Generative AI-Assisted Academic Transgressions: A Theoretical “Reflegration” on Student and Faculty Cheating

Abstract

Academic transgression—any behavior, belief, or condition that violates social and academic norms—is a persistent concern in educational institutions. Cheating, a prevalent form of academic transgression, has been exacerbated by the advent of Generative Artificial Intelligence (GAI). While GAI offers remarkable opportunities, its incorporation into research, publication, and teaching and learning processes has also facilitated easier forms of cheating and fraud.

By combining the fraud diamond framework with self-control theory, this article aimed to delve into the factors affecting GAI-assisted cheating by both students and faculty members. To this end, I intertwined the critical academic reflective writing method with side-by-side propositional theoretical integration—what I coin as Theoretical “Reflegration”—in a cross-level (macro–micro) and interdisciplinary manner, bridging the gap between practical experience and theory by providing personal examples that illustrate theoretical principles.

I discussed how perceived pressure (low self-efficacy, insufficient knowledge/skills, pressure for high attainment), rationalization (lack of interest/unwillingness, dissatisfaction, low motivation), role conflicts (a sub-factor shared by both perceived pressure and rationalization); perceived opportunity (GAI affordances, others’ lack of time/knowledge/evidence), capacity (GAI literacy, sense of impunity), and low self-control with its six sub-factors, can influence academic cheating behaviors among students and faculty. The arguments presented in this article can inform the development of effective solutions and policies to deter and detect at-risk individuals, reduce cheating-conducive situations, and promote academic integrity.

Keywords: Generative AI; Academic Transgression; Cheating; Students; Faculty; Theoretical “Reflegration.”

Introduction

I to the class: “I’ve designed your assignment in a way you can’t cheat on it.”

One graduate student: “I’ll have ChatGPT handle it!” And he actually did.

The accessibility of misconduct-facilitating resources has significantly increased the prevalence of academic misconduct, with more than half of university students reportedly engaging in such behaviors (Lund et al., 2025). Advances in Generative Artificial Intelligence (GAI) have further complicated academic cheating (Oravec, 2023), turning these tools into indispensable components of dishonest practices (Nartgün & Kennedy, 2024). GAI provides students, faculty, and staff with powerful, easily accessible systems for generating content (Oravec, 2023)—including essay generators, online essay mills, and custom writing services—that have made cheating easier (Mohammadkarimi, 2023).

Higher education institutions have long been facing various forms of academic transgression—defined as any behavior, belief, or condition that violates social and academic norms (Meraji Oskuie et al., 2023a). Academic dishonesty, such as cheating and plagiarism, as one type of transgression, has remained a persistent concern in educational settings (Mohammadkarimi, 2023).

One type of academic transgression is research fraud, which encompasses data fabrication, data falsification, plagiarism, authorship and publication fraud, as well as grant and funding fraud (Christensen Hughes, 2022; Glendinning, 2019; Reisig et al., 2020). From another perspective, cheating can be classified into impersonation and bribery. Impersonation involves claiming authorship of another person’s data, references, or academic work, including plagiarism, ghostwriting, and the use of paper/essay mills. Plagiarism—whether intentional or unintentional (Atmini et al., 2024)—includes both self-plagiarism (recycling fraud) and the plagiarism of others’ work (Siaputra & Santosa, 2016). It is prevalent in higher education across many countries (Atmini et al., 2024), particularly in Iran (Abbasi et al., 2021).

Another academic transgression is ghost authorship (Reisig et al., 2020), also known as the paper mill approach, which involves having someone else do the writing (Abd-Elaal et al., 2019). Using AI and other tools, paper mills offer authorship for a fee (Vasconez-Gonzalez et al., 2024). In the context of GAI systems, ghost authorship can be conceptualized as misattributed co-authorship (Oravec, 2023). All of these are issues of academic integrity (Abd-Elaal et al., 2019)—a commitment to honesty, trust, fairness, respect, and responsibility (Dawson, 2021).

The proliferation of such fraudulent behaviors is a major ethical challenge rooted in the “publish or perish” culture, especially in developing and emerging economies (Vasconez-Gonzalez et al., 2024). This culture prioritizes publication quantity over quality, fueling research fraud and the growth of black-market companies in Iranian higher education (Abbasi et al., 2021; Rokni et al., 2020). The situation is exacerbated by advanced Automatic Article Generators, which produce essays, articles, research papers, books, and theses (Abd-Elaal et al., 2019). The gravity of the issue is echoed in a quote from a master’s student:

“If it weren’t for AI, I wouldn’t have passed any of my courses last semester!”

