Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks

Birks, Daniel; Clare, Joseph

doi:10.1007/s40979-023-00142-3

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…Despite the benefits of AI in higher education, it simultaneously raises valid concerns about the academic integrity [23, 24]. The ease with which AI can perform complex tasks might inadvertently encourage academic dishonesty, potentially undermining the educational ethics [23, 25]. Furthermore, the reliance on AI for academic tasks could trigger a decline in critical thinking and personal development skills among students, both of which are essential outcomes to enable the graduates in achieving economic, technological, and social advancements [26, 27].…”

Section: Introductionmentioning

confidence: 99%

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Sallam,

Al-Salahat,

Eid

et al. 2024

Preprint

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The advances in large language models (LLMs) are evolving rapidly. Artificial intelligence (AI) chatbots based on LLMs excel in language understanding and generation, with potential utility to transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. Therefore, this study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master’s (MSc) level in Medical Laboratory Sciences. The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessment of 20 MSc students. The revised Bloom’s taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for assessment of the quality of AI-generated content, with Cohen’s κ for inter-rater agreement. Compared to the mean students’ score which was 40/60 (66.8%), GPT-4 scored 54/60 (90.0%), followed by Bing (46/60, 76.7%), GPT-3.5 (44/60, 73.3%), and Bard (40/60, 66.7%). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5, GPT-4, and Bard. The CLEAR scores indicated that ChatGPT-4 performance was “Excellent” compared to “Above average” performance of ChatGPT-3.5, Bing, and Bard. The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above-average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risks of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated.

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Section: Introductionmentioning

confidence: 99%

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Sallam,

Al-Salahat,

Eid

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Despite the bene ts of AI in higher education, it simultaneously raises valid concerns about the academic integrity [23,24]. The ease with which AI can perform complex tasks might inadvertently encourage academic dishonesty, potentially undermining the educational ethics [23,25]. Furthermore, the reliance on AI for academic tasks could trigger a decline in critical thinking and personal development skills among students, both of which are essential outcomes to enable the graduates in achieving economic, technological, and social advancements [26,27].…”

Section: Introductionmentioning

confidence: 99%

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Sallam,

Al-Salahat,

Eid

et al. 2024

Preprint

View full text Add to dashboard Cite

Background The advances in large language models (LLMs) are evolving rapidly. Artificial intelligence (AI) chatbots based on LLMs excel in language understanding and generation, with potential utility to transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. Therefore, this study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master’s (MSc) level in Medical Laboratory Sciences. Methods The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessment of 20 MSc students. The revised Bloom’s taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for assessment of the quality of AI-generated content, with Cohen’s κ for inter-rater agreement. Results Compared to the mean students’ score which was 40/60 (66.8%), GPT-4 scored 54/60 (90.0%), followed by Bing (46/60, 76.7%), GPT-3.5 (44/60, 73.3%), and Bard (40/60, 66.7%). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5, GPT-4, and Bard. The CLEAR scores indicated that ChatGPT-4 performance was “Excellent” compared to “Above average” performance of ChatGPT-3.5, Bing, and Bard. Conclusions The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above-average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risks of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated.

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“…[4] sought to identify potential aspects of threat, excitement, and promise of AIED and highlighted the importance of traditional pedagogical values, such as skepticism, continuing to argue that the ultimate goal of education should be to promote responsible citizens and healthy educated minds. Therefore, the adoption of ethical frameworks for the use and development of AIED is extremely important, ensuring that it will be continually discussed and updated in light of the rapid development of AI techniques and their potential for widespread application [28].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Literacy in Artificial Intelligence as a Challenge for Teaching in Higher Education: A Case Study at Portalegre Polytechnic University

Lérias,

Guerra,

Ferreira

2024

Information

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The growing impact of artificial intelligence (AI) on Humanity is unavoidable, and therefore, “AI literacy” is extremely important. In the field of education—AI in education (AIED)—this technology is having a huge impact on the educational community and on the education system itself. The present study seeks to assess the level of AI literacy and knowledge among teachers at Portalegre Polytechnic University (PPU), aiming to identify gaps, find the main opportunities for innovation and development, and seek the degree of relationship between the dimensions of an AI questionnaire, as well as identifying the predictive variables in this matter. As a measuring instrument, a validated questionnaire based on three dimensions (AI Literacy, AI Self-Efficacy, and AI Self-Management) was applied to a sample of 75 teachers in the various schools of PPU. This revealed an average level of AI literacy (3.28), highlighting that 62.4% of responses are at levels 3 and 4 (based on a Likert scale from 1 to 5). The results also demonstrate that the first dimension is highly significant for the total dimensions, i.e., for AI Literacy, and no factor characterizing the sample is a predictor, but finding a below-average result in the learning factor indicates a pressing need to focus on developing these skills.

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Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks

Cited by 11 publications

References 30 publications

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5, and Humans in Clinical Chemistry Multiple-Choice Questions

Literacy in Artificial Intelligence as a Challenge for Teaching in Higher Education: A Case Study at Portalegre Polytechnic University

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