Fajt & Schiller, 2025). Both Ghanaian and Rwandan students appreciated ChatGPT's ability to facilitate faster and easier work, although both groups expressed uncertainty about what constitutes academic misconduct ( Anani et al., 2025; Clarke et al., 2023). Scholars such as Pecorari ( 2013) stated that "students who incorporate source material without citation are not necessarily attempting to deceive their readers, but are often doing what they have been taught to do: use expert texts as models for their own writing" (p. 320). Therefore, students borrow texts as a developmental learning strategy. This perspective clashes with traditional views of plagiarism and academic misconduct, which assume that students are intentionally claiming someone else's work as their own. This echoes the theme of "writing passages that are not copied exactly but that have been borrowed from another source" ( Howard, 1995, p. 799). If students, like those in Ghana and Rwanda, are using AI-generated text to develop their writing skills, they may inadvertently plagiarize. Although tools like ChatGPT can support writing development, using AI-generated text without proper attribution may still constitute academic misconduct under institutional policies and standards. Carroll ( 2013) contended that when second-language students plagiarize, it is often due to unfamiliarity with academic norms rather than intentional dishonesty. In some cultures, for instance, practices such as memorization and textual reuse are considered respected forms of learning-practices that may conflict with Western expectations of originality and citation. Therefore, educators should approach such cases with cultural sensitivity and treat them as opportunities for instruction rather than punishment.

Several studies highlight the perceived advantages of ChatGPT, with students appreciating its ability to save time, provide personalized feedback, and offer a wide range of information, making it a helpful tool for learning ( Valova et al., 2024). For instance, Ngo ( 2023) found that university students in Vietnam valued ChatGPT for improving learning efficiency and providing individualized tutoring. Similarly, Farhi et al. ( 2023) reported that students in the United Arab Emirates thought they benefited from ChatGPT as a virtual tutor, language practice partner, and writing support system. The results of a pilot study conducted by Bitzenbauer ( 2023) on the application of ChatGPT in a physics classroom showed that its incorporation was viewed as a favorable tool that influenced students' perceptions of physics and science. Students who were able to use ChatGPT firsthand rated the experience highly. In Latin America, Cherrez-Ojeda et al. ( 2024) found that 70% of medical students said that they used ChatGPT for homework support. They also used the chatbot for support with research paper writing, medical/healthcare education and training, and mental health support. Acosta-Enriquez et al. ( 2024), in their survey on undergraduate students from universities in Peru, found that students who consider ChatGPT a valuable tool for their learning process are more likely to use it more frequently than those who do not perceive its importance, and students who have a greater intention to verify information and use ChatGPT responsibly tend to perform better in their academic activities. Román-Acosta et al. ( 2024), in their study on postgraduate students, found that the postgraduate students surveyed indicated that they occasionally used ChatGPT to generate content as well as structure ideas. An interesting fact is that 31.6% of participants never included an acknowledgement or attribution statement when using text generated by ChatGPT (one-third did not cite or credit ChatGPT).

Y. Xiao and Zhi ( 2023) found that students have positive feelings towards ChatGPT, but one concern was that students might use chatbots to take shortcuts-leading to plagiarism in writing assignments. Four out of the five students interviewed in this study believed that universities should embrace ChatGPT rather than ban it, despite their concerns regarding plagiarism. Elkhodr et al. ( 2023) found that students view chatbots as valuable and enjoyable; however, they have concerns about the information generated in their questions and whether this generated text is irrelevant or incorrect. Additionally, students worried that they could become overly reliant on chatbots for text generation, and this dependence on AI could negatively affect their critical thinking and problem-solving skills. Singh et al. ( 2023) found that students express that if they do not use ChatGPT properly, its use could negatively impact critical thinking skills as well as other skills used in the research process such as writing conclusions. Studies by Das and Madhusudan ( 2024) and Valova et al. ( 2024) indicated that students might rely too much on chatbots, which could reduce their ability to think critically and be creative. Farhi et al. ( 2023) found that students believed that ChatGPT generated high-quality written content; however, this could undermine academic integrity, and students expressed concerns that they might become overly dependent on ChatGPT for academic assignments. Bikanga Ada ( 2024) also found that students believe universities should allow the use of ChatGPT, and it is fair for students to use ChatGPT for academic purposes. In total, 87.8% of the students reported that they used ChatGPT weekly to compensate for missed class lectures and to fill knowledge gaps. Albayati ( 2024) found that students deem ChatGPT to be beneficial in terms of ease of use, but students also acknowledged drawbacks such as privacy and security concerns. Students expressed that ChatGPT's inability to replicate human interaction and mentorship poses challenges, particularly in fostering academic growth and emotional support ( Rahman et al., 2023; Sila et al., 2023). Moreover, specific technical limitations of ChatGPT have been noted, such as difficulties in generating accurate or detailed information on complex topics and its limited capacity to handle certain academic tasks effectively, like complex mathematical expressions ( Ngo, 2023). These drawbacks have raised concerns about the use of ChatGPT, with students and educators alike debating its role in promoting or hindering genuine academic development ( Bonsu & Baffour-Koduah, 2023; Farhi et al., 2023). Like students in other countries, Ecuadorian students face heavy workloads and time constraints, which may lead them to seek self-guided learning support, making tools like ChatGPT particularly appealing. As AI tools gain popularity across South America, their usage among students is likely to increase. It is therefore essential to examine whether Ecuadorian students use AI tools in ways like their peers abroad. Equally important is understanding how Ecuadorian students specifically engage with ChatGPT. This includes identifying perceived risks, exploring how and why these tools are used, and situating these insights within the local Ecuadorian context. By comparing Ecuadorian students' responses with those of students from other countries, we may identify common trends and uncover opportunities for educational improvement.

2.4. Theoretical Framework and Guiding Perspectives

The Transformative Learning Theory, developed by Mezirow ( 2003), serves as the main theoretical framework for this study. For an in-depth exploration of how Mezirow's Transformative Learning Theory has evolved, see Kitchenham ( 2008) and Christie et al. ( 2015). The Transformative Learning Theory was selected because it provides a valuable framework that can be applied to understanding students' perceptions of AI within academic contexts.

The term transformative learning is defined by Mezirow ( 2003):

Transformative learning is learning that transforms problematic frames of reference-sets of fixed assumptions and expectations (habits of mind, meaning perspectives, mindsets)-to make them more inclusive, discriminating, open, reflective, and emotionally able to change. Such frames of reference are better than others because they are more likely to generate beliefs and opinions that will prove more true or justified to guide action.

