A Critical Analysis of Students’ Cheating in Online Assessment in Higher Education: Post-COVID-19 Issues and Challenges Related to Conversational Artificial Intelligence

Bubaš, Goran; Čižmešija, Aleksandra

doi:10.23919/mipro57284.2023.10159826

Cited by 3 publications

(1 citation statement)

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“…While numerous studies highlighted cheating in online assessments during the pandemic, Bubaš and Čižmešija's (2023) Several studies provided empirical evidence on academic dishonesty in online assessments using AI (Cotton et al, 2023;Eaton, 2022;Birks & Clare, 2023;Oravec, 2023;Bubaš & Čižmešija, 2023;Sweeney, 2023) that take any form of plagiarism. For this, every educational institution must look into educational policies on the use of AI, institutional assessments and institutional teaching pedagogy.…”

Section: Ai and Academic Dishonesty In Online College Assessmentsmentioning

confidence: 99%

Beyond Exams: Investigating AI Tool Impact on Student Attitudes, Ethical Awareness, and Academic Dishonesty in Online College Assessments

Hua

2023

IJEMDS

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Academic dishonesty in higher education is a growing concern, exacerbated by the increasing use of AI tools in online assessments. This study investigates the relationship between AI tool dependence, ethical awareness, student attitudes, and academic dishonesty among college students. It also explores the moderating effects of demographic factors. Survey data from college students were analyzed using a Generalized Linear Model (GLM) framework for a thorough examination of the complex interplay between the variables while considering the moderating influence of age, gender, type of institution, and technological proficiency. The study reveals that AI tool dependence is prevalent among college students. While students generally hold positive attitudes toward academic integrity, there is variability in the intensity and nature of these attitudes. Moreover, ethical awareness appears limited, highlighting a potential gap between ethical beliefs and behavior. Surprisingly, there is a consistent pattern of positive attitudes toward academic dishonesty. However, these findings are not explored in-depth in this study. Importantly, neither ethical awareness nor student attitudes significantly mediate the relationship between AI tool dependence and academic dishonesty. Demographic factors do not appear to significantly moderate these relationships. In light of these findings, institutions are encouraged to explore the implementation of the AI Dependence Inclusive Course for Transparency Program (AIDICT). This specialized initiative, shaped by the study’s insights, enhances ethical education and raises awareness of the ethical implications associated with AI tool usage.

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Section: Ai and Academic Dishonesty In Online College Assessmentsmentioning

confidence: 99%

Beyond Exams: Investigating AI Tool Impact on Student Attitudes, Ethical Awareness, and Academic Dishonesty in Online College Assessments

Hua

2023

IJEMDS

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Navigating the New Frontier: Recommendations to Address the Crisis and Potential of AI in the Classroom

Slade,

Byers,

Becker-Blease

et al. 2024

Teaching of Psychology

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Introduction The integration of artificial intelligence (AI) into higher education ushers in a new era of teaching and learning possibilities for both educators and students. Statement of the Problem These technologies not only offer unparalleled opportunities for personalized learning and instructional support, but also introduce challenges related to academic integrity. Literature Review The literature highlights AI's versatile applications in education, from facilitating research to acting as a tutor. Teaching Implications Educators can create AI-informed assignments that encourage active learning, critical thinking, and creativity. AI tools can be integrated as supports rather than substitutes for learning and foster an ethical framework within which students can explore AI's capabilities. Conclusion This review of AI's impact on education and the discussion of teaching strategies underscore the need for a nuanced approach to integrating AI in the classroom. By acknowledging both the challenges and opportunities presented by AI, educators can enhance teaching and learning while upholding academic standards.

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Artificial Intelligence (AI) and Cheating

Akkaş,

Tosun,

Gökçearslan

2024

Advances in Educational Technologies and Instructional Design

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Generative AI (GenAI) systems pose new challenges in academic dishonesty. Students may be tempted to use GenAI systems to cheat and submit content in assignments and projects that they did not create themselves. This points to the need for schools to focus on strong deterrent measures as well as informative and enhancing practices. Academic dishonesty is not limited to students. The use of GenAI in academia raises two extreme ethical problems. These problems relate to the ethical problems of the end user and the ethical problems in the development of this technology. The ethical use of GenAI technologies should be achieved in a way that respects human rights and takes into account user concerns. This new technology requires a rethinking of teaching methods, as well as assessment and evaluation. It is not recommended that policymakers, students, and faculty work collectively and take responsibility for adopting ethical values in the process of integrating GenAI into academia.

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A Critical Analysis of Students’ Cheating in Online Assessment in Higher Education: Post-COVID-19 Issues and Challenges Related to Conversational Artificial Intelligence

Cited by 3 publications

References 25 publications

Beyond Exams: Investigating AI Tool Impact on Student Attitudes, Ethical Awareness, and Academic Dishonesty in Online College Assessments

Beyond Exams: Investigating AI Tool Impact on Student Attitudes, Ethical Awareness, and Academic Dishonesty in Online College Assessments

Navigating the New Frontier: Recommendations to Address the Crisis and Potential of AI in the Classroom

Artificial Intelligence (AI) and Cheating

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