Cheating emerges from an interaction of motivational, cognitive, and social factors (Leaton Gray et al., 2025), while plagiarism has been linked to institutional, academic, personal, technological, and external causes (Atmini et al., 2024). These behaviors constitute fraudulent activity and can be analyzed using the fraud diamond framework, which attributes fraud to four key elements: perceived pressure (or incentive; Ruankaew (2016)), perceived opportunity, rationalization, and competencies (Atmini et al., 2024), or capacity (Ruankaew, 2016). The integration of AI into academic practices can further influence how these factors contribute to plagiarism (Atmini et al., 2024).

Notably, cheating is contextual and socially constructed. In digital environments, acts and tools are considered e-cheating—digitally technologized cheating—only if deemed forbidden (Dawson, 2021). In this context, AI (Artificial Intelligence) and AI (Academic Integrity) intersect (Xie et al., 2023).

In addition to the factors mentioned above, self-control, job satisfaction, and life satisfaction can also reduce various types of academic transgressions by faculty (Meraji Oskuie et al., 2023a). Self-control, a concept from the general theory of crime, is the ability to prioritize long-term interests over actions that offer immediate or near-term gratification but lead to negative consequences (Gottfredson, 2017).

Self-control develops early in life (Gottfredson, 2017) and can be understood as both a state and a trait (Napolitano et al., 2024). Trait self-control is linked to positive outcomes in happiness, well-being, health, relationships, finances, career, and academic performance. Conversely, low self-control is associated with problematic behaviors such as procrastination, overeating, overspending, substance abuse, delinquency, criminality (de Ridder, 2024; Gottfredson, 2017), academic misconduct (Wang & Zhang, 2022), and AI-assisted cheating (Hawdon et al., 2025).

Despite some mixed findings regarding the predictive effect of self-control on online student cheating (Chen et al., 2020), cultural differences in self-control levels exist between more individualistic, affluent countries and more collectivistic, less privileged ones (Błachnio et al., 2022). In collectivistic societies—where cheating rates are higher than in individualistic ones (Ednadita et al., 2020)—self-control does not clearly account for socially unacceptable behaviors (Błachnio et al., 2022).

Nonetheless, the general theory of crime has received consistent support over decades of research and has been applied across cultures (Meraji Oskuie et al., 2023a) to a wide range of both criminal and non-criminal behaviors (Gottfredson, 2017).

GAI-assisted academic transgressions—primarily among students—have increasingly drawn international attention, yet related research in Iran remains scarce (Meraji Oskuie, 2024), particularly regarding faculty. Therefore, this article combined the fraud diamond framework (Atmini et al., 2024; Ruankaew, 2016) with self-control theory (Gottfredson, 2017) to delve into the factors affecting GAI-assisted cheating by both students and faculty.

Methodology

This article combined critical academic reflective writing (Bowman, 2021b) with side-by-side propositional theoretical integration (Muftić, 2009)—a method I coin as Theoretical “Reflegration”—in a cross-level (macro–micro) and interdisciplinary manner (Krohn & Ward, 2016).

I adopted critical academic reflective writing to bridge the gap between my practical experience as an Iranian Assistant Professor and theory, with the aim of fostering transformative change (Bowman, 2021b). This academic reflective writing aligns with theory, using personal examples to illustrate theoretical principles (Bowman & Addyman, 2014).

Hence, reflectivity was combined with theoretical integration—defined as the act of merging two or more sets of logically interrelated propositions into a larger framework to provide a more comprehensive explanation of a phenomenon (Krohn & Ward, 2016). Propositional integration links theories based on their respective predictions about deviance and crime. Side-by-side integration, a subtype of propositional integration, divides the subject matter into distinct categories and applies different theories to explain each category (Krohn & Eassey, 2014).

Additionally, natural reflective learning from life experiences was used to help build theory from practice (i.e., reflection-on-action), which, in turn, enables reflection-in-action (i.e., real-time difficult decision-making) (Bowman, 2021a). Reflective writing typically includes recounting, description, explanation, and discussion (Heron & Corradini, 2023). Critical reflection can enhance awareness, support metacognition, improve problem-solving, and uncover and utilize unarticulated or unconscious knowledge. To counteract “the myth of silent authorship” (Logan, 2012), I used active voice in some sections. I also presented reflective sentences in italics.