The Transformative Learning Theory emphasizes the role of critical reflection in enabling individuals to reassess their assumptions and beliefs in response to new experiences ( Mendoza, 2020). Critical reflection fosters a more inclusive worldview, openness to diverse perspectives, and the ability to connect personal experiences in meaningful ways ( Enkhtur & Yamamoto, 2017). Within this framework, reflective processes can be used to encourage students to confront the implications of using AI in academic writing with a particular relevance in discussions of authorship, originality, and academic integrity-areas of concern when AI tools are employed in educational settings. As Castañeda and Selwyn ( 2018) argued, "...digital technologies do not simply support the transmission or exchange of information between staff and students. Instead, these technologies mould peoples' values, beliefs and behaviours" (p. 4).

Therefore, Mezirow's emphasis on self-reflection and the critical examination of assumptions is especially relevant as students determine when and how AI-assisted writing is appropriate and reflect on the motivations behind using such tools. Can et al. ( 2023) found that student engagement with ChatGPT often raised concerns about potential misuse and academic dishonesty, thereby reinforcing the relevance of the Transformative Learning Theory in guiding critical reflection on students' use of AI tools.

To complement the Transformative Learning Theory and provide a more nuanced understanding of students' engagement with AI in academic writing, this study also integrates academic dishonesty, detection of AI-generated content, and students' perceptions of plagiarism through five lenses or guiding perspectives-process-oriented learning, constructivism, social constructivism, computational constructivism, and Wenger's Communities of Practice (CoP)-to inform the research by offering specific perspectives on how students construct knowledge and interact with AI tools.

Process-oriented learning emphasizes cognitive and metacognitive skill development rather than focusing solely on final written products ( Gibson et al., 2023). This iterative approach-planning, drafting, revising, and reflecting-promotes deeper engagement and critical thinking. However, LLMs like ChatGPT threaten this iterative learning by allowing students to bypass critical stages, thereby reducing writing to generating a finished text ( Rodrigues et al., 2025).

Constructivist and social constructivist theories reflect specific epistemological beliefs about how individuals acquire knowledge. Epistemological beliefs encompass individuals' viewpoints about the nature of knowledge and how it can be acquired, verified, and justified, and these beliefs shape how individuals behave, make decisions, and acquire knowledge ( Wang & Kim, 2023). The educational theory of constructivism proposes that learners construct new knowledge by building from their current knowledge through an active process in which they negotiate their understanding of new information based on experiences and interactions with the environment ( Amineh & Asl, 2015; Rasul et al., 2023). Some authors state that constructivism sees learning as both an active process and a personal representation of the world ( Suneetha, 2014), while others say that it is an educational philosophy that focuses on exploring context-specific truths and that education is most successful when students take an active role in making sense of what they learn ( Mohammed & Kinyo, 2020). It has been stated that students should seek "the pleasure that is inherent in solving a problem seen and chosen as one's own" ( Von, 1995, p. 7). Also, students need to reflect, monitor, and direct their own learning ( Teo & Zhou, 2017).

Social constructivism is a branch of constructivism that highlights the role of collaboration and social interaction in the learning process. Individuals are seen to participate actively in the learning process through social interactions ( Jha, 2017). It emphasizes the importance of social exchanges for learners' cognitive growth and the role of culture and history in student learning ( Applefield et al., 2001).

Educators who use constructivist epistemological frameworks to guide their teaching practices should not take the role of the teacher-centered lecturer who presents facts for students to memorize. Instead, the teacher should shift from the role of the transmitter of knowledge to the role of facilitator or guide who uses scaffolding, tutoring, cooperative learning, and learning communities in the classroom, and the most important goal is to help the learner to become an effective thinker ( Amineh & Asl, 2015). Writing teachers (under the constructivist theory) can enhance student learning "by emphasizing the writing process and require[ing] students to document stages of their drafting process" ( McGuire et al., 2024, p. 338). AI-generated content provides polished results with minimal cognitive engagement, aligning with computational constructivism's concern that AI may encourage passive consumption rather than active knowledge construction ( Ateeq et al., 2024; Chan, 2023).

AI's educational use should align with Freire's Critical Pedagogy, where education empowers students to engage critically with knowledge rather than passively consuming it ( Freire, 2000). Educators should guide students in reflective, critical engagement with AI-generated content, facilitating co-construction of knowledge through dialog ( Alghannam, 2024). Institutions must educate students about ethical AI use, shifting the focus from punitive measures to promoting responsible, reflective practices ( Ateeq et al., 2024; Gibson et al., 2023; Wenger, 1998).

While academic dishonesty is longstanding, AI exacerbates the issue, making plagiarism easier and detection harder ( Pudasaini et al., 2024). Students may view AI-generated content as "less dishonest", particularly when used to refine rather than create original work ( Ateeq et al., 2024). Yet reliance on AI diminishes cognitive effort, negatively impacting deeper learning and intrinsic motivation ( Gerlich, 2025; Rodrigues et al., 2025).

Wenger's Communities of Practice ( Wenger, 1998) provided insight into how AI shapes academic behaviors ( Wenger-Trayner et al., 2023). Communities form through shared engagement and collective learning, establishing norms around AI use. If students normalize AI-assisted writing, institutions risk weakening academic integrity and critical thinking. Overreliance on AI may devalue originality, blur ethical boundaries, and encourage superficial learning ( Chan, 2023; Rodrigues et al., 2025). Wenger-Trayner et al. ( 2023) stressed that authentic community learning emerges from shared problem-solving and deep engagement-something potentially undermined by superficial AI use. Clear institutional guidelines and ethical frameworks are essential to maintaining academic standards and fostering responsible AI use ( Ateeq et al., 2024).

Researchers encourage more surveys to better understand how students engage with ChatGPT and other GenAI tools ( Sullivan et al., 2023). A systematic review article on AD in online learning environments was performed ( Chiang et al., 2022). The authors examined 59 articles from 18 countries, and no publications were reviewed that originated from Ecuador; the closest country was Colombia (1 paper). In addition, another systematic review was conducted on ChatGPT in ESL and EFL education within a 1.5-year time period following ChatGPT's release in 2022 ( Lo et al., 2024), and nearly half of the 70 empirical studies written came from East Asia. Zero studies came from South America. In fact, South America was not listed at all. Due to the lack of research performed in Ecuador about how students engage with ChatGPT, it is vital to conduct studies in this region to fill a significant gap in the literature.

A gap exists in the literature related to undergraduate STEM students' perceptions of artificial intelligence use in EFL classes in South America. By investigating how EFL students in a rural, STEM-focused higher education institution in Ecuador perceive and interact with AI for academic writing, this study contributes to an enhanced understanding of the role of technology in English-mediated education in geographically remote settings. It also sheds light on how AI influences academic integrity, learning behaviors, and writing development in non-traditional EFL populations. The results of this paper can inform conversations that teachers may have with students as they seek to understand their perspectives, raise awareness, and influence classroom behavior; it can also inform conversations among policymakers as they work to establish clear guidelines and approaches to the appropriate use of GenAI in education. Finally, the authors will provide recommendations to EFL practitioners, especially those who teach in South America.

