Between Similarity and Synthetic Authorship: Reconfigurations of Academic Integrity in the Era of Computational Cognitive Systems

Background: Generative Artificial Intelligence (GenAI) technologies such as ChatGPT, Bard, and Claude have emerged as transformative forces in higher education, reshaping teaching, learning, assessment, and research practices. While their integration offers significant potential for personalisation, pedagogical innovation, and productivity enhancement, it simultaneously raises complex concerns regarding ethics, academic integrity, data privacy, and institutional readiness. The fragmented nature of existing research, particularly across Global South contexts such as South Africa, highlights the need for a comprehensive synthesis to guide responsible and equitable GenAI adoption. Purpose: This study systematically reviews recent empirical and conceptual research to examine GenAI readiness, adoption determinants, ethical implications, and regional disparities within higher education. It further identifies theoretical limitations within established frameworks, Technology Acceptance Model (TAM), Technology-OrganisationEnvironment (TOE), Technology Readiness Index (TRI), and Theory of Planned Behaviour (TPB) and proposes a hybrid conceptual framework integrating socio-cultural and ethical dimensions. Methods: A PRISMA-guided systematic literature review was conducted across Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar. Out of 192 identified studies, 18 met the inclusion criteria (2023-2025). Thematic synthesis combined inductive and deductive coding to map patterns across readiness, adoption predictors, ethical issues, and policy trends. Results: Findings reveal four key readiness dimensions: technical access, psychological preparedness, ethical literacy, and institutional support. Predictors of adoption included perceived usefulness, ease of use, selfefficacy, and leadership support, while barriers centred on ethical uncertainty, data privacy concerns, and infrastructural limitations. Conclusions: A hybrid TAM, TOE, TRI, TPB framework, extended with socio-cultural and ethical components, is proposed to explain GenAI adoption holistically. The study recommends targeted AI literacy, ethical governance, and context-sensitive policies to foster equitable and sustainable integration of GenAI in higher education.

Generative artificial intelligence (GAI) has emerged as one of the most disruptive technologies in higher education, transforming how students learn, write, research, and complete assessments. While GAI offers significant academic benefits, including improved access to information, writing support, personalized assistance, and productivity enhancement, its rapid use in higher education has generated serious concerns regarding academic integrity and misconduct. The ability of GAI tools to produce essays, summaries, code, answers, and other forms of academic content has challenged long-standing assumptions about authorship, originality, independent learning, and fair assessment. This article examines the academic integrity and misconduct risks associated with GAI in higher education. Using a narrative literature review approach, the study synthesizes recent scholarship on institutional policies, student behaviors, misconduct patterns, personality predictors, ethical concerns, and preventive strategies related to GAI use in academic contexts. The review finds that GAI-related misconduct is not limited to plagiarism but includes unauthorized assistance, concealed authorship, fabrication, contract-like substitution of academic labor, manipulation of assessments, and misuse of AI-generated content in research and publication. The findings further show that misconduct risks are shaped by institutional ambiguity, weak policy enforcement, assessment design flaws, student perceptions, personality traits, and uneven AI literacy. The article argues that academic integrity in the age of GAI must be addressed through a comprehensive framework that combines authentic assessment, clear governance policies, ethical literacy, early-warning systems, due process, and context-sensitive enforcement mechanisms. It concludes that higher education institutions must move beyond narrow anti-cheating responses and adopt a broader academic integrity strategy that recognizes the complexity of GAI use while preserving fairness, originality, trust, and educational purpose.

