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Academic integrity and copyright literacy policy and instruction in K-12 schools: a global study from the perspective of school library professionals K-12 年级学校的学术诚信和版权扫盲政策与教学:从学校图书馆专业人员的角度开展的全球研究
IF 4.6 Q1 Social Sciences Pub Date : 2024-03-21 DOI: 10.1007/s40979-024-00150-x
Zakir Hossain, Özgür Çelik, Corinne Hertel
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引用次数: 0
Algorithmically-driven writing and academic integrity: exploring educators' practices, perceptions, and policies in AI era 算法驱动的写作与学术诚信:探索人工智能时代教育工作者的实践、认知和政策
IF 4.6 Q1 Social Sciences Pub Date : 2024-03-20 DOI: 10.1007/s40979-024-00153-8
Leah Gustilo, Ethel Ong, Minie Rose Lapinid
Despite global interest in the interface of Algorithmically-driven writing tools (ADWTs) and academic integrity, empirical data considering educators' perspectives on the challenges, benefits, and policies of ADWTs use remain scarce. This study responds to calls for empirical investigation concerning the affordances and encumbrances of ADWTs, and their implications for academic integrity. Using a cross-sectional survey research design, we recruited through snowball sampling 100 graduate students and faculty members representing ten disciplines. Participants completed an online survey on perceptions, practices, and policies in the utilization of ADWTs in education. The Technology Acceptance Model (TAM) helped us understand the factors influencing the acceptance and use of ADWTs. The study found that teacher respondents highly value the diverse ways ADWTs can support their educational goals (perceived usefulness). However, they must overcome their barrier threshold such as limited access to these tools (perception of external control), a perceived lack of knowledge on their use (computer self-efficacy), and concerns about ADWTs' impact on academic integrity, creativity, and more (output quality). AI technologies are making headway in more educational institutions because of their proven and potential benefits for teaching, learning, assessment, and research. However, AI in education, particularly ADWTs, demands critical awareness of ethical protocols and entails collaboration and empowerment of all stakeholders by introducing innovations that showcase human intelligence over AI or partnership with AI.
尽管全球都在关注算法驱动写作工具(ADWTs)与学术诚信之间的关系,但考虑到教育工作者对使用ADWTs的挑战、益处和政策的看法的实证数据仍然很少。本研究响应了关于对 ADWT 的承受能力和负担及其对学术诚信的影响进行实证调查的呼吁。我们采用横断面调查研究设计,通过滚雪球式抽样,招募了代表十个学科的 100 名研究生和教师。参与者完成了一项在线调查,内容涉及在教育中使用自动化数据采集工具的看法、实践和政策。技术接受模型(TAM)帮助我们了解了影响接受和使用 ADWT 的因素。研究发现,受访教师高度评价自动化数据和通信技术能以多种方式支持他们的教育目标(感知有用性)。然而,他们必须克服自己的障碍门槛,如使用这些工具的机会有限(外部控制感)、缺乏使用这些工具的知识(计算机自我效能感),以及担心 ADWT 对学术诚信、创造力等的影响(输出质量)。由于人工智能技术在教学、学习、评估和研究方面已被证实并具有潜在优势,因此越来越多的教育机构开始采用人工智能技术。然而,人工智能在教育领域的应用,特别是自动化数据处理技术,需要对伦理协议有严格的认识,并需要所有利益相关者的合作与赋权,引入创新技术,展示人类智能优于人工智能或与人工智能的伙伴关系。
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引用次数: 0
Student perspectives on the use of generative artificial intelligence technologies in higher education 学生对在高等教育中使用生成式人工智能技术的看法
IF 4.6 Q1 Social Sciences Pub Date : 2024-02-08 DOI: 10.1007/s40979-024-00149-4
Heather Johnston, Rebecca F. Wells, Elizabeth M. Shanks, Timothy Boey, Bryony N. Parsons

The aim of this project was to understand student perspectives on generative artificial intelligence (GAI) technologies such as Chat generative Pre-Trained Transformer (ChatGPT), in order to inform changes to the University of Liverpool Academic Integrity code of practice. The survey for this study was created by a library student team and vetted through focus groups. A total of 2555 students participated in the survey. Results showed that only 7% of students who responded had not heard of any GAI technologies, whilst over half had used or considered using these for academic purposes. The majority of students (54.1%) were supportive or somewhat supportive of using tools such as Grammarly, but 70.4% were unsupportive or somewhat unsupportive towards students using tools such as ChatGPT to write their whole essay. Students who had higher levels of confidence in their academic writing were less likely to use or consider using them for academic purposes, and were also less likely to be supportive of other students using them. Most students (41.1%) also thought there should be a university wide policy on when these technologies are or are not appropriate to use. The results of this research suggest that students require clear policies on the use of GAI and that these technologies should not be banned from university, but consideration must be made to ensure different groups of students have equal access to the technologies.

