高校考试管理制度

教育部、国家体育总局联合印发《关于进一步完善和规范高校高水平运动队考试招生工作的指导意见》，旨在通过考试招生端的重大变革有效推进高校高水平运动队建设与发展。认为：在理念引领方面，紧跟新时代新发展步伐，通过找准办队定位贯彻新发展理念、推动高质量发展，在考试招生全过程中促进教育公平，并深化体教融合，推动部门协作、目标融合与资源共享；在现实关切方面，力求服务国家竞技体育发展战略，回归高水平运动员培养定位，回应教育公平现实诉求，推动高校高水平运动队建设提质增效。提出推进策略：推进体教融合发展；推行高校高水平运动队“注册制”改革；推动青少年体育赛事体系融合；推进高校体育学类专业建设；针对不同运动项目精准施策；增强政策执行的解释性张力，发挥基层实践智慧。

营造有利于学生创造性思维培养的制度环境，是建设教育强国的重要基础。本研究聚焦高利害考试这一关键制度，立足多重制度逻辑视角，基于PISA2022数据，运用三层线性模型、分位数回归、跨层交互及中介效应检验等方法，考察高利害考试对学生创造性思维的影响及机制。结果显示：（1）从全球整体趋势来看，考试问责程度越高的国家和地区，学生创造性思维得分越低；（2）高利害考试对学生群体的影响呈现明显分化，认知能力较低、创造性思维水平较弱及家庭资源弱势学生受到的负面影响更大；（3）高利害考试对学校影响亦呈分化趋势，教育质量较低、资源相对匮乏的学校受负面冲击更大；（4）作为承接制度压力的中间组织，学校在高利害考试压力下往往会增加与“创新”相关的活动，但这些活动偏向形式化、象征性展示，难以真正促进学生创造性思维发展，即使是资源充足的学校，也会陷入开展形式化创新活动的倾向。对于我国而言，需深化教育评价改革，弱化单一高利害考试主导地位，构建多元化评价体系，为创新人才培养提供更具支持性的制度环境。

从我国教育法基本理念的内涵和历史演进来看，受教育权的保障与实现构成了教育法典分则的基本理念，这可以从正当性和可行性两个方面来证成。基本理念对教育法典分则编纂的统摄效力不仅体现在基本理念的价值和功能，也体现在保障受教育权是教育法典总则的核心宗旨。在方法论方面，分则编纂宜采取“横向教育法律关系+纵向教育法律体系”的“入典”标准，充分利用好“援引”这一立法技术，选择“总分结构”的体例设计，吸收“提取公因式”的立法技术，并确立“先主体后客体”的价值位阶。在此基础上，教育法典分则部分由教育主体编、学校教育编、教育与家庭和社会编、特殊事项编等子部门法构成。其中，教育主体编包括学校法律制度、教师法律制度和学生法律制度；学校教育编包括学前教育制度、义务教育制度、高中教育制度、高等教育制度、职业教育制度、特殊教育制度、学位制度、考试制度；教育与家庭、社会编主要包括家庭教育制度、终身教育制度；特殊事项编主要规定少数民族教育制度、国家通用语言文字制度、中外合作办学制度等。

高校综合评价招生模式肇始于国家基础教育新课程改革和实施素质教育的要求,并在高考综合改革试点中逐步完善。高校综合评价招生模式所采用的标准是一个包括国家统考、高校综合测评和高中学生综合素质评价等在内的相互嵌套的综合评价体系。国家统考体现国家意志,突显统一考试的权威性;高校综合测评体现学校自主权,突出人才选拔标准的针对性;高中学生综合素质评价彰显素质教育理念,突出学生评价的育人性。综合素质评价招生模式体现了国家、高校以及以高中为代表的基础教育的多元价值诉求,有助于促进学生综合素质的发展,推动教育治理体系的完善。高校综合评价招生模式改革需要着力解决评价理论缺位、评价功能错位、评价主体越位的问题。加强教育评价理论研究,明确综合评价的功能定位和价值取向,厘清教育评价主体职责是高校综合评价招生模式改革的基本走向。