In the era of rapid technological advancement, Artificial Intelligence (AI) is a transformative force, permeating diverse facets of society. However, bias concerns have gained prominence as AI systems become integral to decision-making processes. Bias can exert significant and extensive consequences, influencing individuals, groups, and society. The presence of bias in generative AI or machine learning systems can produce content that exhibits discriminating tendencies, perpetuates stereotypes, and contributes to inequalities. Artificial intelligence (AI) systems have the potential to be employed in various contexts that involve sensitive settings, where they are tasked with making significant judgements that can have profound impacts on individuals' lives. Consequently, it is important to establish measures that prevent these decisions from exhibiting discriminating tendencies against specific groups or populations. This exclusive exploration embarks on a comprehensive journey through the nuanced landscape of bias in AI, unravelling its intricate layers to discern different types, pinpoint underlying causes, and illuminate innovative mitigation strategies. Delving deeper, we investigate the roots of bias in AI, revealing a complex interplay of historical legacies, societal imbalances, and algorithmic intricacies. Unravelling the causes involves exploring unintentional reinforcement of existing biases, reliance on incomplete or biased training data, and the potential amplification of disparities when AI systems are deployed in diverse real-world scenarios. Various domains such as text, image, audio, video and more significant advancements in Generative Artificial Intelligence (GAI) were evidenced. Multiple challenges and proliferation of biases occur in different perspectives considered in the study. Against this backdrop, the exploration transitions to a proactive stance, offering a glimpse into cutting-edge mitigation strategies. Diverse and inclusive datasets emerge as a cornerstone, ensuring representative input for AI models. Ethical considerations throughout the development lifecycle and ongoing monitoring mechanisms prove pivotal in mitigating biases that may arise during training or deployment. Technical and non-technical strategies come to the forefront of pursuing fairness and equity in AI. The paper underscores the importance of interdisciplinary collaboration, emphasising that a collective effort spanning developers, ethicists, policymakers, and end-users is paramount for effective bias mitigation. As AI continues its ascent into various spheres of our lives, understanding, acknowledging, and addressing bias becomes an imperative. This exploration seeks to contribute to the discourse, fostering a deeper comprehension of the challenges posed by bias in AI and inspiring a collective commitment to building equitable, trustworthy AI systems for the future.

The rapid advancement of artificial intelligence (AI) has outpaced existing research and regulatory frameworks in higher education, leading to varied institutional responses. Although some educators and institutions have embraced AI and generative AI (GenAI), other individuals remain cautious. This systematic literature review explored teaching academics' attitudes, perceptions and intentions towards AI and GenAI, identifying perceived benefits and obstacles. Utilising the unified theory of acceptance and use of technology framework, this study reveals positive attitudes towards AI's efficiency and teaching enhancement, but also significant concerns about academic integrity, accuracy, reliability, skill development and the need for comprehensive training and policies. These findings underscore the necessity for institutional support to navigate the integration of AI and GenAI in tertiary education. Implications for practice or policy: Attitudes towards AI and GenAI integration are diverse with educators recognising benefits but raising ethical and practical concerns. These concerns indicate a need for a more comprehensive understanding and dialogue within academic communities. Academics' intentions to use these technologies are contingent upon the development of robust ethical guidelines and supportive institutional policies. Institutional support and training shape behaviours. The scarcity of formal training, systematic guidelines and policy frameworks currently limits effective integration.