3.1. Aim of Research

The present study employs a multiple-choice survey to explore Ecuadorian EFL students' perceptions of AI chatbots such as ChatGPT in creating and improving their writing in English, with a specific focus on academic dishonesty. The survey explores how students define cheating, why they believe students use ChatGPT to cheat, the impact of these technologies on cheating rates, and their opinions on the detection, consequences, and prevention of cheating with AI. Additionally, the survey examines how students think ChatGPT can support student writing and whether chatbots should be used for this purpose at all. The study contains five research questions (RQs).

RQ1: How do students perceive the use of generative AI dishonestly in L2 writing, including their definition of it, their views on its negative implications, and the motivations they believe make students use it? This research question and its sub-questions deal with general aspects of students' perception of the use of generative AI dishonestly in L2 writing, including how they define it, whether they think it is negative, and what they believe motivates students to use it:

RQ1.1: What definition and examples do students give for academic dishonesty in L2 writing with ChatGPT?

RQ1.2: What negative consequence of using AI dishonestly in their L2 writing can students identify?

RQ1.3: What do students believe are their motivations for using AI dishonestly in their L2 writing?

RQ2: What do students believe about how easy it is to detect AI-generated textual content?

RQ3: What do students think teachers and institutions should do about AI-based academic dishonesty in writing in terms of response to and prevention of dishonesty?

RQ4: Do students think it is acceptable to use ChatGPT and similar technologies in their academic writing, and what reasons do they give for using it?

RQ5: How do students think tools like ChatGPT have already affected academic integrity in writing, and what predictions do they make about future generative AI use in writing?

3.2. Study Design

3.2.1. Context and Participants

The present study was conducted at a rural university in northern Ecuador, situated high in the Andes, far from major urban centers and other higher education institutions, where the student population is comprised mainly of Ecuadorian individuals. The university's undergraduate offerings are exclusively STEM programs ( Ricaurte et al., 2022; Ricaurte & Viloria, 2020). As a partial English as a Medium of Instruction (EMI) institution, English is necessary for academic study, research, and professional development for at least half of the student's academic programs. The geographical isolation of this university presents unique educational challenges and opportunities. Unlike students in urban institutions, these learners do not have immediate access to extensive academic networks, international conferences, or English-speaking environments. Consequently, their English proficiency development is mainly classroom-dependent, and their exposure to academic writing in English is primarily mediated through formal instruction rather than organic immersion. This context makes integrating AI tools, such as ChatGPT, particularly relevant, as these technologies can serve as supplementary learning resources in an environment with limited access to native English speakers or specialized academic support.

The 56 participants who responded to the survey were intermediate EFL students (B1-level) enrolled as undergraduates in the university described above. The participants had undeclared majors at the time of this study, but all intended to declare STEM majors. The participants were not trained to be English teachers. The students belonged to two sections of one of the author's B1 level, communicative EFL courses, a required course for students. Demographic information was collected from students and is illustrated in Figure 1. Overall, 31 self-reported as male, 24 as female, and 1 as other. Age information revealed that 29 students were 18 years old, 19 students were 19 years old, and 4 students were 23 or older.

The present study employs a multiple-choice survey to explore Ecuadorian EFL students' perceptions of AI chatbots such as ChatGPT in creating and improving their writing in English, with a specific focus on academic dishonesty. The survey explores how students define cheating, why they believe students use ChatGPT to cheat, the impact of these technologies on cheating rates, and their opinions on the detection, consequences, and prevention of cheating with AI. Additionally, the survey examines how students think ChatGPT can support student writing and whether chatbots should be used for this purpose at all. The survey contains 11 questions related to perceptions and the use of AI in writing (see Appendix A).

The study adhered to ethical guidelines for educational research, ensuring voluntary participation, anonymity, and minimal influence from the instructor ( Columbia University, 2024; University of Waterloo, 2024). Participation was explicitly voluntary, clearly communicated in the consent form, and students could opt out without academic consequences. The survey was administered at the end of class to avoid coercion, allowing students to leave freely if they chose not to participate. Anonymity was ensured by assigning numerical codes to responses rather than using identifying information. The survey was conducted through Microsoft Forms, allowing private and independent participation without researcher oversight, aligning with best practices to mitigate social desirability bias ( University of Waterloo, 2024).

A clear distinction between instructional and research roles was maintained. Although present to address technical issues, the researcher avoided interaction regarding students' responses. The researcher's dual role was managed according to Institutional Review Board recommendations, clarifying the role as researcher, not evaluator, and ensuring confidentiality throughout data collection ( Columbia University, 2024). The study strictly followed ethical strategies emphasized in educational research, including voluntary participation, separation of teaching and research roles, anonymous data collection, and explicit opt-out procedures ( University of Waterloo, 2024). These practices ensured the reliability and authenticity of student responses.

3.2.2. Data Collection

An anonymous, multiple-choice Microsoft Forms questionnaire was created and delivered to students. It was written in English and included 11 questions, an additional item about age and gender. Questions ( Appendix A) attempted to assess students' opinions of what might constitute academic dishonesty with generative AI applied to writing and what constituted appropriate use. All questions included the option "other", and students who chose this option were asked to provide an explanation. Although the survey included open-ended "other (please specify)" response options, many participants did not respond to these prompts. Out of the 11 questions, only one student provided an in-depth response. Consequently, qualitative insights were not central to the analysis. Open-ended responses were reviewed informally but were not subjected to systematic coding. All the survey questions were recorded with the participants' consent.

The questionnaire was applied in the two classes that one of the researchers was assigned to teach. These students were given information about the topic of the study three days before the survey was given to them. The researcher noted that the students seemed to feel enthusiastic about being a part of the study. At this time, the students were also notified that their answers to the survey would be anonymous. The survey was applied the following Monday during the first hour of each class after students had signed a consent form. The survey was accessed via a link passed through a chat application. The researcher monitored students during the survey and helped them with questions. The average time to complete the survey was 35:07, which included outliers who completed the survey in as little as 2-3 min and one who completed it in 108 min. The time needed to complete the questionnaire was estimated to be 11 min. The questionnaire return rate was 100% because attendance is a graded component of the course, meaning that students must attend regularly and consistently as part of their academic performance. Since students were accustomed to being present every class session, participation in the survey was naturally high, contributing to the perfect response rate. Although the study achieved a 100% response rate, this figure must be interpreted cautiously. Participation occurred in a required course where class attendance was graded, and the instructor-researcher was physically present during survey administration.