本项目旨在了解学生对聊天生成预训练转换器(ChatGPT)等生成式人工智能(GAI)技术的看法,以便为利物浦大学学术诚信守则的修改提供参考。本研究的调查问卷由图书馆学生团队制作,并通过焦点小组审核。共有 2555 名学生参与了调查。结果显示,只有 7% 的受访学生没有听说过任何 GAI 技术,而超过一半的学生已经使用或考虑使用这些技术进行学术研究。大多数学生(54.1%)支持或有些支持使用 Grammarly 等工具,但 70.4% 的学生不支持或有些不支持学生使用 ChatGPT 等工具撰写整篇文章。对自己的学术写作有较高信心的学生不太可能使用或考虑将其用于学术目的,也不太可能支持其他学生使用这些工具。大多数学生(41.1%)还认为,应该制定一项大学范围内的政策,规定何时适合或不适合使用这些技术。研究结果表明,学生需要明确的 GAI 使用政策,大学不应该禁止使用这些技术,但必须考虑确保不同群体的学生有平等的机会使用这些技术。
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引用次数: 0
University ‘Pay-for-grades’: the bait and switch search engine optimization strategies of contract cheating websites in the United States 大学 "付费评级":美国合同作弊网站的诱饵和转换搜索引擎优化策略
IF 4.6 Q1 Social Sciences Pub Date : 2024-01-25 DOI: 10.1007/s40979-023-00148-x
Timothy M. Daly, James C. Ryan

This paper presents the first systematic investigation into the search engine optimization practices of major contract cheating websites in the United States. From a business perspective, visibility in organic search engine results is considered one of the top client recruitment tools. The current understanding of student recruitment strategies by these companies remains largely unexplored in both academic literature and popular press. Replicating the business research practices used in the search engine optimization industry, comprehensive search engine ranking and traffic data was obtained for the 38 largest contract cheating websites in the US. The overall objective was to illuminate the strategies that these companies take to get their services at the top of the search results of as many students as possible – not just the relatively small proportion of students actively cheating. The results show that these companies dominate the search results for not just students searching to cheat, but also for naïve search efforts, when students are simply doing genuine research or classwork. These nefarious companies use highly sophisticated search engine manipulation strategies to bait naïve student searchers onto their sites, thus enabling the potential to switch them to cheaters. Higher education institutions, armed with the specific details provided in this study, can use the strategies outlined in the discussion to directly and negatively impact on the success of these contract cheating services.

本文首次对美国主要合同作弊网站的搜索引擎优化实践进行了系统调查。从商业角度看,在有机搜索引擎结果中的能见度被认为是最重要的客户招募工具之一。目前,学术文献和大众媒体对这些公司的学生招聘策略的了解基本上还是空白。仿照搜索引擎优化行业的商业研究方法,我们获得了美国 38 家最大的合同作弊网站的综合搜索引擎排名和流量数据。总体目标是阐明这些公司采取了哪些策略,使其服务在尽可能多的学生--而不仅仅是相对较少的主动作弊学生--的搜索结果中名列前茅。研究结果表明,这些公司不仅在搜索作弊的学生的搜索结果中占据主导地位,而且还在天真的搜索结果中占据主导地位,因为学生只是在做真正的研究或课堂作业。这些邪恶的公司利用高度复杂的搜索引擎操纵策略,诱使天真的学生搜索者进入他们的网站,从而有可能将他们转为作弊者。高等教育机构可以根据本研究提供的具体细节,利用讨论中概述的策略直接对这些合同作弊服务的成功产生负面影响。
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引用次数: 0
Testing of detection tools for AI-generated text 测试人工智能生成文本的检测工具
IF 4.6 Q1 Social Sciences Pub Date : 2023-12-25 DOI: 10.1007/s40979-023-00146-z
Debora Weber-Wulff, Alla Anohina-Naumeca, Sonja Bjelobaba, Tomáš Foltýnek, Jean Guerrero-Dib, Olumide Popoola, Petr Šigut, Lorna Waddington
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.