Artificial intelligence (AI) will revolutionize health science education. And it is happening now. The most recent reason? ChatGPT—a newly available Chatbot AI with pronounced synthesis and language capabilities (OpenAI, 2023). Like previous tech interventions and platforms, such as Twitter, that revolutionized communication and influenced research and public discourses in the health sciences, ChatGPT will not only influence health education, practice and research, but will shift them profoundly. While the time to fully pre-empt ChatGPT development is past, the opportunity for nursing to respond well has not. But first, ChatGPT and its possible impacts on higher education in the health sciences must be considered. Here, we introduce ChatGPT, highlight its likely impact on higher education, and what nursing and the health sciences can do about it. ChatGPT is an AI trained as an interactive conversational model chatbot capable of responding to prompts in various text formats (Gleason, 2022; OpenAI, 2023). ChatGPT runs off of GPT-3 (Generative Pre-Trained Transformer-3), the technology underlying its ability to understand and generate text. This means the application can perform more sophisticated functions in response to users' entries, including—seeking and clarifying through follow-up questions, challenging underlying definitions, and stating and questioning assumptions—among many. There are clear parallels between this and many other scholarly discourses—including the production and evaluation of student writing, conference conversations, and academic publications (Kamler & Thomson, 2006). These sophisticated functions have quickly raised attention. The AI laboratory, OpenAI, only launched ChatGPT on December 2, 2022 and has already gained attention in the mass media and academic press (Gleason, 2022; Graham, 2022; Wingard, 2023) While Chatbots and AI have existed for approximately 60 and 70 years, respectively (Ina, 2022), ChatGPT is different. It will have a transformative effect on higher education, especially around writing and student work. First, it is "generative." ChatGPT can create new text based on a range of inputs, avoiding the rote and repetitive responses of other AI chatbots—a glaring clue for any customer using a commercial AI customer service chat they are not, after all, having their complaint heard by a real person. ChatGPT can also demonstrate sensitivity to context and, from this, it can generate text that sounds natural—more human. Second, ChatGPT is a free to use application, thereby removing the pay-to-access barrier experienced by students seeking to access other AI applications. Given this, and its remarkable capacities, ChatGPT had over 1 million users within the first 5 days of its release (Gleason, 2022). Third, ChatGPT provides virtually instant, comprehensive and logical text responses in any format and genre requested of it (including numerous essay, prose, tweets, LinkedIn posts, or columns). Crucially, some authors report that this text is undetectable by current plagiarism software. For instance, Gleason (2022) asserted that there is no way to prove that ChatGPT generated text is AI generated. Fourth, the scale and complexity of ChatGPT is remarkable; it is among the largest language models ever created with 175 billion parameters, enabling ChatGPT to avoid rigid, script-based responses (OpenAI, 2023). The ability of ChatGPT to respond like a human would is its greatest strength—and paradoxically its greatest risk in scenarios in which a human response is necessary for ethical and scholarly standards and integrity. The possible impacts of ChatGPT on higher education are transformative, and ChatGPT is testing our ability to envision and respond to these. Should those in nursing and other parts of higher education integrate ChatGPT in the service of learning, avoid considering its impact altogether, or acknowledge it but prohibit students' engagement, assuming then that students will abide by this regulation? To address these vital questions first, we asked ChatGPT what its likely impacts are going to be on higher education. Its response was as follows: It is worth noting that GPT-3, although powerful, is not a silver bullet solution for all problems in education and proper implementation and ethical use of the technology is important. Also, there may be some concerns with privacy and security when using GPT-3 in educational settings." ChatGPT was able to provide this coherent, cogent and very human-like response within 10s of receiving the query. It was equally capable of responding meaningfully to follow-up prompts, providing additional information, depth and clarity in its responses, and composing tweets and LinkedIn postings to address the topic. As ChatGPT itself indicated, its profile in higher education is one of balancing benefits with risks, and hence, considering various stances towards its use can help educators and educational institutions makes such critical decisions. Here, we present three different hypothetical case scenarios reflecting responses to ChatGPT by nursing and the health sciences, postulating possible impacts for each. Previously, we argued that Twitter was a conversation that would be ongoing regardless of nursings' acceptance of it (Archibald & Clark, 2014). Nursing and other health professions face a similar, but more challenging decision about whether or not to respond to the emergence of ChatGPT. With already pressing concerns—avoidance is tempting and superficially may feel good, at least initially. Avoidance may stem from many causes, including: fear, a lack of awareness of the existence of this emergent platform and technology, a lack of appreciation of the full scope of its capabilities and possible impacts, or ignorance or dismissal of the possible