Student Cheating

Every semester, in the first session, I ask the class, “Have you ever heard of Academic Integrity before?” Their answer is always a “NO!” So was mine when I was a student—a long-standing failure of the education system, not of the students.

Despite consistently emphasizing academic integrity, research ethics, and honesty in my classes and coursework guides—and designing assignments to minimize opportunities for cheating—surprisingly, many students still cheat, either intentionally or unintentionally.

Cheating and plagiarism can become habitual (Sozon et al., 2024), motivated by purpose (extrinsic goals and performance orientation), self-efficacy (outcome expectations), and costs (prospects of punishment and self-image) (Leaton Gray et al., 2025). Additionally, AI capabilities have exacerbated these tendencies, creating new challenges for verifying the authenticity of papers and exams. This task is difficult for examiners, especially due to high workloads, limited time (Lukac & Lazareva, 2023), and the need for machine learning tools (Alsabhan, 2023).

Many students, notably at the graduate level, suffer from insufficient and ineffective English as a foreign language education. Many also lack the skills and knowledge for conducting research or even locating valid journal articles. These deficiencies are often more pronounced among students who frequently skip classes. Many try to justify their absences or lower performance by citing job requirements or medical conditions. Occasionally, students feel so desperate that they disclose personal information—placing an emotional burden on me as their teacher. In one such instance, a graduate student—despite receiving assistance from her colleague—submitted a GAI-generated assignment for the English for Specific Purposes course, failing to recognize and remove the AI-generated prologue restatements of the prompt and the typical closing segments at the end.

Perceived pressure—whether real or imagined—drives individuals to engage in unethical behavior (Atmini et al., 2024; Ruankaew, 2016). This includes lower self-efficacy and cognitive ability (Sholikhah et al., 2024), self-doubt (Kangwa et al., 2024), as well as laziness, poor time management, struggling with good academic performance, personal issues, and university-related pressures from courses, assignments, assessments, or deadlines (Leaton Gray et al., 2025; Sozon et al., 2024). Such perceived pressures can drive students to cheat, potentially making it a part of their culture (Sozon et al., 2024). There may also be other sources of pressure:

When discussing students’ goals for attending university, I find many do not prioritize learning—especially in some lower-quality institutions, where learning may not even be a consideration. Many, particularly at the graduate level, pursue university education due to job-related pressures—such as pay raises or promotions—or expectations imposed by family members like parents or spouses. Some may not enjoy their field of study. Yet, many young male students treat university as a refuge to postpone compulsory military service—being willing to continue to the PhD level, merely for that reason.

Such Sociocultural factors—including competition, social rejection, societal norms and pressures, parental pressure, peer dishonesty, and cheating acceptability—along with psychological variables like distress, perfectionism, and self-control, significantly contribute to academic integrity violations (Sozon et al., 2024). However, cultural differences exist in the reasons for, and perceptions of, academic dishonesty (Błachnio et al., 2022).

Many of my students constantly complain about the work-life-study imbalance. The majority of students—whether adult learners or traditional—are employed, and, as a result, struggle to attend classes, complete assignments, and prepare for exams. This pressure, although understandable, can push some toward GAI-assisted cheating.

Besides perceived pressure, perceived opportunity also influences cheating, including one’s subjective view of real or imagined opportunities, the ability or power to exploit them for personal gain, and a low perceived likelihood of detection and adverse consequences (Atmini et al., 2024; Ruankaew, 2016).

Indeed, students’ growing understanding and familiarity with AI’s diverse capabilities open avenues for increased cheating and plagiarism, while also motivating them to justify their actions (Alsabhan, 2023; Stone, 2025). The technological features and affordances of each platform shape cyber transgressions (Meraji Oskuie et al., 2023b). For example, easy copying, sharing, and automated processes that do not require a deep understanding have made e-cheating a far greater threat than traditional cheating (Dawson, 2021).

GAI applications are easy to use and demand minimal knowledge, expertise, or, especially, foreign language skills. One of my graduate students joked, “We don’t need to know English to talk to AI; we’ll teach AI how to speak Persian!” He then sent me a GAI-generated assignment. I felt a mix of frustration and amusement, wondering how he thought I wouldn’t realize it and how naïve his attempt at cheating was.