生成式预训练转换器大型语言模型的最新进展强调了在学术环境中不公平地使用人工智能(AI)生成内容的潜在风险,并加大了寻找检测此类内容的解决方案的力度。本文研究了人工智能生成文本检测工具的一般功能,并根据准确性和错误类型分析对其进行了评估。具体来说,本研究试图回答以下研究问题:现有检测工具是否能可靠地区分人类撰写的文本和 ChatGPT 生成的文本,机器翻译和内容混淆技术是否会影响人工智能生成文本的检测。研究涵盖了 12 种公开可用的工具和两种商业系统(Turnitin 和 PlagiarismCheck),这些工具和系统在学术界被广泛使用。研究人员得出结论,现有的检测工具既不准确也不可靠,主要偏向于将输出结果归类为人类撰写的文本,而不是检测人工智能生成的文本。此外,内容混淆技术大大降低了工具的性能。这项研究做出了几项重大贡献。首先,它总结了该领域最新的类似科学和非科学工作。其次,它基于严谨的研究方法、原始文档集和广泛的工具覆盖范围,展示了迄今为止最全面的测试结果之一。第三,它讨论了在学术环境中对人工智能生成的文本使用检测工具的意义和缺点。
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引用次数: 0
A model for preventing academic misconduct: evidence from a large-scale intervention 预防学术不端行为的模式:来自大规模干预的证据
IF 4.6 Q1 Social Sciences Pub Date : 2023-12-15 DOI: 10.1007/s40979-023-00147-y
Lyle Benson, Rickard Enstroem
It is well known that students intentionally and unintentionally commit academic misconduct, but how can universities prevent academic misconduct and foster a culture of academic integrity? Based on a literature synthesis, an actionable Model for Preventing Academic Misconduct is presented. The model’s basic premise is that students’ voluntary participation in individual courses or academic integrity modules will have far less impact on preventing academic misconduct than required faculty or university-wide programming in core courses. In validating the model, the steps taken by the School of Business at a Canadian university to prevent academic misconduct are examined. Two online tutorials were created and implemented as required modules in the School of Business introductory core courses. Actual academic misconduct incidents recorded by the University from 2016 to 2021, a three-year pre-intervention period and a two-year post-intervention period partly covering the COVID-19 outbreak, are used to gauge the model’s effectiveness in preventing academic misconduct. The findings are discussed through a Social Learning Theory lens: the high-level implementation gives rise to a culture of academic integrity propelled by the establishment of common knowledge.
众所周知,学生会有意或无意地实施学术不端行为,但大学如何才能预防学术不端行为并培养学术诚信文化呢?本文在文献综述的基础上,提出了一个可操作的 "预防学术不端行为模型"。该模型的基本前提是,学生自愿参与个别课程或学术诚信模块,对预防学术不端行为的影响远远小于要求教师或全校在核心课程中开展的活动。在验证该模式时,我们考察了加拿大一所大学商学院为防止学术不端行为而采取的措施。商学院创建了两个在线教程,并将其作为入门核心课程的必修模块。该大学从 2016 年到 2021 年记录的实际学术不端行为事件(干预前的三年和干预后的两年,部分涵盖了 COVID-19 的爆发)被用来衡量该模型在预防学术不端行为方面的有效性。研究结果通过社会学习理论的视角进行了讨论:高层次的实施促进了学术诚信文化的形成,而学术诚信文化的形成则是由常识的建立所推动的。
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引用次数: 0
Italian academic system disregards scientific merit in faculty hiring processes 意大利的学术体系在聘用教师的过程中忽视了科学价值
IF 4.6 Q1 Social Sciences Pub Date : 2023-12-01 DOI: 10.1007/s40979-023-00145-0
Pasquale Gallina, Francesco Lolli, Oreste Gallo, Berardino Porfirio
Professorships in Italy are assigned following public competitions. However, favouritism affects faculty hiring. Researchers lacking clientelistic support remain excluded from academia and are obliged to seek employment abroad or at non-university institutions, or to abandon their career. Do non-recruited researchers have better or worse scientific capacity than those who have attained professorships in Italy? Files regarding the competitions in bibliometric disciplines won by 186 professors in Florence were analysed. An equal number of professors recruited at other Italian universities and scientists who never attained professorship in Italy were randomly drawn from the pool of individuals having national scientific qualification (the prerequisite for professorship) in the same disciplines as each Florentine professor. H-indexes of the year of qualification (T1), of the Florence call (T2), and in July 2021 (T3) were obtained from Scopus. Non-recruited individuals were more likely (Chi-square test) to show a higher H-index than both Florentine (T1 p = 0.0005, T2 p = 0.0015, T3 p = 0.0095) and non-Florentine professors (T1 p = 0.0078, T2 p = 0.0245, T3 p = 0.0500). Fifty-four non-recruited scientists serve in foreign universities, 100 at national/international research centres. The remaining scientists (25 who continue producing despite precarious employment, and seven who have stopped publishing) were as likely as Florentine (T3 p = 0.69) and non-Florentine (T3 p = 0.14) professors to show a higher H-index. Italian faculty hiring disregards merit. A more challenging qualification would limit the access of researchers with lower scientific capacity, and favour those with greater proficiency. As it stands, competition is useless. Once professors obtain permanent employment, they seem less motivated to publish.