influence ChatGPT may have on higher education. Yet in the famous words of Abraham Lincoln: "You cannot escape the responsibility of tomorrow by evading it today." Ignoring the existence of ChatGPT or avoiding its inevitable impact on higher education will result in multi-level harms almost immediately. It would be a grave mistake for nursing and the health sciences for many reasons. First, there will be no structures in place to ensure the integrity of student learning, particularly since safeguards would not be in place to determine if student writing was AI generated. Students are writing essays with ChatGPT at the very moment we write this. We are already behind the curve. Second, a 'head-in-the-sand' perspective does little for the professional images of nursing and other health professions. Rather, avoidance may have the negative effect of highlighting a lack of timeliness, or at worst, even make approaches to professional education appear irrelevant. Third, students will not have the benefit of integrating ChatGPT into their learning, meaning the opportunity to treat ChatGPT as another learning tool will be depleted. Fourth, an avoidance approach means that shortcomings in ChatGPT cannot be critically analysed, again undercutting the potential application as a learning tool. By avoiding the use of ChatGPT in higher education, educators also avoid the risk of manifold and serious privacy and security concerns, which ChatGPT adeptly highlighted for us. Without safeguards, students' personal data could be subject to unauthorized access, or other forms of misuse. If not secured, this information could be used for nefarious purposes. For those overseeing nursing (and indeed other professions health education), this risk extends not only to academia and education, but also to public safety. In the prohibition stance, nursing and health science educators and educational institutions take a strong stance against the use of ChatGPT, positioning its use—like essay writing mills—as a direct threat to academic integrity. Such a stance would require the use of browser lockdowns and student oaths or agreements within a punitive lens or model founded on distrust and codified in more bureaucracy (e.g., in forms and declarations). Patrolling and enforcing such measures will be resource intensive and would, in great probability, be ineffective given the complexity of monitoring a large student body, and the possibilities that students may ingeniously find ways to bypass instituted lockdown measures. Further, students may respond with frustration to such restrictive measures that appear to undermine the larger proportion of students who choose to think and act ethically in their scholarly conduct—and truly own their personal ethics and commitment to academic integrity. This may seem antithetical to the adult educational model central to higher education and educational institutions. Moreover it draws attention to the importance of education and prevention in ethical conduct—focusing on helping students (especially those early in their studies) understand what scholarly integrity is and why it is important, not only for the credibility of their eventual qualifications but more widely for public safety and the public good. While the prohibition stance carries with it many of the same shortcomings and challenges encountered in the avoidance stance, such as the inability for students to learn from ChatGPT, there is similarly a risk that prohibition prevents educators from developing students' critical appraisal of ChatGPT's outputs. Specifically, as a powerful tool capable of synthesizing and integrating large and disparate volumes of web-based information, ChatGPT will both reflect and then reproduce and amplify extant biases and stereotypes in this literature. Students require guidance to identify that ChatGPT may replicate these biases, what these biases are, and to formulate their critiques. Such exercises could be powerful learning opportunities for students. Like an avoidance stance, a prohibition stance fundamentally neglects that patients—as consumers of health care—are likely to turn to ChatGPT for knowledge regarding their health concerns or questions. This may, like Internet searching, amplify the individual information seeking behaviours that occur in the absence of access to or support from nurses and other healthcare providers. Similar to Internet searching which is not in itself problematic, nurses and other health professionals must have a working knowledge of ChatGPT—including its shortcomings in providing health information—in order to effectively support and educate individuals within their care. By avoiding or prohibiting the use of ChatGPT in higher education, institutions are less capable of preparing future health professionals for this forthcoming reality. A third possible stance of nursing and the health professions is one of integration of ChatGPT into educational processes and assessments. We align with and advocate for this pragmatic and forward thinking choice—one that accepts the likelihood and inevitable ubiquitous use of