The student-teacher power dynamic may have compromised the neutrality of responses because these contextual factors likely influenced the students' decision to participate, thereby introducing a power imbalance. It is strange to have every single student complete a survey. While explicit steps were taken to ensure voluntary participation, such as emphasizing that students could leave freely, the instructor's presence may have inadvertently created pressure to comply. This dynamic constitutes a methodological limitation, as it may have affected the authenticity of student responses or introduced social desirability bias. Future research should consider using a third-party facilitator or administering surveys outside class hours to minimize these concerns. In addition to addressing concerns about the 100% response rate, it is also essential to ensure conceptual clarity. Therefore, to avoid definitional ambiguity in the results and discussion section, several key terms are operationalized as follows:

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Pseudo-success: Pseudo-success refers to situations in which students appear to achieve academic success (for example, a strong grade on an essay), but they do not meaningfully engage with the materials because they used AI tools to think for them. Said differently, students achieve good grades without truly understanding the material.

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Dishonest use: Dishonest use is when students plagiarize. They use GenAI to complete assignments and do not report that they used AI to help them (a lack of transparency). Dishonest use is when students misrepresent who authored a written assignment, such as an essay.

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Ethical AI Practices: Ethical AI practices are the transparent and responsible use of AI in academic contexts. These practices refer to adhering to academic policies and crediting GenAI text (transparency).

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Raising Awareness: Raising awareness refers to informing students, teachers, and administration about creating original works.

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Implementing Policies: Implementing policies refers to developing, communicating, and enforcing institutional rules or guidelines related to AI use and academic writing.

The purpose of these definitions is to ensure consistent conceptual anchoring of key concepts throughout the Results and Discussion section.

4.1. RQ1: Students' General Perceptions of Generative AI

The survey questions related to this research question dealt with students' general perceptions of academic dishonesty related to using ChatGPT and similar technologies in producing academic writing. The questions were as follows:

Q1: How do you define academic dishonesty involving AI technologies like ChatGPT in the context of your writing production?

Q2: What specific examples can you provide for using AI technologies dishonestly in your writing?

Q3: What are the main reasons someone might use AI technologies dishonestly in your writing production?

Q4: What do you believe are the consequences of using AI dishonestly in your writing?

4.1.1. Q1: Main Findings

In addition, direct instruction could be used to define what constitutes academic dishonesty and explicitly state why it matters, as this approach will serve as a cognitive scaffold aligned with process-oriented learning theory, which emphasizes that awareness of ethical practices is foundational for the development of academic writing skills ( Gibson et al., 2023). The purpose of academic integrity is to justify the students' future degrees. A degree signifies that a student knows things and can do things, and they have met certain standards and achieved learning outcomes fairly well. Cheating can help students obtain a credential (a diploma), but they will not know anything and will not be prepared for the workforce. This finding may reflect a deeper cultural and institutional reality specific to the Ecuadorian EFL context ( Castañeda & Selwyn, 2018). Writing is not only a classroom activity but a requirement that plays a key role in students' academic progress. In this setting, students must often submit essays as part of essential evaluations, such as graduation projects or final term assignments, that determine whether they move forward in their studies. When students do not view AI-generated, translated, or copied texts as dishonest, it may not be an intentional act of misconduct, but rather a reflection of how writing is understood within their academic environment. Instead of being seen as a process of learning and development, writing is often viewed as a final product that must be completed to meet institutional expectations. This practical view, along with limited exposure to international standards of academic honesty, may help explain why many students do not recognize these practices as dishonest. For this reason, educators should focus not only on defining academic dishonesty, but also on helping students see writing as a meaningful activity that supports critical thinking and personal growth.

4.1.2. Q2: Main Findings

The results of Q2 indicate some disagreement between student and teacher perceptions of what constitutes academic dishonesty. Only 29% of participants answered that using AI, copying AI, and not citing sources are examples of using AI dishonestly in EFL writing. The results obtained on question Q2 indicated that 66% of students have an incomplete idea of specific examples for using AI technologies dishonestly in their writing, including using and copying AI without recognition. Chan ( 2025) mentioned that students clearly understand traditional plagiarism but understand GenAI plagiarism less clearly. This contrasts with our results because only 34% of students understood that all three options constituted plagiarism. To overcome this disconnect, educators could implement guided discussions on academic integrity; Wenger's Communities of Practice ( Wenger, 1998) support this strategy, in which ethical norms are developed through dialog and shared participation in a learning community. Since most students did not identify key forms of dishonesty, this suggests a gap in their understanding of academic integrity within AI-assisted writing. Teachers can also raise awareness by facilitating classroom discussions on why academic integrity matters, linking it to the credibility of their degrees and future professional competence ( Mezirow, 2003). Teachers need to engage students in problem-based learning scenarios where they must determine whether specific AI-related writing practices are ethical or not. Teachers need to teach through a social constructivist approach instead of simply defining what constitutes AI-related dishonesty. Teachers need to explain how academic dishonesty in AI mirrors workplace dishonesty, contributing to professional incompetence, corruption, and societal underdevelopment, and to connect this information with real-world consequences ( Chan, 2023; Mendoza, 2020). Finally, teachers must work with students to co-create classroom policies on responsible AI use, ensuring they internalize and apply ethical writing principles in their academic work.

4.1.3. Q3: Main Findings

4.1.4. Q4: Main Findings

The results from question Q4 indicate that 87% of students have an incomplete idea of the consequences of using AI technologies dishonestly in their writing, which involve academic penalties, hindering development, and pseudo-success, among others. The data suggest that many students perceive negative consequences associated with using generative AI in academic writing. This finding favors studies by Elkhodr et al. ( 2023) and Singh et al. ( 2023), whose students believed that AI negatively affected critical thinking, problem-solving skills, and other skills used in the writing process. The concept of hindering writing skills also relates to the impact of academic dishonesty on students' own academic growth ( Rahman et al., 2023; Sila et al., 2023). When students use AI tools to create a well-written essay (rather than engaging in the mental processes that lead to a deeper understanding of the material), they bypass the necessary steps that constructivist theory deems necessary for learning to take place, as inappropriate use of AI undermines the epistemological process of knowledge construction ( Von, 1995; Wang & Kim, 2023). Classroom discussions on the long-term effects of AI reliance (weakened writing skills and pseudo-success) can be facilitated by educators, and this recommendation aligns with the theory of process-oriented learning because authentic writing not only involves the creation of a final product but the cognitive effort required to produce it ( Rodrigues et al., 2025). Moreover, educators should design activities that contrast AI-generated output with student-created content, allowing learners to see the differences in depth, personalization, and skill development. Institutions might also consider incorporating formative assessments and reflective tasks where students evaluate their own learning processes, helping them understand that academic success is not just about completing tasks, but about meaningful growth. By leveraging students' existing concerns, especially about being penalized or unprepared, teachers can motivate ethical behavior and promote process-oriented learning practices that emphasize integrity, effort, and authentic achievement.

4.2. RQ2: What Do Students Believe About AI Text Detection?

The survey related to this research question dealt with students' beliefs about detecting AI-generated content. The question was as follows:

Q5: How do you perceive the detection of AI-based academic dishonesty in your writing?