意大利的教授职位是在公开竞争之后分配的。然而,偏袒会影响教师招聘。缺乏裙带关系支持的研究人员仍然被排除在学术界之外,被迫在国外或非大学机构寻找工作,或者放弃他们的职业生涯。与那些在意大利获得教授职位的研究人员相比,未被招募的研究人员的科学能力是更好还是更差?对佛罗伦萨186名教授在文献计量学科竞赛中获胜的资料进行了分析。从与每一位佛罗伦萨教授具有相同学科的国家科学资格(教授资格的先决条件)的个人中随机抽取从意大利其他大学征聘的相同数量的教授和从未在意大利获得教授职位的科学家。资格年(T1)、Florence call (T2)和2021年7月(T3)的h指数来源于Scopus。非招募个体的h指数更有可能(卡方检验)高于佛罗伦萨教授(T1 p = 0.0005, T2 p = 0.0015, T3 p = 0.0095)和非佛罗伦萨教授(T1 p = 0.0078, T2 p = 0.0245, T3 p = 0.0500)。54名未受聘的科学家在外国大学任职,100名在国家/国际研究中心任职。剩下的科学家(25名在不稳定的工作中继续进行研究的科学家,7名停止发表论文的科学家)与佛罗伦萨(T3 p = 0.69)和非佛罗伦萨(T3 p = 0.14)教授一样,显示出更高的h指数。意大利的教师招聘不看重成绩。一个更具挑战性的资格将限制科学能力较低的科学家获得,而有利于那些更熟练的科学家。就目前而言,竞争是无用的。一旦教授获得了长期工作,他们似乎就不那么有动力发表论文了。
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引用次数: 0
“Out of my control”: science undergraduates report mental health concerns and inconsistent conditions when using remote proctoring software “我无法控制”:理科生在使用远程监控软件时报告了心理健康问题和不一致的情况
Q1 Social Sciences Pub Date : 2023-11-15 DOI: 10.1007/s40979-023-00141-4
Annika Pokorny, Cissy J. Ballen, Abby Grace Drake, Emily P. Driessen, Sheritta Fagbodun, Brian Gibbens, Jeremiah A. Henning, Sophie J. McCoy, Seth K. Thompson, Charles G. Willis, A. Kelly Lane
Abstract Efforts to discourage academic misconduct in online learning environments frequently include the use of remote proctoring services. While these services are relatively commonplace in undergraduate science courses, there are open questions about students’ remote assessment environments and their concerns related to remote proctoring services. Using a survey distributed to 11 undergraduate science courses engaging in remote instruction at three American, public, research-focused institutions during the spring of 2021, we found that the majority of undergraduate students reported testing in suboptimal environments. Students’ concerns about remote proctoring services were closely tied to technological difficulties, fear of being wrongfully accused of cheating, and negative impacts on mental health. Our results suggest that remote proctoring services can create and perpetuate inequitable assessment environments for students, and additional research is required to understand the efficacy of their intended purpose to prevent cheating. We also advocate for continued conversations about the broader social and institutional conditions that can pressure students into cheating. While changes to academic culture are difficult, these conversations are necessary for higher education to remain relevant in an increasingly technological world.