ChatGPT in nursing and health science education (similar to the Internet), and leverages, rather than dismisses, its potential implications and harms. However, such integration requires educators to re-imagine assessment to emphasize process over end point, such as the essay as the terminal output for grading (Gleason, 2022). For instance, Gleason (2022) proposes that educators require students to generate a ChatGTP text and use this in comparison to the course readings and objectives to critically appraise the content. Given that ChatGPT can replicate biases present in online text, facilitating such critical appraisal can prove useful pedagogically in developing vital critical and reflections skills—rendering these skills to be more and not less valuable due to the ChatGPT platform. ChatGPT itself indicates that it is fundamentally up to students to abide by and uphold principles of academic integrity (OpenAI, 2023). Contrary to some literature emerging on this topic that stipulates plagiarism detection software is ineffective in detecting ChatGPT generated content (e.g., Gleason, 2022), ChatGPT itself indicates that software exists that is sensitive to ChatGPT generated text (OpenAI, 2023). A team of investigators in the United Kingdom investigated, running student short form essay responses through the plagiarism software Grammarly and TurnitIn with scores of 2% ± 1% and 7% ± 2%, respectively (Yeadon et al., 2022). Based on these scores and the favourable grading that these essays received, these authors concluded that ChatGPT is a major threat to fidelity in their academic context. It is clear that educational institutions must ensure academic integrity policies and detection software is in place, staff are sufficiently attuned and trained to ChatGPT including the current shortcomings and forthcoming advances in plagiarism software, to mitigate the risk of inappropriate use of ChatGPT. Seen as a valid platform that lacks an inherent hostility to academic and professional integrity, ChatGPT should be used—similar to the Internet—fairly and in a manner aligned with student's skill levels. The tool can support complexity, however, learning and applying the tool demands a skillset that must be learned. As such, an integration stance requires rapid adaptation of educational institutions to ensure appropriate staff training is provided on its ethical use and practical applications, comprehensive policies are in place to guide educator and student behaviour, and that timely and responsive risk assessment and mitigation plans are in place to revisit the use and integration of ChatGPT at frequent intervals, given the rapidity of its ongoing development. In conclusion, with the advent and remarkable uptake of ChatGPT, health professionals and educators now face an important decision. As we have inferred above, it is a decision fused for each of us, and collectively for our institutions, with a mix of emotion, projection and reaction. ChatGPT will touch up each—but it is what disciplines, institutions, and people do in response that matters most. Do we choose to see and handle ChatGPT as a tool that, despite its risks, can not only co-exist with but can also improve student learning when approached appropriately? Or do we regard this technology as oppositional and antithetical to learning and scholarly ethics, governed by fear of its misuse and lack of awareness of its potential? How learners engage with higher degree and professional training, and how evaluation can occur within higher education will all be transformed by the public availability of ChatGPT. Despite the possible desire towards a knee-jerk reaction prohibiting its use in education, a more tempered solution is likely the most viable: balance the privacy, security and academic integrity risks with the possible benefits of its application. This position exerts intention, agency, and influence over how ChatGPT will shape higher education (Archibald & Barnard, 2018). Even within an integrative model, educators must recognize the rapid advancements and forthcoming scalability of ChatGPT. The volume of input data used to train ChatGPT is continually advancing. New techniques are being developed to reduce bias in ChatGPT's modelling, which will contribute to the power and applicability of ChatGPT in the future—further heightening its human-like responses, its strengths and its risks. As such, ensuring a flexible process of assessment and resulting institutional approaches and policies will be paramount to keeping pace with the current and rapidly evolving state of this AI technology. In the words of Fasano and White (1982), "by identifying the possibilities (of the future), we can decide more wisely what we should do today to create a better world for tomorrow" (p. 20). All authors have agreed on the final version and meet at least one of the following criteria (recommended by the ICMJE*): (1) substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content. This writing received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. No conflict of interest has been declared by the authors.

