Evolving intersections: AI, disability, and academic integrity in higher education

Purpose This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.Design/methodology/approach This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.Findings With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.Practical implicationsThis study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.Originality/value This study proposes a new, innovative framework summarising the balance between AI and academic integrity.

The field of academic integrity in higher education has made significant gains in exploring the proliferation of integrity issues, the frequency of student misconduct behaviours, and in identifying strategies for embedding academic integrity education more broadly into the curriculum. Regardless of calls for institution-wide approaches which focus on preventing academic misconduct, those of us engaged in the field can attest that there will always be a need to address academic misconduct behaviours and support the development of those students who engage in them. As student affairs practitioners in a Canadian post-secondary institution, we present our approach to creating meaningful teaching and learning experiences that enable students with misconduct violations to critically explore potential misconduct situations and practice the skills needed to make alternative decisions. Utilising existing work that frames academic integrity as ‘standards of practice’, this chapter demonstrates our application of key themes from the academic integrity literature within our teaching and learning practice. Recognizing that mandated academic integrity education can be a challenging learning experience, we discuss our approach to engaging these students in analyzing the common situational factors that post-secondary students face that pose potential academic integrity conflicts and the way ethical decision-making frameworks can support their ability to navigate academic integrity concerns in the future. We conclude the chapter with our key learnings and recommendations for implementing an engaging experience with students who are mandated to attend instruction following an academic integrity violation.

Redefining academic integrity in an artificial intelligence (AI)-driven world is now a pressing issue for higher education. This paper examines the implications of AI on academic integrity in higher education, focusing on how AI tools challenge traditional notions of originality, authorship, and ethical accountability in academic content creation. The shift in focus is from detection and punishment to ethics and pedagogy, guided by the notion of enabling students to use AI ethically. Case studies from Southeast Asia reveal how institutions are responding to AI concerns with inclusive pedagogies, disclosure-based policies, and frameworks for transparency and accountability. This paper argues that academic integrity accounts for the needs of diverse learners to support equitable access to institutional learning opportunities and resources. Institutions need to make the necessary changes to continuously adapt to the rapid development of AI technologies. By reimagining academic integrity, higher education can uphold its responsibilities in an AI-driven future.

The article is devoted to the study of the role of pedagogical methods in the formation of ethical values of higher education seekers through the prism of academic integrity. In the context of modernisation of the educational space of Ukraine, academic integrity is considered a key tool for fostering responsibility, honesty and professional ethics among future specialists. The article defines the essence of the concept of ‘academic integrity’, analyses its basic principles and substantiates its connection with moral and professional qualities of the individual. Particular attention is paid to pedagogical methods that promote the integration of the principles of academic integrity into the educational process, in particular problem-based learning, discussions, project work and methods of reflection. It is shown that their systematic use develops critical thinking, the ability to objectively analyse information, and forms internal motivation to comply with ethical standards. It is substantiated that violations of academic standards, such as plagiarism or falsification, not only reduce the quality of a specialist's training but also undermine their competitiveness in the modern labour market. It is concluded that the effective application of pedagogical methods in combination with the principles of academic integrity is a necessary condition for the formation of highly qualified, morally stable and socially responsible professionals. Directions for further research aimed at developing practical recommendations for the implementation of a culture of academic integrity in higher education are proposed.

Managing student academic misconduct in tertiary institutions is a complex and ongoing challenge. When investigations confirm academic misconduct, authorities typically impose disciplinary action, require engagement in an educational intervention, or a combination of both. The impact of educational interventions following academic misconduct findings has received limited attention in the literature. Some educational interventions require students to produce reflections, which can serve as valuable feedback to academic integrity policies and procedures. This research utilised 3070 student reflections collected over three years and thematically analysed to uncover insights from the aggregate student voice. Three themes were identified about what students reflected to be the cause of the behaviour that led to the misconduct: students’ lack of understanding about academic integrity, their time management practices, and lack of perceived self-efficacy. The themes and their notional causal nature can drive efforts to strengthen student knowledge and skills about academic integrity to include a stronger focus on effective time management and self-efficacy. Such efforts can be implemented as proactive educational interventions to support student learning during orientation activities or as a part of disciplinary outcomes imposed after a misconduct finding. Educational institutions can also use these themes to guide the development of academic integrity policies to effectively manage student misconduct into the future.