在在线学习环境中阻止学术不端行为的努力通常包括使用远程监考服务。虽然这些服务在本科理科课程中相对普遍,但关于学生的远程评估环境和他们对远程监考服务的担忧仍存在悬而未决的问题。通过对2021年春季在三所美国公立研究型机构进行远程教学的11门本科科学课程进行的调查,我们发现大多数本科生报告在次优环境下进行测试。学生对远程监考服务的担忧与技术困难、担心被错误地指控作弊以及对心理健康的负面影响密切相关。我们的研究结果表明,远程监考服务可以为学生创造和维持不公平的评估环境,并且需要进一步的研究来了解其防止作弊的预期目的的有效性。我们还提倡继续讨论可能迫使学生作弊的更广泛的社会和制度条件。虽然改变学术文化是困难的,但这些对话对于高等教育在一个日益科技化的世界中保持相关性是必要的。
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引用次数: 0
The temptation to cheat in online exams: moving beyond the binary discourse of cheating and not cheating 在线考试作弊的诱惑:超越作弊和不作弊的二元话语
Q1 Social Sciences Pub Date : 2023-11-01 DOI: 10.1007/s40979-023-00143-2
Michael Henderson, Jennifer Chung, Rebecca Awdry, Cliff Ashford, Mike Bryant, Matthew Mundy, Kris Ryan
Abstract Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on those who do not cheat, but who are tempted to do so. We conceptualise this group as being at risk of future cheating behaviour and potentially more receptive of targeted strategies to support their integrity decisions. In this paper we report on a large-scale survey of university students ( n = 7,511) who had just completed one or more end of semester online exams. In doing so we explore students’ reported temptation to cheat. Analysis surrounding this “at risk” group reveals students who were Tempted ( n = 1379) had significant differences from those who Cheated ( n = 216) as well as those who were Not tempted ( n = 5916). We focus on four research questions exploring whether there are specific online exam conditions, security settings, student attitudes or perceptions which are more strongly associated with the temptation to cheat. The paper offers insights to help institutions to minimise factors that might lead to breaches of assessment integrity, by focusing on the temptation to cheat during assessment.
摘要围绕评估诚信的讨论往往集中在考试条件、作弊者与未作弊者的动机和价值观上。我们认为,话语需要摆脱欺骗的二元表示。相反,我们建议,通过关注那些没有欺骗,但却被诱惑要这样做的人,对话可能会更有成效,更有影响力。我们将这一群体定义为未来有作弊行为的风险,并且可能更容易接受有针对性的策略来支持他们的诚信决策。在本文中,我们报告了一项针对刚刚完成一个或多个学期末在线考试的大学生(n = 7511)的大规模调查。在这样做的过程中,我们探讨了学生报告的作弊诱惑。围绕这一“危险”群体的分析显示,被诱惑的学生(n = 1379)与被欺骗的学生(n = 216)以及没有被诱惑的学生(n = 5916)有着显著的差异。我们关注四个研究问题,探讨是否有特定的在线考试条件、安全设置、学生的态度或看法与作弊的诱惑更密切相关。本文提供了一些见解,通过关注评估过程中作弊的诱惑,帮助机构最大限度地减少可能导致违反评估完整性的因素。
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引用次数: 1
Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks 将人工智能与现有的预防框架联系起来,促进了学术不端行为
Q1 Social Sciences Pub Date : 2023-10-15 DOI: 10.1007/s40979-023-00142-3
Daniel Birks, Joseph Clare
Abstract This paper connects the problem of artificial intelligence (AI)-facilitated academic misconduct with crime-prevention based recommendations about the prevention of academic misconduct in more traditional forms. Given that academic misconduct is not a new phenomenon, there are lessons to learn from established information relating to misconduct perpetration and frameworks for prevention. The relevance of existing crime prevention frameworks for addressing AI-facilitated academic misconduct are discussed and the paper concludes by outlining some ideas for future research relating to preventing AI-facilitated misconduct and monitoring student attitudes and behaviours with respect to this type of behaviour.
摘要本文将人工智能(AI)促进的学术不端行为问题与基于犯罪预防的关于预防传统形式学术不端行为的建议联系起来。鉴于学术不端行为不是一种新现象,我们可以从有关不端行为的现有信息和预防框架中吸取教训。本文讨论了解决人工智能促进的学术不端行为的现有犯罪预防框架的相关性,并概述了与预防人工智能促进的不端行为和监测学生对这类行为的态度和行为有关的未来研究的一些想法。
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引用次数: 1
期刊
International Journal for Educational Integrity
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