TECHNOLOGY INTEGRATED PEDAGOGY AND LEARNING

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.

In this article, we investigate the critical intersections of AI, academic integrity, and disability in the context of a large undergraduate course. Our aim was to adapt the course to respond to generative AI (GenAI) to avoid entrenching barriers for students, and instead teach them how to use GenAI tools in ways that deepen their learning and uphold academic honesty. Grounded in disability justice and access pedagogies, we outline five design goals centered on guidelines for AI usage, education on responsible AI use, revised assessments, support for teaching assistants (TAs), and accessible materials. These activities are detailed in our methodology. In our findings, we provide a critical reflection of the course adaptation, taking up issues such as varying levels of familiarity with GenAI, students’ capacity to engage with course changes, resistance to GenAI, instructors’ relational shifts to AI, and feelings of demoralization among the teaching team. We conclude by offering practical recommendations for educators, calling for learning communities to view this disruption as an invitation to listen to disabled students.

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.

ACADEMIC INTEGRITY IN HIGHER EDUCATION INSTITUTIONS

Significantly impacted education, necessitating effective integration into teaching practices. This study examined the application of the Substitution, Augmentation, Modification, and Redefinition (SAMR) model to evaluate ICT integration in English Language Teaching (ELT) at the tertiary level. The research aimed to determine whether the SAMR model could enhance pedagogical practices beyond mere technology substitution and how it compared with the Technological Pedagogical Content Knowledge (TPACK) model. This study addressed the issue of stagnant ICT integration in ELT, hypothesizing that the SAMR model provided a more robust framework for transformative technology use compared to the TPACK model. The study’s purpose was to assess the SAMR model’s effectiveness in advancing ICT integration in university-level ELT. Objectives included comparing SAMR with TPACK and identifying barriers to effective ICT use. A systematic literature review was conducted using six electronic databases covering the past decade (2013-2023). The review followed PRISMA guidelines with defined inclusion and exclusion criteria. Preliminary results revealed that while the SAMR model offered the potential for enhancing teaching practices, many educators remained at the substitution stage, mainly replacing traditional methods with technology without significant pedagogical changes. Challenges included insufficient training, institutional resistance, and lack of support. The findings underscored the need for extensive training, supportive policies, and continued research to improve ICT integration in higher education. This study contributes to understanding effective technology integration and provides recommendations for institutions seeking to elevate their teaching practices through meaningful ICT adoption. This research offers insights and practical recommendations for enhancing technology integration in higher education, focusing on overcoming current limitations and advancing teaching methodologies through the SAMR model.

This study explores the spatial dynamics of higher education (HE) integration in China’s Greater Bay Area (GBA), focusing specifically on short-term cross-border academic mobility. Informed by Lefebvre’s (1991) spatial theory and Marginson’s (2022b) glonacal agency heuristic, a mixed-method convergent design was employed, integrating infrastructure and funding statistics, policy documents, and interviews with cross-border faculty. Findings identify three clear spatialities shaping the GBA HE space: innovation-oriented spatiality, mainland-inclined integration spatiality, and integrated everyday-life spatiality. These spatial configurations are influenced by global, national, and local forces. In response, academics engaged in reflexive practices across multiple scales, using their agency to navigate and reshape the space, creating hybrid spaces that transcended top-down planning. The study contributes to the global discourse on academic mobility and cross-border collaboration in HE, demonstrating how scholars’ mobility can transform a policy-driven HE integration into a more adaptable, collaborative, and inclusive environment. The findings offer valuable insights for other regions prioritising cross-border HE collaboration, particularly those seeking to balance local development with global aspirations.

<em>Nowadays, with the rapid breakthroughs in software and AI technology, the growing use of AI among university students has sparked considerable debate. In this context, the present research seeks to understand the impact of AI-assisted writing tools on students' perceptions of fairness, academic integrity, and their learning outcomes. An exploratory sequential design was used in this study, beginning with two focus groups to gather qualitative insights that informed the development of a 22-item survey. The survey was then administered to 107 students from the English departments at three institutions of Mohamed V University: FLSH, FSE, and ENS. The results indicate that AI use significantly affects students’ perceptions of fair assessment, with many viewing it as a tool that enhances work quality and academic grades. However, the use of AI is also associated with reduced motivation, lower willingness to invest effort, and diminished self-confidence. To address these challenges, participants suggested alternative assessment methods, including oral exams, tasks that emphasize critical thinking and creativity, and group work. By Analyzing these findings through the lens of Equity Theory and in light of existing research, this study offers insights into the evolving dynamics of fairness and academic integrity in the era of AI. It also offers recommendations for adapting assessment practices to maintain equity and integrity in higher education.</em>

IntroductionThis study investigates higher education teachers’ perceptions of active teaching methods—Case-Based Learning (CBL), Problem-Based Learning (PBL), and Team-Based Learning (TBL)—and their integration with digital technologies. These pedagogical strategies are crucial for fostering critical thinking, collaboration, and problem-solving skills among students, especially in the digital age. Despite their benefits, challenges such as resource limitations, time constraints, and insufficient training hinder their implementation.MethodsA mixed-methods approach was adopted, involving 104 educators from various higher education institutions in Kazakhstan. Participants underwent an eight-week training program consisting of 72 h of workshops, collaborative activities, and self-paced learning. Pre- and post-training assessments measured knowledge, confidence, and practical application of active teaching methods. Surveys, feedback sessions, and peer evaluations provided qualitative and quantitative insights into participants’ experiences and challenges.ResultsThe training program significantly improved participants’ knowledge, confidence, and ability to implement CBL, PBL, and TBL. Post-training assessments showed a 4.2-point increase in confidence and practical application scores. Most educators (66.35%) reported moderate adaptability of these methods to their current educational programs, while 27.88% achieved high adaptability. Participants identified resource limitations (43.27%) and time constraints (33.65%) as key challenges. Problem-Based Learning (PBL) was preferred for fostering critical thinking and problem-solving, with a majority (51.92%) favoring it over other methods. Digital technologies, such as Moodle and augmented reality tools, enhanced teaching effectiveness and student engagement.DiscussionThe findings highlight the transformative potential of active teaching methods and digital integration in higher education. Educators across various academic positions and institutions perceived these methods as highly effective, aligning with the Diffusion of Innovations Theory. Challenges such as insufficient resources and curriculum misalignment were significant barriers to broader adoption. Continuous professional development, institutional support, and strategic resource allocation are crucial for optimizing these methods’ impact.

While there is a growing body of research on the role of ChatGPT in English language teaching and learning, its application in diverse language education remains underexplored. This study addresses this gap by examining how language instructors of non-English languages integrate ChatGPT into their teaching practices. Adopting a phenomenological case study approach, we explored the lived experiences and perceptions of 13 language instructors at a Midwestern U.S. university. Participants were purposefully selected based on three criteria: (1) they taught their native language as a second or foreign language in higher education; (2) they attended a workshop on ChatGPT facilitated by the research team; and (3) they were non-native English speakers who had used ChatGPT for their own English learning. Data were collected through semi-structured interviews conducted one month after the workshop. Interviews were recorded, transcribed, and thematically analyzed following Braun and Clarke’s (2006) six-step process. The analysis revealed how participants used ChatGPT to support language instruction and learning, the perceived benefits and challenges of ChatGPT in multilingual contexts, and evolving teacher roles in the era of AI. Findings underscore the need to expand ChatGPT’s multilingual capabilities and support educators in integrating AI responsibly and effectively. This study contributes practical insights into how AI tools can complement human instruction in diverse language classrooms and inform institutional support and policy development for AI integration in higher education.