GAI has also lowered the traditional costs of cheating, such as time, effort, and risk of detection (Leaton Gray et al., 2025). These perceived opportunities require the capacity to perpetrate fraud undetected (Atmini et al., 2024), including: holding an authoritative organizational position; the intelligence and ability to exploit weaknesses in control systems; the ego and confidence to remain undetected; and the capability to manage stress and sustain deception over time (Ruankaew, 2016). Additionally, the lack of motivation to study contributes to cheating (Sozon et al., 2024).

Sometimes, students ask about my salary—wondering whether higher education is worth it. Comparisons to others’ financial situations can discourage them from pursuing their studies, and social and economic uncertainties about future success can deepen this doubt.

Such rationalizations are often deliberate attempts to mitigate cognitive dissonance by engendering indifference toward the committed fraudulent act and justifying it as ethical (Atmini et al., 2024; Ruankaew, 2016). For example, the presence of deviant peers (Hawdon et al., 2025) and peer approval of cheating are factors conducive to academic dishonesty (Sozon et al., 2024). Many students overestimate the prevalence of peer cheating, which, in turn, can predict their own dishonest behavior (Stone, 2025). Indeed, the use of cheating neutralization techniques is associated with all forms of cheating, while a sense of guilt tends to reduce them (Hawdon et al., 2025). Meanwhile, many students—both on-campus and online—may not perceive their actions as violations of academic integrity (Abd-Elaal et al., 2019).

Some students prefer easy, ready-made solutions and become easily frustrated early in doing assignments. While this may be seen as laziness, I believe it can be attributed to somewhat low self-control and attention problems. Many of these students belong to the social media generation, who prefer photos with short captions, brief video clips, and easy-to-follow listicles. Hence, GAI tools fit in with their preference for short, fast, and ready-made content.

Low or depleted self-control can lead to unacceptable behavior (Ednadita et al., 2020), stemming from the inability to resist temptation or opportunity (Silver & Ulmer, 2012). Individuals often encounter motivational conflicts, choosing between short-term self-interest and long-term collective goals (Hofmann et al., 2024). Self-control, as a regulatory “active self,” supports the resolution of most of these conflicts in favor of long-term goals (Gillebaart & Schneider, 2024). It reflects the tendency to weigh the potential costs of an act and consists of a set of inhibitions one carries with them (Silver & Ulmer, 2012). Low self-control is a multidimensional construct, measurable through six components (De Ridder et al., 2012), as shown in part of Figure 1, which integrates the above insights into the fraud diamond framework and low self-control theory:

Insufficient Knowledge

Research Ethics

Computer/Internet Literacy

Research Database Literacy

English Language Literacy

Research Methodology

Skipping Classes

Professional/ Promotional Requirements

Military Service

Family Pressure

Perceived Pressure

Low Self-Efficacy

Low Skills/Abilities

Insufficient Knowledge/Skills

Work-Life-Study Imbalance

Pressure for High Attainment

Capacity

Knowledge of GAI Use

Knowledge of Available GAI Tools

Perceived Opportunity

GAI Affordances

GAI Speed

Ease of Use

Professors’ Lack of Time/Knowledge/ Evidence

GAI’s Language Neutrality

Rationalization

Lack of Interest in Learning/Field of Study

Low Motivation for Learning/Life

Dissatisfaction with Studying

Lack of Purpose

Low Self-Control

Impulsivity

Preference for Simple over Complex Tasks

Risk-Seeking

Preference for Physical over Cerebral Activities

Self-Centered Orientation

Low Tolerance for Frustration

Factors Affecting Generative AI-Assisted Student Cheating

Figure 1: A Conceptual Map of Factors Affecting Generative AI-Assisted Student Cheating

Social Pressure for High Educational Attainment

Role Conflicts

Low Motivation

GAI Platform Affordances

Faculty Cheating

The enticing allure of AI technologies can trap some faculty members into using them to produce articles, essays, or books. As a reviewer for several journals, I have increasingly encountered submissions fully written with GAI tools—especially in English—not only from students but, unfortunately, also from faculty.

Since the launch of ChatGPT, there has been a 72% increase in potentially AI-generated articles (Vasconez-Gonzalez et al., 2024). While student cheating has long been the primary focus of research (Elliott, 2013), studies on faculty misconduct—particularly GAI-assisted cheating and fraudulent review practices—remain scarce.