Q5: Main Findings

According to the results obtained, 73% of the students perceived that AI use is easily detectable or detectable if reviewed. Survey results show that students overestimate teachers' abilities to detect works that an AI has written, as 46% of students believed that AI is easily detectable. In contrast, AI-generated text is difficult to detect, and AI-detecting programs are unreliable for determining whether a text was written by AI ( Alexander et al., 2023; Clark et al., 2021; Foster, 2023; Gao et al., 2023; Gorichanaz, 2023; Khalil & Er, 2023), yet the students surveyed did not seem to know this. Reasons for this discrepancy are open to speculation. A likely explanation is that students have a naïve understanding how AI detectors function. Another possible explanation could be that their previous teachers have oversold the efficiency of AI detection capabilities either due to their own ignorance on the subject or in an attempt to decrease plagiarism. Students may assume that AI detectors are like the plagiarism detectors that they may already be familiar with and which they may think are effective due to their past experiences with them. Or perhaps they, especially EFL and ESL students, assume it would be easy for a teacher to detect AI-generated content based on grammatical and mechanical perfection and sophistication that would clearly be unlikely for them to produce.

A significant consequence of this mismatch is the debate as to whether teachers should disabuse students of this notion. It is reasonable for teachers to think that students' beliefs that AI is easy to detect would dissuade students from using AI for plagiarism. Thus, teachers may not want to inform students of the truth about the difficulties of AI detectability. However, this assumption is not necessarily supported by the research. Some research has shown that the likelihood of unethical behavior being detected does not reduce the probability that one will engage in an unethical act ( Gamliel & Peer, 2013), while other researchers have found that it does ( Dawson & Hanoch, 2024). Regardless of whether the ease of detectability affects a tendency towards unethical behavior or not, it is known that a host of factors affect the choice to engage in unethical acts ( Belle & Cantarelli, 2017), so the argument that letting students know that AI is difficult to detect, and therefore will increase academic dishonesty, has poor research support and is likely reductive, considering all the factors that can influence ethical decision making.

Furthermore, there are ethical considerations about letting students persist in their ignorance on this point. It has been shown that students who are aware of the failings of AI detectors have "rais[ed] concerns...regarding...the ethical implications and negative consequences of misclassification of genuinely original works as machine-generated outputs" ( Angeles et al., 2024, p. 56). This is especially relevant in the EFL and ESL setting, as AI detectors have been shown to be especially prone to marking ESL and EFL writers' work as AI-generated when it is not ( Liang et al., 2023). Without the knowledge that AI detectors are highly fallible, students may not be able to adequately defend themselves against teachers and administrators who implement AI detectors without awareness of the unreliability of AI detection. In terms of educational policies and procedures in response to this issue, administrators should be sure to provide training to their teachers on human and machine AI detection as well as to establish clear guidelines on how teachers should address AI-giarism as such policies align with social constructivist theory that states learning should be grounded in dialog ( Jha, 2017). Future research might determine why some students might not understand that detecting AI-generated text is difficult and how that ignorance affects their tendency to cheat with AI. Finally, AI detectors should be improved, if possible.

4.3. RQ3: How Should Authorities Respond to AI-Based Academic Dishonesty?

The survey questions related to this research question dealt with students' opinions on what teachers and institutional authorities should do when they encounter AI-based academic dishonesty in student writing and what they should do to prevent it from occurring. The questions were as follows:

Q6: How do you believe teachers or institutions should respond to AI-based academic dishonesty in writing?

Q7: What measures do you believe could effectively prevent AI-based academic dishonesty in writing?

4.3.1. Q6: Main Findings

In total, 37% of the students reported that teachers or institutions should respond to AI-based academic dishonesty in writing by educating students. Additionally, 55% of students believe that forcible penalties should be implemented or AI-based plagiarism detectors used. Research indicates the importance of instruction and awareness-building in the classroom, but this research also highlights that punitive actions are ineffective long-term ( Ateeq et al., 2024; Gorichanaz, 2023). Even so, the students reported that safety measures like AI detectors are necessary to maintain academic integrity. Similarly, researchers state that a lack of clear institutional guidance contributes to students' uncertainty around responsible AI use ( Rodrigues et al., 2025). While some institutions may focus on detection tools or strict regulations, the literature suggests that proactive education, framing AI as a literacy issue rather than a threat, is more impactful. Updated writing pedagogies that incorporate AI ethically must first be situated within clear conversations, instruction, and policies on appropriate AI use ( Mezirow, 2003). Teachers can add the following text to their syllabi to help students understand what constitutes unauthorized assistance. Here is an example paragraph that could be added to educators' syllabi regarding AI use in the writing classroom:

In a language class, evaluation is based on your ability to show that you are working towards mastery of the language, including showing skill with grammar, sentence structure, punctuation, and word choice that is appropriate to your level. You are not expected to produce perfect writing that is completely error-free and sounds like a native speaker wrote it. Rather, you should show that you have mastered the grammatical structures and vocabulary that have been covered in this course and in previous courses in the program. Because the teacher must have an accurate picture of your language skills at the time of evaluation, it is considered academically dishonest to use unauthorized assistance to complete your assignments. Unauthorized assistance may include, but is not limited to:

○

Having your paper revised, edited, and corrected by another person;

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Having your paper edited and corrected by artificial intelligence or another computer program, such as Grammarly, Linguix, or Ginger, among others;

○

Using an online translator to help you understand written or spoken texts.

Your teacher will guide you on the appropriate use of outside resources and help you understand what constitutes unauthorized assistance.

4.3.2. Q7: Main Findings

Overall, 78% of students consider that raising awareness, providing support, and encouraging honesty are effective measures to prevent AI-based academic dishonesty. Additionally, 28% consider that strict monitoring detection tools should be implemented. Currently, AI-based technologies are seen as tools that make our lives easier and reduce the time of dedication to school homework. However, universities and other educational institutions must show students the implications of academic dishonesty and provide conditions for encouraging honesty. In the study by Y. Xiao and Zhi ( 2023), students believed that universities should embrace ChatGPT. In contrast, our study's students believe universities need to provide support and raise awareness of AI-based academic dishonesty. The finding that 46% of students emphasized the need for support suggests that learners are not rejecting AI. Instead, they are asking for structured guidance and institutional resources to help them use it ethically and effectively. Teachers can reduce academic pressure and add more ungraded writing practice in class. Teachers should incorporate more low-stakes (ungraded writing) practice in their writing courses. Students in this study seem more concerned with understanding how to navigate AI responsibly. This finding highlights an important role for educators: they must serve as instructors and mentors in digital literacy to help students develop the skills needed to evaluate and use AI tools appropriately. Educators should also validate students' desire for support and create open, judgment-free spaces where students feel comfortable asking questions about what is allowed and why. This proactive, student-centered approach can foster responsible engagement with AI tools, helping students balance innovation with academic integrity.