从主体间性的角度看教育评价，教育评价是泛在的价值操作过程，也是意义建构的过程。在教育和评价之间，存在正式和非正式的教育与正式和非正式的评价之间的组合模式。正式教育的正式评价起着专业化发展的引领作用。教育评价在特定场域展开，受场域的影响。教育评价具有治理的功能，是治理的工具。从个体到组织、国家乃至国际，治理的层级不同，评价的内容也不同。善治既是对国家治理的评价，也具有教育治理的指导价值。评价具有导向性作用，评价也是治理的对象。从评价治理的角度上说，教育评价要持自反性的立场，对评价保持再评价的开放思维。一方面增强元评价意识；另一方面，还要不断对现实问题的场域特征保持警觉，警惕教育评价陷入行为主义、经济主义、地方主义和机会主义的陷阱。

随着信息化发展，传统考试存在组织成本高、批改耗时、作弊难控等问题。为此，本研究开发“同文在线考试系统”，集成自动组卷、实时监控、在线答题、自动评分及成绩分析等功能，旨在提升考试管理效率与质量，保障公平性。该系统已在山西同文职业技术学院落地应用，覆盖高职英语等公共课以及各系部专业课考试。用户反馈其操作简便易上手，运行稳定可靠，自动化特性大幅降低考试组织与批改成本，实时监控和防作弊措施有效遏制违规行为，确保考试公正；成绩分析功能为教育工作者提供精准数据支持，有力推动教育评估现代化转型，助力教育公平与质量提升，应用前景广阔。

The relationship between teachers and students in higher education has now developed into a game of two-way interaction, with both sides often clashing over the choice of curriculum assessment methods. Curriculum examination is a crucial factor in evaluating the quality of higher education. Additionally, it significantly impacts the fairness of education and students’ motivation to learn. To resolve such conflicts, we analyze the teacher–student game psychology using the conflict analysis method. Then, based on the overall stability of the situation, we have come to the conclusion that we need to adopt “innovative examinations”. Specific recommendations were made to teachers and students through the analysis of the results, and then the integration of the influencing factors proposed optimization strategies aimed at ensuring the fairness of the examination, based on the choice of the type of course, with reference to the reality of teachers and students, and oriented to the educational and teaching environment. We provide practical guidance for selecting and optimizing assessment methods to improve their appropriateness for individualized teacher and student needs. This promotes the process of teaching reform and helps achieve sustainable development in education.

Large-scale paper-based examinations (PBEs) in computing education frequently emphasize rote memorization, thereby misaligning instructional objectives with assessment techniques. Such incongruities hinder the preparation of students for real world challenges in both industry and academia by inadequately evaluating higher-order cognitive abilities. Often, educators are deterred from implementing comprehensive skills assessment due to the perceived complexity and resource-intensive grading processes involved. To mitigate these limitations, this paper introduces an exam mode as an integral feature of the open-source learning platform Artemis. Designed for both local and cloud-based deployment, this exam mode incorporates anti-cheating protocols, automates the grading of diverse exercise types, and features double-blind manual grading to ensure assessment integrity. It fosters the evaluation of complex cognitive skills while substantially reducing the administrative load on faculty. This paper substantiates the effectiveness of the Artemis exam mode through widespread institutional adoption, demonstrated by over 50 successful computer-based examinations (CBEs). An in-depth case study involving 1,700 undergraduate software engineering students offers key insights, best practices, and lessons learned. This research not only pioneers the documentation of a secure, scalable, and reliable exam system at an institutional scale but also marks a seminal contribution to modernizing assessment strategies in computing education, with a particular focus on constructive alignment.

In contemporary educational concepts, the value of personalized learning is widely recognized, emphasizing the need to cater to the unique needs and abilities of each student. As a crucial component of evaluating educational effectiveness, examinations are gradually transitioning to more efficient and convenient online modes in line with technological advancements and the demands of the times. In light of this, this article proposes a personalized online examination system design based on reinforcement learning algorithms. This system relies on the MySQL database to provide data support, implements the required system functions interface using the Spring Boot framework, and utilizes the Vue framework for the front-end interface. The reinforcement learning algorithm enables the system to continuously optimize the adaptive learning path, personalized assessment, exam process optimization, question generation, difficulty adjustment, and automatic paper setting, aiming to promote academic progress.