I submitted an English article to a domestic journal, where the reviewer compromised my original submission by uploading it entirely into a GAI application for evaluation. The resulting comments asked me to explain a keyword—an AI-fabricated term that did not exist in my manuscript!

While GAI tools can help reduce subjectivity, bias, conflicts of interest, and reviewer fatigue (Hutson, 2024), taking credit for an AI-generated review is inherently unethical.

Some professors may have knowledge deficiencies in research methodology, research ethics, English as a Foreign Language, and computer and Internet literacy—resulting in low self-efficacy that can lead to the (in)deliberate misuse of GAI tools.

Low self-efficacy, some intrinsic motivations (Sozon et al., 2024), and a lack of skills or awareness can cause some faculty to (un)intentionally engage in research misconduct (Abbasi et al., 2021). There are also other perceived pressures:

Maintaining a work-life balance, as well as the balance within the teaching-research-service triangle, can be demanding and, at times, frustrating. The negative impacts of these imbalances on job and life satisfaction are undeniable.

Faculty members experience the most unstable work-life balance compared to other roles in higher education and face continuous tensions related to various activities inherent to their profession (Franco et al., 2021). Struggles to balance professional and personal life may lead to job dissatisfaction, exacerbated by family and home obligations, daycare/childcare, health issues, work culture, and personal work challenges (Diego-Medrano & Salazar, 2021), along with role conflict stemming from holding multiple roles (employee, teacher, researcher, parent, and career-building student). These pressures aggravate stress and influence overall work-life satisfaction and well-being (Franco et al., 2021).

The “publish or perish” regime consolidates this high pressure, urging faculty to mass-produce publications. Additionally, many faculty members hold multiple jobs for various reasons. When these pressures intersect, they result in a lack of time, exerting additional stress and pushing some toward quick, easy solutions—while simultaneously providing justification for fraudulent research practices.

The (non)financial benefits of publishing for both researchers and their institutions exert pressure and induce stress to navigate the “publish or perish” (Vasconez-Gonzalez et al., 2024), often by churning out articles that no one reads (Adams, 2006). This can sometimes lead to compromising research ethics just to secure career advancement (Vasconez-Gonzalez et al., 2024). Despite the norm of holding multiple high-ranking positions in Iranian governance (Khabaronline, 2013, January 4), some faculty face financial concerns that compel them to take on multiple jobs.

Career plateauing can also have negative effects on the willingness to acquire new competencies, pursue growth, and conduct research. This can lead some faculty to adopt GAI-assisted cheating as a coping mechanism.

Many factors can contribute to faculty career plateauing—some specific to the Iranian higher education ecosystem. Personal, organizational, systemic, and societal factors can lead to faculty burnout, low-quality teaching and classroom management, disinterest in research and academic duties, lack of empathy toward students, and inflexibility and inability to update their knowledge (Heidari et al., 2020). These diverse factors may trigger different types of plateauing at various career stages (Darling, 2020).

The feeling of impunity among tenured or star faculty—and their perceived organizational irreplaceability—is a capacity for GAI-assisted cheating. Job security, however, can be double-edged, as both its presence and absence can encourage academic misconduct.

Tenure protects faculty while obligating continued professional development and adherence to professional standards. Nonetheless, it can also protect the incompetent. Conversely, job insecurity in the absence of tenure can erode employee morale, loyalty, and trust, without improving productivity (Adams, 2006).

Some higher-ranking faculty or administrators enjoy the privilege of “star power,” with prominent names and charismatic personalities. They secure large grants and, in turn, feel protected by the system. As a result, their serious misbehaviors are often minimized or ignored (National Academies of Sciences, 2018). This sense of immunity can serve as a capacity for perpetrating academic transgression. Another capacity is GAI literacy.

Knowledge of available GAI tools, their affordances, and AI know-how is a significant capacity that contributes to fraudulent research conduct.

Although knowledge of GAI, its use, application, evaluation, and creation are important aspects of AI literacy, this remains incomplete without consideration of ethical issues (Ng et al., 2021). Moreover, some perceived opportunities related to GAI’s affordances can further induce unethical use.

In the chaotic market of higher education, GAI’s affordances—speed, ease of use, and language neutrality—can help alleviate some of the pressures mentioned earlier, especially when others may lack the knowledge, evidence, or time to prove that cheating has occurred.