The rapid advancement of generative artificial intelligence (AI), particularly tools like ChatGPT, is reshaping educational landscapes by enabling students to generate responses that closely mimic human-written answers. This development presents both opportunities and challenges for e-assessments, especially concerning academic integrity and the authenticity of student learning outcomes. Traditional assessment methods, which often emphasize memorization and standardized testing, are proving insufficient in this new context, as they may not effectively measure higher-order skills like critical thinking, creativity, and problem-solving. This study employs a systematic literature review (SLR) to investigate adaptive e-assessment strategies in higher education that address the integrity challenges posed by generative AI while supporting meaningful learning. Through an in-depth analysis of recent literature on e-assessment practices and AI integration, this study identifies key adaptive strategies such as randomized questioning, project-based assessments, open-book exams, and AI-enhanced plagiarism detection. The findings reveal that while generative AI complicates the assessment process, it also provides an impetus for rethinking assessment design in ways that promote application-based knowledge and discourage cheating. By advocating for a shift towards assessments that evaluate critical skills rather than rote knowledge, this study proposes a framework that can support educators in creating robust, integrity-focused e-assessments. This research contributes to the evolving discourse on educational assessment, offering practical recommendations for institutions aiming to balance the benefits of AI-enhanced learning with the need for fair and accurate assessments.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Artificial Intelligence (AI) tools recently became a prominent concern in higher education classrooms. Many teachers have implemented the technology into their assignments, while others are strictly against this technology’s use for assignments. Either way, students have found ways to use it in their academic careers. Though research on the power of AI in the workplace exists, research is lacking in its appropriate use in higher education. Universities need to define AI’s role on campus and establish guidelines on how these tools may or may not be used and how faculty can recognize misuse, specifically related to academic integrity. This study aimed to determine how faculty view AI as a part of undergraduate literature, language, and linguistics programs. From the interview study, common themes emerged, including implementation, academic integrity, the human aspect of linguistics, and the future of AI writing tools. Interviewed faculty also stated that those in higher education must tread carefully through this strong intersection between technology and the arts to use AI responsibly, strategically, and ethically. KEYWORDS: Artificial Intelligence (AI); Artificial General Intelligence (AGI); Linguistics; Higher Education; ChatGPT; Machine Translation; Academic Integrity; Ethics

As generative artificial intelligence (AI) technologies—such as ChatGPT, DALL·E, and other large language and image models—become increasingly mainstream, they introduce new ethical, legal, and governance challenges that are particularly urgent in developing countries. Nigeria, Africa’s most populous nation and a regional technology hub, offers a compelling case study of how these technologies are being adopted in environments with minimal regulatory infrastructure and limited public awareness. This paper examines the ethical and societal implications of generative AI in Nigeria and interrogates the country's preparedness to manage these risks. Despite the creation of the National Centre for Artificial Intelligence and Robotics (NCAIR) in 2020 and the recent passage of legislation such as the Nigeria Data Protection Act (2023) and the Startup Act (2022), Nigeria lacks a unified national AI formal risk classification systems, or sector-specific ethical guidelines. These gaps are important given the widespread, unregulated use of generative AI tools in education, politics, and digital commerce. In higher education, students increasingly rely on generative AI for assignments and projects, raising concerns about academic integrity in a system already strained by infrastructural deficits. Meanwhile, in the political domain, deepfake videos and AI-generated misinformation have circulated in election periods, threatening democratic stability in a media world prone to disinformation and weak content regulation. The paper compares Nigeria’s regulatory trajectory with global trends, particularly the European Union’s Artificial Intelligence Act and similar initiatives in Kenya, South Africa, and Rwanda. It highlights how Nigeria’s reactive approach to AI governance contrasts sharply with more proactive global models. Sectoral analysis reveals risks including digital labour displacement, cultural misrepresentation through foreign-trained models, algorithmic bias, and the erosion of public trust. Ultimately, the study calls attention to Nigeria’s urgent need for a comprehensive, context-sensitive AI ethics and governance framework. Through an analysis grounded in local realities and informed by global comparisons, the paper contributes to broader conversations about equitable, responsible AI adoption in the Global South.

Generative Artificial Intelligence (GenAI) has attracted growing attention in higher education due to its potential to transform teaching, learning, assessment, and academic production. This article presents a systematic literature review focused on the use of GenAI in university settings, conducted following Kitchenham's methodological model. A total of 41 studies published between 2020 and 2025 were analyzed across major databases such as Scopus, Web of Science, Springer, and IEEE Xplore. The findings reveal a wide range of applications, including learning personalization, automated feedback, academic writing support, content generation, and optimization of teaching and administrative tasks. However, the review also identifies significant ethical, pedagogical, and epistemological challenges, such as academic integrity, algorithmic bias, and the urgent need to strengthen critical digital literacy among both instructors and students. This review systematizes 26 GenAI tools, 21 educational strategies, and 12 ethical implications, providing a comprehensive foundation for institutional policy development and future research. The study ultimately aims to promote the responsible, ethical, and pedagogically meaningful integration of emerging generative AI technologies within higher education.