Clinical education is a significant part of medical education, and paying attention to clinical evaluation is important. One of the challenges in the teaching-learning process is evaluating students’ performance. Learners, as the main stakeholders of the educational system, may have different experiences of evaluation quality. Awareness of these experiences is effective in improving the quality of clinical evaluation. Therefore, this study was conducted to “explore the experiences of nursing students for evaluation of professional competence by using the objective structured clinical examination (OSCE)”. This study was conducted with a qualitative descriptive research approach and conventional content analysis method in 2022–2024. The participants included 12 undergraduate nursing students at Maragheh University of Medical Sciences, who were selected by purposeful sampling, and their experiences were collected using semi-structured and in-depth interviews until reaching data saturation. The data analysis of the interviews led to the extraction of 268 primary codes, 7 subcategories, 2 categories, and 1 theme: “Credibility and stability”. The category “Exam’s accuracy in measuring competence " included 3 sub-categories: “Challenges in objective adaptation “, " Communication and organizational challenges for exam preparation " and " Inadequate simulation of stations and exam environment “, and the category " Exam power for repeatability” included 4 sub-categories: " Characteristics of the students “, " Lack of evaluators’ skills and mastery “, " Inefficiency of the evaluation tool " and " Disturbance in executive affairs”. OSCE can be used in self-evaluation, creating motivation and strengthening different dimensions of students’ learning, as well as discovering weaknesses and strengths for planning by managers and faculties. According to the results of this study, many factors such as management before and during the exam, characteristics of the evaluators, prevailing educational conditions in the faculty, and the method of clinical training are effective in achieving the “reliability and sustainability” required in the OSCE.

… [24] Study, they developed an online examination management system using a genetic algorithm and added two new features to the online examination automatic generation of …

This paper attempts to analyze the examinations administration system of the Office of the Controller of Examinations (OCoE) at Tribhuvan University. This body of the university is the face and is responsible for the evaluation of students. It also prepares and announces in advance the calendar of examinations, arranges for the printing of question papers, scheduling exams, declaration of results, distribution of transcripts and certificates, and conducts convocation to award academic success (Patil et al, 2021). The main objectives of this study are to explore the framework of re-engineering for examination management information system and identifying major challenges in the existing examination system. Purposeful sampling method was used to gather information about the university examination and results processing system. Qualitative response was collected by visiting 20 concerned authorities related to information technology (IT). Key Informant Interviews (KII) and group discussion were conducted with 20 from among the members of higher steering committees of IT, OCoE staffs and software developers to collect comparative opinions regarding experiences and practices of existing and newly initiated examination software. Different examination software as DBS and TUEMIS are functioning separately. So, there is a need of integrated system for effective, efficient, and reliable IT solution in OCoE. The study has explored four major problems at OCoE, TU, including IT, administrative, decentralization, and human resource management, and also has presented their solutions. As a solution to IT issues, supply development, commissioning, and re-engineering of examination management information system (EMIS) have recently been initiated. A total of 22 modules are designed along with other 10 supporting modules that will remain milestone to establish OCoE as a paperless and complete automated examination administration system. Decentralization of OCoE regional offices strengthens, empowers, and activates decentralized examination processing functions. This will foster participatory leadership, and inter-disciplinary collaborations, and promotes ownership as well (Fekadu et al., 2021). The OCoE is less administrative and more technical. So, its examination and results processing functions should be led by IT experts and professionals carried out by IT technicians.

… -based examination is becoming … examination supported by iFLYTEK AI examination system and explains its specific implementation. The exam paper library construction, examination …

Abstract Despite the benefits swift academic progress holds for many stakeholders, there is scarce literature on how academic progress may be improved by changes to assessment policies. Therefore, we investigated academic progress of first-year students after an alteration of characteristics of the assessment policies in three large course programmes: business administration (n = 2048) changed the stakes; medicine (n = 1630) changed the stakes and performance standard; psychology (n = 1076) changed the stakes, performance standard and resit standard. Results indicate that students’ academic progress was sensitive to the characteristics of the assessment policy in all three course programmes. The changes in progress could be explained by differences in performance, as well as by differences in selection for progress by the different policies. Implications are that assessment policies seem effective in shaping student progress, although one size does not fit all.

I use an unbalanced panel of over 11,000 academic records spanning from Spring 2017 to Spring 2020 to identify the difference in effects of the COVID-19 pandemic across lower- and higher-income students’ academic performance. Using difference-in-differences models and event study analyses with individual fixed effects, I find a differential effect by students’ pre-COVID-19 academic performance. Lower-income students in the bottom quartile of the Fall 2019 cumulative GPA distribution outperformed their higher-income peers with a 9% higher Spring 2020 GPA. This differential is fully explained by students’ use of the flexible grading policy with lower-income ones being 35% more likely to exercise the pass/fail option than their counterparts. While no such GPA advantage is observed among top-performing lower-income students, in the absence of the flexible grading policy these students would have seen their GPA decrease by 5% relative to their counterfactual pre-pandemic mean. I find suggestive evidence that this lower performance may be driven by lower-income top-performing students experiencing greater challenges with online learning. These students also reported a higher use of incompletes than their higher-income peers and being more concerned about maintaining (merit-based) financial aid.