4.4. RQ4: Reasons for Typical and Acceptable Use of ChatGPT

The survey questions related to this research question sought to understand the effect of generative AI technologies on students' writing habits and their motivations for using them:

Q8: How do you perceive the use of AI tools like ChatGPT as a support for your writing tasks?

Q9: In your opinion, is it correct to use AI tools like ChatGPT in your writing?

4.4.1. Q8: Main Findings

4.4.2. Q9: Main Findings

4.5. RQ5: Generative AI Effects on Academic Integrity and Writing Now and in the Future

The survey questions related to this research question were intended to determine how students perceived AI's impact on academic integrity in writing in the present and how it might impact writing in the future. The questions were as follows:

Q10: In your opinion, how has the arrival of AI technologies like ChatGPT impacted academic integrity in your writing production?

Q11: How do you predict the use of AI tools like ChatGPT for writing will change in the near future?

4.5.1. Q10: Main Findings

Overall, 73% of the students consider a significant or moderate increase in instances of academic dishonesty. Students from this study believe that AI technologies on academic integrity within academic writing will significantly increase. The literature reflects this finding ( Dergaa et al., 2023; Thorp, 2023). Practitioners should interpret these data as an opportunity to encourage deeper reflection among students on their own writing behaviors. One possibility is that students who see no significant impact may not engage in dishonest practices themselves and, therefore, do not perceive AI as a threat to integrity. Alternatively, it may indicate a lack of awareness or an underestimation of how AI tools can compromise academic honesty. Teachers can facilitate reflective writing or class discussions where students analyze their own use of AI and its potential impact on their development as writers. Teachers can encourage students to think critically about the role of academic integrity not just as a set of rules, but as a foundation for genuine learning and professional readiness. Classroom activities could include analyzing AI use and discussing scenarios where students must assess the integrity of different writing practices. Academic integrity is about avoiding punishment and ensuring that students' future degrees reflect true competency and knowledge. For students who do not perceive the inappropriate use of AI on academic writing as a problem, it is important to create safe classroom spaces where differing viewpoints can be discussed, especially about accountability. By engaging students in dialog and reflection, educators can move beyond simple enforcement and toward transformative learning experiences that prepare students for responsible academic and professional conduct.

4.5.2. Q11: Main Findings

The response for the option "other" was lengthy. One student wrote, "My opinion about AI is that it helps you do long jobs and that you are too lazy to do since it is not useful for the career you want to study, but I also use it to learn new things that make me curious, such as genetic mutations in humans. or about how to find good forums on the deep web and apart from this, the AI helps you to understand with simple words about the topic you want, for example, if you are going to have an osmosis test and you don't know anything, the AI gives you the best summary of the world in a few seconds and you can learn it, based on the question if it will increase the use of AI in the majority of people since Intel is manufacturing the new range of its CPUs incorporating AI in the CPU for which it helps in many things to computers and sooner or later this will reach phones".

In total, 21% of the students consider that using AI-based technologies for writing will be more common in the near future. Heidt ( 2025) proposed that "Unlike the 'early days' of two years ago, when using AI meant summarizing a paper or outlining an essay, students are now reading into the tools' ability to emulate human connection, turning chatbots into podcast hosts, language tutors, professors, and even personal trainers." This individualized response mirrors the findings of Baidoo-Anu and Owusu Ansah ( 2023), who observed that students use AI not only for academic tasks but also for curiosity-driven learning and real-world applications. Similarly, George et al. ( 2023) highlighted that students value AI for its ability to explain difficult concepts in accessible language and to support autonomous learning outside traditional classroom settings. However, unlike some studies focusing primarily on AI's misuse, this response reflects a balanced view, acknowledging its practical and intellectual benefits. The participant's viewpoint aligns with a growing body of research that sees students as critical users of AI rather than passive or purely instrumental users. Educators should consider incorporating open-ended questions in classroom discussions or assessments related to AI to uncover diverse student perspectives, especially those beyond standard academic uses. Open-ended responses like those found in Q11 suggest that some students actively think about AI's role in society, learning, and future technologies. Educators can better tailor instruction to support both language development and digital literacy in a fast-evolving technological landscape by creating space for students to articulate how and why they use AI.

This study contributes a novel perspective to the growing body of literature on generative AI in education by focusing on the experiences of rural, STEM-oriented university students in a South American context where English is used as a medium of instruction. Unlike previous studies that have mostly examined urban environments, students from humanities disciplines, or native English speakers, this research highlights how EFL learners in geographically isolated areas engage with GenAI tools like ChatGPT. These learners depend heavily on classroom instruction for language development, and their academic success is often closely tied to their ability to complete writing tasks in English. In this context, the use of AI-generated, translated, or copied texts may not reflect intentional misconduct. Instead, it may be a response to academic pressure, limited institutional guidance, and a practical view of writing as a product required to meet institutional expectations. GenAI thus becomes both a linguistic aid and a shortcut that reduces cognitive effort. By focusing on this underrepresented population, the study creates space to reconsider how academic integrity policies, teaching practices, and AI literacy can be better adapted to support learners who face multiple layers of disadvantage, including linguistic, geographic, and disciplinary challenges.

Building on this perspective, the study explored the impact of generative AI technologies like ChatGPT on students' English writing skills and their perceptions of academic integrity by using a multiple-choice survey, and the research examined how EFL students at a rural STEM university define AI-related dishonesty, what motivates them to use these tools, and how they assess the risks and benefits associated with GenAI in academic writing. The findings reveal that, while students generally understand what academic dishonesty means, they disagree on whether AI-generated content can be accurately detected. Students also hold varied opinions on how teachers should handle dishonesty involving AI and how tools like ChatGPT influence their writing. For instance, students acknowledged that the pressure to achieve high grades might tempt them to use AI-generated texts dishonestly. About 43% recognized potential negative consequences, such as facing academic penalties, weakening their writing skills, and experiencing a false sense of accomplishment. These results support previous research highlighting that relying heavily on AI can damage critical thinking, problem-solving skills, and overall academic development.

Additionally, students were split regarding the detectability of AI-generated texts. Nearly half (46%) of the participants believed AI content could easily be spotted, contrasting with existing research showing significant limitations in AI detection technologies. The findings further emphasize that proactive educational measures and precise guidance are more effective than applying strict punishments. In contrast to Y. Xiao and Zhi's ( 2023) study, where students favored fully integrating ChatGPT into their studies, participants in this study showed more significant interest in responsible and guided AI use. Another noteworthy finding was that students did not consider translating text from Spanish to English as academically dishonest. This aligns with the findings in this study, which noted that while translation tools like Google Translate can enrich vocabulary, they may discourage students from directly engaging with the target language.