Generative Artificial Intelligence (GenAI) has attracted growing attention in higher education due to its potential to transform teaching, learning, assessment, and academic production. This article presents a systematic literature review focused on the use of GenAI in university settings, conducted following Kitchenham's methodological model. A total of 41 studies published between 2020 and 2025 were analyzed across major databases such as Scopus, Web of Science, Springer, and IEEE Xplore. The findings reveal a wide range of applications, including learning personalization, automated feedback, academic writing support, content generation, and optimization of teaching and administrative tasks. However, the review also identifies significant ethical, pedagogical, and epistemological challenges, such as academic integrity, algorithmic bias, and the urgent need to strengthen critical digital literacy among both instructors and students. This review systematizes 26 GenAI tools, 21 educational strategies, and 12 ethical implications, providing a comprehensive foundation for institutional policy development and future research. The study ultimately aims to promote the responsible, ethical, and pedagogically meaningful integration of emerging generative AI technologies within higher education.

Purpose of the study: This systematic literature review investigates the integration of Artificial Intelligence (AI) technologies within blended learning environments in higher education institutions globally, published between 2020 and 2025. The study aims to synthesize empirical evidence on AI-enhanced blended learning models, identify prevalent AI tools and pedagogical approaches, evaluate learning outcomes, and map research trends, challenges, and future directions. Materials and methods: Following the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search was conducted across five major academic databases: Scopus, Web of Science (WoS), ERIC, IEEE Xplore, and Google Scholar. Search terms combined AI-related terminology with blended learning and higher education concepts. Studies published between January 2020 and March 2025, written in English, employing empirical or quasi-experimental designs, and focusing on tertiary education were included. After rigorous screening and quality assessment using the Mixed Methods Appraisal Tool (MMAT), 47 studies met the inclusion criteria and were subjected to thematic synthesis and descriptive analysis. Results: The analysis of 47 eligible studies revealed six dominant AI application categories in blended learning: intelligent tutoring systems (ITS) (27.7%), natural language processing and chatbots (23.4%), adaptive learning platforms (21.3%), AI-driven learning analytics (14.9%), AI-based assessment tools (8.5%), and generative AI tools (4.3%). The majority of studies reported statistically significant improvements in academic performance (85.1%), learner engagement (78.7%), and personalized learning experiences (72.3%). Key challenges identified include algorithmic bias, data privacy concerns, insufficient instructor AI literacy, and inequitable digital access. Conclusions: AI integration in blended learning environments demonstrates significant promise in enhancing pedagogical effectiveness and learner outcomes in higher education. However, sustainable and equitable deployment requires robust ethical frameworks, targeted professional development for educators, and inclusive institutional policies. Future research should prioritize longitudinal studies and cross-cultural comparative analyses.

Integrating Artificial Intelligence (AI) tools, particularly generative AI (GenAI), in higher education is reshaping assessment practices, presenting both challenges and opportunities. While these tools enhance learning, they also raise concerns about academic integrity and the authenticity of student work. Traditional assessments, such as essays and take-home assignments, are increasingly susceptible to AI-assisted plagiarism, necessitating a re-evaluation of assessment strategies. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, examines educators' challenges in assessing student learning in AI-infused environments. Using Scopus, IEEE Xplore, and ScienceDirect, we identified relevant literature highlighting concerns about originality, critical thinking evaluation, and the quality of student work. Findings underscore the need for AI-resistant, process-based assessments, such as oral exams and multi-stage evaluations, to uphold academic integrity. The study advocates for institutional AI policies and digital literacy programs to promote ethical AI use and mitigate academic misconduct. Additionally, it emphasises a balanced human-AI collaboration in assessments, ensuring that AI enhances rather than replaces student effort. Addressing these challenges can reduce academic misconduct cases, allowing educators to focus on fostering meaningful learning experiences and sustainable educational outcomes.