… of academic tasks and has been designed as a practical tool which we encourage HEIs to tailor … on their own policy decisions regarding the use of GenAI tools in academic evaluations. …

… that no assessment model or policy eliminates the fundamental tensions between academic integrity, student learning, workload, and evolving technology. Instead, assessment in the …

Generative artificial intelligence provides both opportunities and challenges for higher education. Existing literature has not properly investigated how this technology would impact assessment in higher education. This scoping review took a forward-thinking approach to investigate how generative artificial intelligence transforms assessment in higher education. We used the PRISMA extension for scoping reviews to select articles for review and report the results. In the screening, we retrieved 969 articles and selected 32 empirical studies for analysis. Most of the articles were published in 2023. We used three levels—students, teachers, and institutions—to analyses the articles. Our results suggested that assessment should be transformed to cultivate students’ self-regulated learning skills, responsible learning, and integrity. To successfully transform assessment in higher education, the review suggested that (i) teacher professional development activities for assessment, AI, and digital literacy should be provided, (ii) teachers’ beliefs about human and AI assessment should be strengthened, and (iii) teachers should be innovative and holistic in their teaching to reflect the assessment transformation. Educational institutions are recommended to review and rethink their assessment policies, as well as provide more inter-disciplinary programs and teaching.

… new policy given the fast-evolving situation. In the absence of official institutional policy, several articles stated that individual academic … updated their academic integrity policy or honour …

The integration of Generative AI (GenAI) in Education presents immense potential for reshaping learning experiences and empowering students and educators. However, harnessing this potential requires collective action and responsible decision- making to ensure the effective and ethical use of AI technologies. This paper presents a series of recommendations and proposals aimed at effectively integrating GenAI in the higher education sector, catering to the perspectives of government, AI developers, students, educators, universities, schools, and researchers. By exploring diverse viewpoints about ChatGPT and future Google Gemini, this research aims to create a comprehensive recommendation guiding regulatory measures that address challenges, ethical considerations, and best practices of GenAI integration. Through a holistic approach, researchers believe that policymakers can foster a transformative and ethical environment, leveraging the full potential of generative AI while safeguarding students' well-being and academic integrity.

This study presents a corpus analysis of academic integrity policies from Higher Education Institutions (HEIs) worldwide, exploring how they address the issues posed by technological threats, such as Automated Paraphrasing Tools and generative-artificial intelligence tools, such as ChatGPT. The analysis of 142 policies conducted in November and December 2022, and May 2023 reveals a gap regarding the mention of AI and associated technologies in the available academic integrity policies. Despite the growing prevalence of these tools in the 6-month period since the release of ChatGPT, no HEIs had produced revised academic integrity policies. Content analysis of 53 guidance documents produced by HEIs suggests an overall positive focus of Gen AI tools, yet advises caution. This study suggests a modification to Bretag et al.’s (Int J Educ Integr 7, 2011) exemplary academic integrity model, introducing “Technological Explicitness” — emphasizing the need to include explicit guidelines about new technologies in academic integrity policies. These results underscore the urgent need for HEIs to revise their academic integrity policies, considering the evolving landscape of AI and its implications for academic integrity. This paper argues for a multifaceted approach to deal with the issues of integrating technology, education, policy reform, and assessment restructuring to navigate these challenges while upholding academic integrity.

Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. However, a comprehensive understanding of global institutional adoption policies remains absent, with most prior studies focusing on the Global North and lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal that universities are proactively addressing GAI integration by emphasising academic integrity, enhancing teaching and learning practices, and promoting equity. Key policy measures include the development of guidelines for ethical GAI use, the design of authentic assessments to mitigate misuse, and the provision of training programs for faculty and students to foster GAI literacy. Despite these efforts, gaps remain in comprehensive policy frameworks, particularly in addressing data privacy concerns and ensuring equitable access to GAI tools. The study underscores the importance of clear communication channels, stakeholder collaboration, and ongoing evaluation to support effective GAI adoption. These insights provide actionable insights for policymakers to craft inclusive, transparent, and adaptive strategies for integrating GAI into higher education.