Future research could explore student perceptions more broadly by investigating cultural and institutional differences across various contexts. Additionally, studies could examine how student attitudes toward AI evolve over time. Moreover, studies could test the effectiveness of educational interventions aimed at encouraging ethical AI practices. Based on the findings of this study, educators can take several practical steps. First, teachers should actively raise students' awareness of why academic integrity matters and link the idea of honesty directly to professional and personal credibility. Open class discussions about the long-term impacts of excessive AI reliance, such as weaker critical thinking, can effectively promote genuine learning. Second, educators should address factors contributing to academic dishonesty, such as intense pressure from deadlines. Allowing flexibility in assignment deadlines and helping students shift from a fixed to a growth mindset may reduce stress and the temptation to use AI on assignments. Moreover, including more low-stakes, ungraded writing activities can build students' confidence and foster open dialog about ethical AI usage.

Educators should also move beyond strict punishment and emphasize the intrinsic value of writing as a process that helps students grow intellectually. Classroom discussions around originality, authorship, and the ethics of AI can encourage students to engage meaningfully with their work. Teachers should provide clear guidance about responsible AI use in order to guide students in distinguishing between legitimate support and unethical practices. Teachers should encourage students to discuss why and how they use AI openly. This open discussion can help teachers personalize their instruction by blending language development and digital literacy skills together. Finally, teachers can thoughtfully integrate AI into their writing instruction once expectations are clearly set and students are sufficiently supported. Integrating AI into the classroom may help with clear, structured feedback; teaching practical revision and paraphrasing strategies; and increased opportunities for formative practice. Ultimately, educators should ensure that AI serves as an aid rather than a shortcut to prepare students for the evolving demands of future academic and professional contexts.

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Students' Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language

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While research articles on students' perceptions of large language models such as ChatGPT in language learning have proliferated since ChatGPT's release, few studies have focused on these perceptions among English as a foreign language (EFL) university students in South America or their application to academic writing in a second language (L2) for STEM classes.

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LAIK your classroom: A practical framework to integrate generative AI in higher education classrooms

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LAIK your classroom: A practical framework to integrate generative AI in higher education classrooms

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LAIK your classroom: A practical framework to integrate generative AI in higher education classrooms | Journal of Applied Learning and Teaching

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The proliferation of generative artificial intelligence (GenAI) tools in the last two years and their ongoing development make their integration in education an inevitable reality. Recent studies into the integration of GenAI tools in academia have focused on opportunities for education and challenges to consider. In this context, few frameworks and models have been proposed, but none have addressed the need for a comprehensive and practical framework to facilitate teachers’ efforts to integrate GenAI into their classrooms. This study, therefore, addresses this research gap by developing a framework for the integration of GenAI in classrooms, the LAIK framework. The framework is designed to identify the necessary elements that should be considered before, during, and after integrating GenAI tools in higher education classrooms. To build the framework, data were collected from teachers, students, and classroom observations. Results from the study highlighted the need for four practical stages in this framework, namely (1) laying the foundation, (2) assembling GenAI-friendly classes, (3) investigating and monitoring, and (4) keeping the teacher informed. The framework also offers a variety of options to consider for practical ways to integrate GenAI technology to support learning in the classroom.

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Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness

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Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness

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Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness

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The rapid integration of Generative Artificial Intelligence (GenAI) into educational contexts has presented both opportunities and challenges for students seeking and using feedback. While AI-gener…

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Talk is cheap: why structural assessment changes are needed for a time of GenAI

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Talk is cheap: why structural assessment changes are needed for a time of GenAI

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Talk is cheap: why structural assessment changes are needed for a time of GenAI

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Generative AI (GenAI) challenges assessment validity by enabling students to complete tasks without demonstrating genuine capability. In response to this challenge, institutions have developed and…

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Rhetorical Choices & Voice: Generative AI in the First-Year Composition Classroom

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Rhetorical Choices & Voice: Generative AI in the First-Year Composition Classroom

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https://academyforeducationalstudies.org/wp-content/uploads/2025/04/krueger-final.pdf

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Writing with generative AI and human-machine teaming: Insights and recommendations from faculty and students

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Writing with generative AI and human-machine teaming: Insights and recommendations from faculty and students

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Al-Ali & Highland (2025) GenAI Crash Course

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Check out this Presentation designed by sebah.alali.

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Academic and technologies teams at Barnard College developed an AI literacy framework to provide a conceptual foundation for AI education and programming eﬀorts in higher education institutional contexts.

Credit: SuPatMaN / Shutterstock.com © 2024

Barnard College is an undergraduate liberal arts college for women. It is both a distinct institution and a member of the broader Columbia University ecosystem in New York City. Several small but agile campus teams are working to advance conversations about generative artificial intelligence (AI) topics. As members of Instructional Media and Academic Technology Services (IMATS) and the Center for Engaged Pedagogy (CEP), we have developed educational programming on various AI topics for the Barnard community. Over the past year, we have held open lab sessions to test diﬀerent text- and image-based AI tools, hosted guest speakers on copyright and fair use, facilitated generative AI syllabus statement workshops for faculty, conducted instructional workshops (GenAI 101), and led individualized education sessions for faculty departments. This process has been ongoing and iterative as the tools shift and campus community members' needs change. We have also put internal survey, evaluation, and feedback mechanisms in place to better understand faculty and staff needs related to the use of generative AI tools.

The Need for AI Literacy

Barnard College has established several internal working groups and task forces to discuss bigger questions about the impact of AI on our institution. Currently, there is no mandate or recommendation for faculty to embrace or ban AI in their classrooms. However, faculty are encouraged to deﬁne and discuss their expectations around the use of AI in their assignments. (The CEP has created many faculty resources, including decision trees to guide faculty planning, sample syllabus statements, assignments integrating generative AI, and other materials, to help guide decision-making about whether and how to incorporate generative AI in the classroom.) Higher levels of AI literacy can help faculty make informed decisions about the use of AI in their courses and assignments. On the academic technology services side, the IMATS team has decided not to implement or pursue AI surveillance technology to monitor academic integrity due to bias and questionable reliability of these tools. However, the landscape and corollary policies could change as generative AI technologies evolve.

A Framework for AI Literacy

Members of the IMATS and CEP developed the following framework to guide the development and expansion of AI literacy among faculty, students, and staff at Barnard College. Our framework provides a structure for learning to use AI, including explanations of key AI concepts and questions to consider when using AI. The four-part pyramid structure was adapted from work done by researchers at the University of Hong Kong and the Hong Kong University of Science and Technology (see figure 1). (The Hong Kong researchers' work builds upon Bloom's Taxonomy.) The framework is intended to meet people where they are and scaffold upon their current AI literacy level, whether they have little to no knowledge of AI or are prepared to build their own large language model (LLM). It breaks AI literacy into the following four levels:

1. Understand AI

2. Use and Apply AI

3. Analyze and Evaluate AI

4. Create AI

When applying this framework, it is important to keep in mind that AI is a broad field. There are many types of AI, both real and theoretical. While the concepts enumerated in this article focus primarily on generative AI, the overall structure of the framework can be applied to other forms of AI and technology literacies (e.g., cybersecurity). And, while the information in each level builds on the concepts discussed in the previous level, it is not necessary to learn everything at one level before moving on to the next. For instance, when analyzing how generative AI could impact the labor market, understanding how generative AI models are trained is helpful; however, it's not necessary to master the intricacies of neural networks to conduct such an analysis.