The rise of generative artificial intelligence (AI) has notably transformed higher education practices, raising complex issues around academic integrity, authorship, and ethical scholarship. Unlike previous digital tools, generative AI can independently generate academically credible texts, challenging traditional ideas of originality and student agency. This paper examines the evolution of the concept of academic integrity in AI-enabled learning, particularly within Indian higher education. Drawing on interdisciplinary perspectives from educational ethics, sociology, learning sciences, and AI studies, it analyses integrity-related dilemmas influenced by factors like large enrolments, exam-focused cultures, linguistic diversity, varying AI literacy, and socio-economic gaps. The research argues that current compliance-based integrity systems, mainly relying on plagiarism detection, are inadequate for handling AI-influenced academic work. Instead, it advocates for a learning-focused redefinition of integrity emphasizing transparency, epistemic responsibility, critical thinking, and demonstrable understanding. To support this, the paper suggests pedagogical and ethical strategies such as redesigning assessments to focus on reasoning and process, providing clear guidance on responsible AI use, developing critical AI literacy among faculty and students, and institutionalizing transparent AI-assisted academic practices. This work contributes to global discussions by highlighting AI-related integrity issues within the unique context of Indian higher education.

Artificial Intelligence (AI) has revolutionized several industries, and scientific publishing is no exception. From peer review automation to the detection of plagiarism and manuscript proofing, AI is revolutionizing research production, dissemination, and evaluation. Although AI brings tremendous potential to automate publishing, it raises significant questions regarding ethics and integrity that must be addressed correctly (1). The most significant impact that AI has made on publishing is speeding up the peer review process. Traditional peer review is laborious and time-consuming, and it tends to result in very lengthy publication cycles. AI tools can assist in pre-screening submissions, finding possible reviewers according to expertise, and even identifying ethics concerns like duplicate publication or image tampering. Some AI tools, like ScholarOne and Editorial Manager, have already started using machine learning algorithms to recommend reviewers and detect probable conflicts of interest, making an efficient and unbiased review process possible (2). Besides peer review, AI has also improved the editorial process by employing language processing models that assist authors in manuscript editing. These include products like Grammarly, Writefull, and Paperpal, which use AI-driven natural language processing (NLP) to correct grammar, simplify the language, and improve readability. This proves helpful in non-native English-speaking academics, who may be unable to present research findings effectively. Also, AI-driven translation software is breaking language barriers, allowing for research dissemination across linguistic groups (3). The detection of plagiarism has also shifted fundamentally with the advent of AI. Conventional software such as Turnitin and iThenticate have come a long way, using deep learning algorithms to identify more evolved instances of academic fraud, including paraphrasing plagiarism and AI-generated content. After the proliferation of generative AI tools such as ChatGPT, the difference between human-written and machine-written content has become more difficult to distinguish, calling for increasingly sophisticated AI-driven authenticity checks (4). But publishing with AI is not without issues. The ethical aspects of AI-generated research content are becoming a problem more and more. The greater the dependence on AI writing aids, the greater the problems concerning authorship, novelty, and intellectual property. The majority of journals now have strict policies for using AI-generated content and being open and accountable in scientific publishing. Second, the risk of bias in AI algorithms is still an issue because AI algorithms learn from what is already in print form, thus potentially continuing existing biases in publishing materials (5). Besides that, AI is transforming the availability of scientific literature. AI-based recommendation platforms such as Semantic Scholar and Scite simplify scientists' ability to locate pertinent literature by analyzing citation patterns and trend research. Open-access journals also leverage AI to increase content published therein and make it more accessible and readable to audiences, thereby democratizing knowledge dissemination. Despite all these advancements, human judgment remains unavoidable in publishing. No matter how much help AI will extend, ethical decisions and contextual appreciation are still impossible without human ability. Symbiosis with AI as a helper, but not a replacement for writers, editors, and referees, is the best means to achieve this (6). Last but not least, AI is undoubtedly revolutionizing the terrain of scientific publishing. Its capacity to simplify workflows, enhance quality, and enable accessibility is charting the future of academic communication. With these opportunities, however, come the ethics and integrity issues that need to be resolved in order to achieve responsible AI deployment. As the world of publishing advances, a delicate balance that harnesses the power of AI while maintaining the integrity of academics will be critical in sustaining trust and credibility within the science literature.