Academic integrity policy that is inaccessible, ambiguous or confusing is likely to result in inconsistent policy enactment. Additionally, policy analysis and development are often undertaken as top down processes requiring passive acceptance by users of policy that has been developed outside the context in which it is enacted. Both these factors can result in poor policy uptake, particularly where policy users are overworked, intellectually critical and capable, not prone to passive acceptance and hold valuable grass roots intelligence about policy enactment. The case study presented in this paper describes the actions of a community of practice (CoP) at a regional Australian university to deconstruct and translate ambiguous academic integrity policy into a suite of accessible academic integrity resources that were intelligible to staff and students, and which assisted academic staff to consistently enact policy. The paper narrates the formation of the CoP, the tangible and intangible value it created, the social learning practices enacted by its members, its grassroots policy work and the material resources produced from that work. An evaluation of the CoP was conducted using a value creation framework to explore its immediate value, potential value, applied value, realised value, and reframing value. These values were considered at each stages of the CoP’s lifespan. The evaluation was a useful process that demonstrated the wide-ranging value created by the CoP. Six insights were drawn from the evaluation which promote understanding of the value created for a university by a CoP, particularly in contributing to academic integrity culture over a sustained period of time. This paper contributes to a research gap on specific examples of discretion within rule-based systems. It illustrates how academics and members of the CoP used their discretion to interpret and enact academic integrity policy within a higher education setting. Drawing from the evaluation of the CoP we argue for greater understanding of the grass-roots contribution of academic and professional staff to academic integrity policy translation and enactment.

During the recent COVID-19 outbreak, educational institutions have transitioned to online teaching for all students for most of the programs. Due to lack of in-person interactions and monitoring, assessments in online courses may be more susceptible to contract cheating, collusion, fabrication and other types of academic misconduct than the assessments in face-to-face courses. This situation has raised several research questions that need immediate attention, such as what are the best possible options for online assessments and how to administer online assessments so that academic integrity could be preserved. The authors have conducted a scoping study and carried out an extensive literature review on i) different types of assessments that are suitable for online courses, ii) strategies for ensuring academic integrity, and iii) methods, tools and technologies available for preventing academic misconduct in online assessments. It is evident from the literature review that there are a range of options available for designing assessment tasks to detect and prevent violations of academic integrity. However, no single method or design is enough to eliminate all sorts of academic integrity violations. After thorough research and analysis of existing literature, the authors have provided a comprehensive set of recommendations that could be adopted for ensuring academic integrity in online assessments.

This study examines the impact of AI, particularly ChatGPT, on academic integrity and assessment practices in higher education. As AI integration grows, concerns about its potential to undermine academic rigour and increase inequalities have surfaced. Through interviews with students and a lecturer, the research explores the benefits and challenges of using ChatGPT in academic work. The innovative approach of having students use ChatGPT to write assignments highlights both efficiency gains and the need for responsible use. Findings reveal the importance of using AI-generated content as a supplement rather than a replacement for traditional learning, with concerns about its potential misuse. The study advocates for updated integrity policies and clear guidelines to ensure AI enhances, rather than compromises, education. Emphasising ethical AI use and process-oriented assessments, the study offers strategies to promote fairness, integrity, and critical thinking in the digital age.

The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “full AI,” enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology’s potential to enhance learning experiences.   Implications for practice or policy: Higher education institutions should adopt flexible frameworks like the AIAS to guide ethical integration of GenAI into assessment practices. Educators should design assessments that leverage GenAI capabilities, while supporting critical thinking and human input. Institutional policies related to GenAI should be developed in consultation with stakeholders and regularly updated to keep pace with technological advancements. Policymakers should prioritise research funding into the impacts of GenAI on higher education to inform evidence-based practices.

This paper explores the perceptions of academic staff and students to student cheating behaviours in online exams and other online assessment formats. The research took place at three Australian universities in July and August 2020 during the emergency transition to online learning and assessment in response to the COVID-19 pandemic. The study sought to inform decision making about the future of online exams at the participating universities. Quantitative and qualitative data were collected using online surveys. The findings of the study led to seven key observations, most notably the need to redefine the characteristics of academic misconduct to account for changes wrought to examinations in a digital world. The study concludes with lessons learned in relation to enhancing academic integrity in digital examinations and assessments.