Additionally, academic dishonesty can be predicted by psychosocial, demographic, academic, and situational factors (Bubaš & Čižmešija, 2023), including extrinsic motivations (Sozon et al., 2024). Such cheating motives among students can reasonably be generalized to faculty cheating and may include the pursuit of job promotion or academic gain, relationship/image management, perceived organizational injustice, conformity to others’ cheating, low risk of detection, lack of severe consequences, situational barriers to research, and a belief that cheating is harmless (Bubaš & Čižmešija, 2023). Cheating behaviors are also influenced by psychological characteristics such as self-control, which in turn, can be affected by metacognitive knowledge, including knowledge of regulatory strategies, task demands, and personal strengths and weaknesses (Hennecke & Kulkarni, 2024). Higher self-control is also linked to sociodemographic factors such as older age, being married, having children, identifying as female, and higher educational attainment (Meraji Oskuie et al., 2023c).

Given their greater age, experience, and advanced education, faculty members might possess higher levels of self-control. But the question is whether they actually want to exert it.

Indeed, a distinction exists between the motivation to exercise self-control in specific situations and the capacity to refrain from deviant acts across various situations (Silver & Ulmer, 2012). Self-control can be inhibitory—effortful, reactive, and intentional (Nilsen et al., 2024)—and, like a muscle, can be temporarily depleted when used (Silver & Ulmer, 2012). It can also be initiatory—effortless, proactive, strategic (Nilsen et al., 2024), automatized, and habitual (Gillebaart & Schneider, 2024). Morality is central to self-control. Moral commitment requires self-control and derives from the perceived moral obligations to others, internalization of action-specific and general cultural norms (Silver & Ulmer, 2012). These insights can be integrated into the fraud diamond framework to more comprehensively explain faculty cheating behaviors, as shown in Figure 2:

Insufficient Knowledge

Research Ethics

Computer/Internet Literacy

English Language Literacy

Research Methodology

Teaching-Research-Service Triangle

Multiple Jobholding

Perceived Pressure

Low Self-Efficacy

Insufficient Knowledge/Skills

Pressure for High Attainment

Capacity

GAI Literacy

Knowledge of Available GAI Tools

Perceived Opportunity

GAI Affordances

GAI Speed

Ease of Use

Others’ Lack of Time/Knowledge/ Evidence

GAI’s Language Neutrality

Rationalization

Lack of Interest/ Unwillingness

Lack of Interest in Research

Dissatisfaction

Career Plateau

Low Self-Control

Impulsivity

Preference for Simple over Complex Tasks

Risk-Seeking

Preference for Physical over Cerebral Activities

Self-Centered Orientation

Low Tolerance for Frustration

Factors Affecting Generative AI-Assisted Faculty Cheating

Figure 2: A Conceptual Map of Factors Affecting Generative AI-Assisted Faculty Cheating

Publish or Perish Regime

Role Conflicts

Low Motivation

GAI Platform Affordances

Sense of Impunity

Job Dissatisfaction

Life Dissatisfaction

Unwillingness to Learn

Work-Life Imbalance

Knowledge of GAI Use

Factors in GAI-Assisted Cheating

To present an integrated map of the factors affecting GAI-assisted cheating among both faculty and students, I have combined the elements from Figures 1 and 2.

Perceived Pressure

Capacity

Insufficient Knowledge/Skills

Pressure for High Attainment

Low Self-Efficacy

GAI Literacy

Perceived Opportunity

GAI Affordances

Others’ Lack of Time/Knowledge/ Evidence

Rationalization

Lack of Interest/ Unwillingness

Low Motivation

Dissatisfaction

Low Self-Control

Impulsivity

Preference for Simple over Complex Tasks

Risk-Seeking

Preference for Physical over Cerebral Activities

Self-Centered Orientation

Low Tolerance for Frustration

Factors Affecting Generative AI-Assisted Academic Cheating

Figure 3: A Conceptual Map of Factors Affecting Generative AI-Assisted Academic Cheating

Sense of Impunity

Role Conflicts

Discussion

Generative Artificial Intelligence (GAI) can contribute to quality education by serving multiple advisory, assistive, and informational roles (Acosta-Enriquez et al., 2024). However, when misused or poorly designed, it can inflict irreparable harm on individuals and society (Ng et al., 2021). Even if only a minority engage in cheating, it must be prevented to preserve fairness, public safety, productivity, and trust in education systems (Dawson, 2021). Emphasizing the responsible and mindful use of GAI tools can help users prepare for a highly collaborative, GAI-saturated future (Oravec, 2023).