Level 1: Understand AI

The bottom of the pyramid covers basic AI terms and concepts. Most of the programming and instruction at Barnard has focused on levels one and two (understanding, using, and applying AI), as this is a rapidly evolving technology, and there is still a lot of unfamiliarity with it.
Core Competencies

Be able to define the terms "artificial intelligence," "machine learning," "large language model," and "neural network"

Recognize the benefits and limitations of AI tools

Identify and explain the differences between various types of AI, as defined by their capabilities and computational mechanisms

Key Concepts

Artificial intelligence, machine learning, artificial neural networks, large language models, and diffusion models

Artificial narrow intelligence, artificial general intelligence, artificial super intelligence, reactive machines, limited memory, theory of mind, and self-aware

AI tools, such as ChatGPT (see figure 2), Siri, Alexa, Deep Blue, and predictive text

Technical frameworks related to AI (open-source models versus closed models, APIs and how they are used)

Reflection Questions

What type of AI is this?

What technologies does this AI tool utilize?

What was this tool designed to do? What kind of information does it accept as input and return as a response (text, video, audio, etc.)?

What might this tool be particularly useful for?

What would it not be useful for?

Level 2: Use and Apply AI

The second level of AI fluency indicates that users can use tools like ChatGPT to achieve their goals; these users are familiar with prompt engineering techniques and know how to collaboratively refine, iterate, and edit with generative AI tools. Programming designed to build level two fluency at Barnard College includes hands-on labs and real-time collaborative prompt engineering (see figure 3).
Core Competencies

Successfully utilize generative AI tools for desired responses

Experiment with prompting techniques and iterate on prompt language to improve AI-generated output

Review AI-generated content with an eye toward potential "hallucinations," incorrect reasoning, and bias

Key Concepts

Prompt engineering, context windows, hallucinations, bias, zero-shot prompting, and few-shot prompting

Prompting techniques for text-based generative AI, such as adding specificity, using context and details, and asking the model to consider pros and cons or evaluate alternative positions

Privacy, conﬁdentiality, and copyright considerations for information fed into prompting tools

Reflection Questions

Why did a prompt generate a particular response?

How could the prompt be adjusted to get a different response?

What strategies can be used to reduce bias and hallucinations?

How can AI output be checked for bias and hallucinations?

Level 3: Analyze and Evaluate AI

Analyzing and evaluating AI involves a more complex meta-understanding of generative AI. At this level, users should be able to critically reflect on outcomes, biases, ethics, and other topics beyond the prompt window. An example of programming at this level is an event that featured an expert who discussed the current copyright and intellectual property questions surrounding AI and the environmental and climate impacts that generative AI might have (see figure 3). Of course, one can participate in conversations about these questions and ideas without knowing all of the AI definitions. However, familiarity with the preceding levels in the pyramid informs one's baseline understanding and vocabulary, helping the individual to understand how AI intersects with other fields.
Core Competencies

Examine AI in a broader context, bringing in knowledge from one's discipline or interests

Critique AI tools and offer arguments in support of or against their creation, use, and application

Analyze ethical considerations in the development and deployment of AI

Key Concepts

Critical perspectives on AI (The following examples are not intended to be all-encompassing.)

environmental sustainability

labor

privacy

copyright

race, gender, class, and other biases

misinformation

Reflection Questions

What other perspectives or frameworks might be useful in assessing the implications of using generative AI tools?

Where might biases in AI come from?

In what ways does the use of generative AI tools align with or diverge from your personal values?

Level 4: Create AI

At this level of AI fluency, users are able to engage with AI at a creator level. For example, users can build on open APIs to create their own LLM or leverage AI to develop new systems (see figure 5). Currently, Barnard offers less programming at level four than at the other three levels, but there have been workshops at the Computational Science Center that provide technical instruction related to building AI and machine-learning models. Engaging people at all levels of AI fluency is important.
Core Competencies

Synthesize learning to conceptualize or create new ideas, technologies, or structures that relate to AI. Reaching this level of literacy could include the following:

Conceive of novel uses for AI

Build software that leverages AI technology

Propose theories about AI

Reflection Questions

What is uniquely human about your ideas, technologies, or structures? How might they differ from what an AI could create?

What specific AI features lend unique affordances to ideas, technologies, or structures?

Conclusion and Next Steps

While this AI literacy framework is not exhaustive, it provides a conceptual foundation for AI education and programming efforts, particularly in higher education institutional contexts. The intention is to maintain neutrality with AI use, recognizing that technology literacy can lead to the decision not to use it. The impact of AI on higher education will likely be significant, impacting admissions, research, and curricula. Education and foundational literacy are the first steps for a community to engage productively with this rapidly changing technology.

There are many possible next steps Barnard College might take related to generative AI, but specific to the AI literacy framework, the IMATS and CEP teams may explore "moving up" the literacy pyramid in programming, resources, and events as awareness and basic literacy grow. Currently, the majority of our offerings are at levels one and two, but we hope to shift our programming focus to levels two and three. A recent survey revealed that a significant number of faculty and students still have never used generative AI and have negative perceptions of these tools, so our teams are also exploring ways to better facilitate practical and critical engagement.

Another goal of the AI literacy initiative is to underline the human aspect of these technologies. While using generative AI can almost feel like alchemy-spinning gold from plain text via black box technology-it is very much built upon human knowledge, which has its own biases and inequities. Using a critical lens when engaging with generative AI may help users identify existing biases and prevent users from exacerbating them.

Notes

1. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, and James Zou, "GPT Detectors Are Biased Against Non-native English Writers," Patterns 4, no. 7 (July 2023): 100779.

2. Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, and Maggie Shen Qiao, "Conceptualizing AI Literacy: An Exploratory Review," Computers and Education: Artificial Intelligence 2 (2021); Benjamin S. Bloom, Max D. Engelhart, Edward J. Furst, Walker H. Hill, and David R. Krathwohl, Taxonomy of Educational Objectives: The Classification of Educational Goals, Handbook I: Cognitive Domain (New York: David McKay, 1956).

Melanie Hibbert is Director of Instructional Media and Academic Technology Services and the Sloate Media Center at Barnard College, Columbia University.

Elana Altman is Senior Associate Director for UX and Academic Technologies at Barnard College, Columbia University.

Tristan Shippen is Senior Academic Technology Specialist, Barnard Library and Academic Information Services at Barnard College, Columbia University.

Melissa Wright is Executive Director, Center for Engaged Pedagogy, at Barnard College, Columbia University.

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