Artificial intelligence (AI) is a growing force of change in higher education, providing assistance to students, teachers, and administrators in teaching, learning, and administration. As AI technologies advance rapidly, they present a combination of significant opportunities and complex challenges. In this study, we examine the role of AI in higher education, highlighting both its positive and negative impacts, as well as current policy gaps and issues arising from its deployment. The literature on the topic was reviewed to determine how AI decisively impacts teaching and learning, the role of AI in assessments and academic integrity, as well as ethics, psychological considerations, and institutional governance from an ethical and psychological perspective. Technologies used in new areas, such as adaptive AI-based systems, intelligent tutoring platforms, and generative AI tools, create new opportunities for accessibility and personalization of learning experiences, thereby increasing student motivation. Skills development, such as writing and linguistic skills, can enhance AI capabilities. Additionally, it facilitates assessment methods by improving processes, providing immediate feedback, and adjusting evaluations accordingly. However, when students rely heavily on AI for their assessment tasks, it raises questions about academic integrity, cognitive offloading, and the limits of skills acquisition. While progress has been made, numerous open questions remain regarding the detection of AI-generated content, including the incorporation of fake narratives into generative AI tools, biases, privacy concerns, and the impact of the technology on our environment. Policies on AI governance have not yet matured in many higher education institutions; an integrated approach will be required from a broader perspective, including training faculty and utilizing institutional resources to benefit from AI while mitigating associated risks. To synthesize recent research on artificial intelligence in higher education, this study employs a narrative review approach. Unlike existing reviews that focus primarily on the integrated analysis of AI’s pedagogical, assessment, ethical, psychological, and institutional governance implications, this multidimensional perspective provides a consolidated framework to support responsible AI use across higher education systems.

… of oral assessment in undergraduate higher education were included in this review. Other types of assessment (… level, or oral assessments in postgraduate higher education, vocational …

Artificial Intelligence (AI) developments challenge higher education institutions’ teaching, learning, assessment, and research practices. To contribute timely and evidence-based recommendations for upholding academic integrity, we conducted a rapid scoping review focusing on what is known about academic integrity and AI in higher education. We followed the Updated Reviewer Manual for Scoping Reviews from the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic reviews Meta-Analysis for Scoping Reviews (PRISMA-ScR) reporting standards. Five databases were searched, and the eligibility criteria included higher education stakeholders of any age and gender engaged with AI in the context of academic integrity from 2007 through November 2022 and available in English. The search retrieved 2223 records, of which 14 publications with mixed methods, qualitative, quantitative, randomized controlled trials, and text and opinion studies met the inclusion criteria. The results showed bounded and unbounded ethical implications of AI. Perspectives included: AI for cheating; AI as legitimate support; an equity, diversity, and inclusion lens into AI; and emerging recommendations to tackle AI implications in higher education. The evidence from the sources provides guidance that can inform educational stakeholders in decision-making processes for AI integration, in the analysis of misconduct cases involving AI, and in the exploration of AI as legitimate assistance. Likewise, this rapid scoping review signals key questions for future research, which we explore in our discussion.

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents’ views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools 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 implications This 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.

ABSTRACT Unethical behaviour has become an increasingly controversial issue in Higher Education institutes. There have been debates about the reasons for the increase in unethical behaviour. But many of those debates contain problems. A key problem has been the lack of empirical results about faculty members’ perceptions of their role in the phenomenon, how cultural contexts influence the perception of university teachers about their role in the academic integrity field and whether conflicts exist between what they believe their role should be and the types of roles they actually play. The aim of the study is to explore this aspect using a qualitative research design to facilitate comprehensive access to faculty members’ beliefs and practices. The findings suggest that professors believe the teaching role extends beyond encouraging the learning of the subject matter being studied and includes offering education and information to students about the importance of avoiding academic misconduct such as cheating and plagiarism. Implications for university across different countries are also discussed.

… and academic integrity in assessment. Their use is worrying for education in general, … thinking, information literacy skills, resilience, and integrity. Although these tools may take the pain …

Abstract Contract cheating is an increasingly challenging problem facing the higher education sector. This study assesses the current state of contract cheating research from various methodological and empirical perspectives. Through a systematic literature review of 51 peer-reviewed articles on contract cheating in higher education, we identify clusters of keywords, research trends and major research themes. Our analysis shows that theory-based research and studies using methodologies such as case study are lacking. We consolidate the research findings, presenting them in a conceptual framework to address contract cheating in higher education. Promising areas for future research on contract cheating in higher education are identified.