The Covid-19 pandemic forces us to do distance learning to suppress the spread.However, there are problems, and one of them is academic dishonesty in providing sources of information, compiling references, doing assignments, and other academic violations.This research aims to determine the effect of distress, ethical attitudes, and self-efficacy on academic dishonesty.Academic integrity is a serious issue that impacts not only universities but also the government, society, and workplace.This research was carried out in Department of Economics Education at the Faculty of Economics in Universitas Negeri Medan, where distance learners are the main emphasis.The Structural Equation Model was employed in the data analysis process (SEM).According to the study's findings, academic dishonesty and the ethical attitude variable will be significantly impacted by distress, which will also have an influence on diminishing ethical attitudes.Nevertheless, self-efficacy did not show a significant effect on academic integrity.The study's findings offer a summary of the state of students' academic integrity in higher education and serve as a template for legislation aimed at enhancing it.

This paper provides a review of current research on academic integrity in higher education, with a focus on its application to assessment practices in online courses. Understanding the types and causes of academic dishonesty can inform the suite of methods that might be used to most effectively promote academic integrity. Thus, the paper first addresses the question of why students engage in academically dishonest behaviours. Then, a review of current methods to reduce academically dishonest behaviours is presented. Acknowledging the increasing use of online courses within the postsecondary curriculum, it is our hope that this review will aid instructors and administrators in their decision-making process regarding online evaluations and encourage future study that will form the foundation of evidence-based practices.

The article provides a theoretical and methodological analysis of the ethical imperatives of scientific research in the context of academic integrity in higher education. Academic integrity is interpreted not only as a formal regulatory requirement but also as a fundamental value-based and methodological foundation of research activity carried out by students, university teachers, and young scholars. The study emphasizes that adherence to ethical principles ensures the validity, reliability, and social significance of scientific results and contributes to the development of a responsible research culture within the academic community. Special attention is paid to the challenges facing modern higher education in the era of digitalization, particularly those related to the rapid spread of digital resources and the active use of artificial intelligence tools in research and learning processes. The risks associated with plagiarism, self-plagiarism, fabrication and falsification of data, as well as the potential blurring of authorship boundaries in the context of generative technologies, are analyzed. In this regard, the importance of developing methodological literacy and ethical responsibility among students and young researchers is highlighted. The article also outlines the significant role of university teachers in shaping students’ value orientations regarding academic honesty and responsible authorship. It is argued that ethical principles should be systematically integrated into the educational process through courses on research methodology, pedagogy and psychology of higher education, and academic writing. A systemic approach to implementing the principles of academic integrity is proposed, combining pedagogical, organizational, legal, and technological mechanisms aimed at strengthening ethical standards in higher education institutions and ensuring the credibility of scientific knowledge in the global academic environment.

Objective: This study investigates the integration of digital technologies in pedagogy and learning, with a focus on English language instruction and general academic contexts in non-English-speaking regions, particularly Uzbekistan. Method: Employing a qualitative descriptive approach, data were collected through interviews and observations involving both students and teachers to examine perceptions and experiences related to digital tools such as mobile applications, AI-assisted platforms, and digital libraries. Results: The analysis reveals that digital integration fosters learner autonomy, enhances access to information, and supports personalized learning. Students report improved efficiency and language development, while teachers recognize both the supportive potential and challenges of AI in instructional settings. However, issues such as over-reliance on technology, risks of academic dishonesty, and reduced interpersonal interactions are also identified. Novelty: Distinct from prior studies, this research offers a dual-perspective analysis—juxtaposing institutional pedagogical strategies with student lived experiences—and identifies specific digital applications that facilitate language acquisition and academic time management. The study emphasizes the importance of structured digital integration, informed digital literacy, and a balanced approach to innovation and academic integrity in higher education.

The rapid development and availability of Generative AI (GenAI) tools, such as ChatGPT, offer new possibilities for academia, yet they also raise concerns for academic integrity. Chapelle's (2024) seminal work on GenAI’s implications serves as an impetus for this study, which explores teaching academic integrity in the context of AI use. Chapelle frames GenAI as an ‘inflection point’, calling for a re-evaluation of pedagogical strategies to support students in understanding its technological and ethical implications. This article presents findings from a mixed-methods study at a university in Cyprus, where ESL undergraduates in academic writing classes engaged with ChatGPT. Participants with B2/C1 English proficiency from diverse cultural backgrounds were assessed through pre- and post-intervention evaluations and qualitative interviews. A semester-long intervention incorporated activities to enhance academic writing skills and comprehension of ethical AI use, focusing on critical thinking, proper citation, and policy adherence. Findings indicate that students demonstrated increased AI knowledge and awareness of academic integrity. This study contributes to the discourse on integrating GenAI in academia, providing insights into effective instructional practices and addressing ethical considerations in academic writing. It offers guidance for educators and institutions aiming to integrate GenAI responsibly, ensuring academic integrity remains central in second language education.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

ACADEMIC INTEGRITY IN HIGHER EDUCATION INSTITUTIONS

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce.

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.

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.