While GAI adoption is growing among students and faculty, a significant number remain laggards, deepening the technological divide and worsening performance disparities. At the same time, if using GAI to shortcut academic tasks—from coursework to theses and research—becomes commonplace, how will academic integrity be affected?

Unfortunately, most higher education institutions do not sufficiently emphasize the importance of academic integrity. A culture of ethical behavior should be promoted to educate both students and faculty on the value of academic integrity (Sozon et al., 2024). Ethical and moral development education (Sozon et al., 2024; Xie et al., 2023), academic integrity promotion, awareness campaigns, cultural change, cheating detection (Dawson, 2021), stricter policies and disciplinary measures (Vasconez-Gonzalez et al., 2024), and the formulation of up-to-date honor codes (Sozon et al., 2024) are all vital measures to reduce GAI-assisted cheating.

Here, professors play a crucial role in implementing organizational goals and influencing the learning process and student outcomes through their own performance (Franco et al., 2021). To support this, the literature offers suggestions for assignment design, assessment practices, and detection methods aimed at limiting opportunities for cheating (Meraji Oskuie, 2024).

In addition to various measures for awareness-raising and restricting opportunities for academic transgression, self-control should be incorporated into preventive measures. Interventions can mitigate the effects of low conscientiousness on academic dishonesty by teaching broadly applicable self-control strategies in childhood and training specific behaviors in adulthood (Giluk & Postlethwaite, 2015).

Overall, academic cheating can be reduced by strengthening self-efficacy during stressful situations (Yavich & Davidovitch, 2024), alleviating self-doubt, promoting self-regulation, and addressing factors such as gender, culture, performance, feedback, peer pressure, and proctoring. Employing various resources and techniques to detect and measure academic cheating also plays a crucial role (Kangwa et al., 2024).

Policies, regulations, laws, and initiatives should effectively address perceived pressure, perceived opportunity, rationalization, capacity for cheating, and low self-control to restrain GAI-assisted cheating. Thus, upholding academic integrity in the digital learning era requires a concerted, multifaceted approach (Kangwa et al., 2024).

Ethicality must be the core principle guiding all aspects of GAI technologies—from algorithm development and training to accessibility, equity, and usage. An ethical GAI ecosystem can alleviate some barriers to quality education, particularly in developing and underdeveloped nations, and hopefully mitigate informational, scientific, educational, and developmental gaps.

The ethical and responsible use of GAI has become vital for institutions and educators (Hutson, 2024), as these technologies have the potential to both exacerbate and mitigate socioeconomic inequalities (Capraro et al., 2024). In the long run, well-designed and properly deployed AI can offer widespread access to education across geo-politico-cultural and learning ability barriers (Bulathwela et al., 2024)—but only if used ethically.

Conclusion

The current article aims to delve into factors affecting GAI-assisted academic transgressions by both students and faculty, through theoretically integrating the fraud diamond framework, self-control theory, and personal reflections into three conceptual maps. The integrated conceptual map illustrates perceived pressure (low self-efficacy, insufficient knowledge/skills, pressure for high attainment); rationalization (lack of interest/unwillingness, dissatisfaction, low motivation); role conflicts (a sub-factor shared by both perceived pressure and rationalization); perceived opportunity (GAI affordances, others’ lack of time/knowledge/evidence); capacity (GAI literacy, sense of impunity); and low self-control with its six sub-factors.

The proposed conceptual maps can inform effective solutions and policies to deter and detect at-risk individuals, reduce cheating-conducive situations, and promote academic integrity. This article is limited to theoretical integration and reflections drawn from everyday academic experiences. Therefore, the outlined factors need to be studied further to deepen our understanding and guide effective control measures. The article contributes to the literature by introducing a theoretical integration of the fraud diamond framework and self-control theory within higher education studies. Its originality lies in the coinage of Theoretical “Reflegration”—a method that merges theoretical reflections from academic life with theory integration to support theory building. Future scholars are encouraged to adopt this method to strengthen their integrated theoretical frameworks and generate deeper insights.

Declarations:

Conflict of Interest: The author declares that there is no conflict of interest.

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethical Approval Statement and Informed Consent Statements: Not Applicable.