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– ## Discussion4.docx **## Discussion4.docx** 2 **Discussion 4** Name Institution Course Instructor Date **Discussion 4** **Part 1** The article titled “AI-generated research paper fabrication and plagiarism in the scientific community” written by Elali and Rachid in 2023 is addressing the impact of…
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– ## Discussion4.docx
**## Discussion4.docx**
2
**Discussion 4**
Name
Institution
Course
Instructor
Date
**Discussion 4**
**Part 1**
The article titled “AI-generated research paper fabrication and plagiarism in the scientific community” written by Elali and Rachid in 2023 is addressing the impact of AI-generated research fabrication where it is focusing on its power of running from detection from algorithm and human. They are showing the way language models such as ChatGPT can be used to write studies that are fabricated and yet look convincing to be scientifically legitimate. The authors describe the way this fabricated work can offer information that misguides the systematic reviews impact clinical decision-making and scientific literature. Zimba and Gasparyan (2021) also highlight that even some of these works are difficult for AI detectors and experienced reviewers to detect, mostly when the individuals have used the paraphrasing tools or reworded the content. The paper also evaluates the motivation that is causing people to engage in this misconduct, which includes the desire to have prestige, academic pressure, and competition within medical training (Elali & Rachid, 2023). They suggest that one way to address this is to have mandatory data proof when submitting, invest in advancing detection tools, and have stronger editorial vigilance to maintain integrity within research.
**Part 2**
The article titled “Artificial Intelligence, Chatbots, Plagiarism and Basic Honesty: Comment” authored by Kleebayoon and Wiwanitkit published in 2023 is evaluating the ethical impacts that people will have through using AI and Chatbots when doing academic tasks, mostly focusing on plagiarism. They highlight that even though AI is a tool, it can also meet the traditional definitions regarding plagiarism, mostly when the creator hides that they have used AI and represents the work as if they have entirely done it themselves (Kleebayoon & Wiwanitkit, 2023). They explain that the ethical issue does not result from people using AI but due to the individuals failing to acknowledge that they have used it when doing scientific or academic research. These authors are therefore comparing this approach to academic misconduct, where some students have been tasking other individuals to do their work for them, which is a misrepresentation and fraud. However, they are still acknowledging that technological tools such as AI are used for legitimate contexts, such as automated grammar checker bioinformatics, mostly when their use has been correctly declared. They, therefore, hold a position that honesty should remain to maintain scientific integrity, and when there is AI advancement, it is important for students and professionals to know how to apply it ethically.
**Part 3**
The article titled “Out of the laboratory and into the classroom: the future of artificial intelligence in education” authored by Daniel Schiff published in 2020 is addressing the role of artificial intelligence in education (AIEd) where it is comparing the promises that it has and the risks that is likely to expose to the individuals. Even though AIEd technologies such as intelligent tutoring systems are providing scalability and personalized instructions, the article is raising an alarm for people to assume that this success could not have happened without them addressing the challenges faced during implementation and how important is the teacher-student interaction (Schiff, 2020). Schiff also highlights the contrast that the AIEd's optimistic visions are having in trying to change education, where some of the issues raised include bias, deprofessionalization, and standardization of this teaching. He also highlights that there is a likelihood of AIEd reinforcing the current inequities when the development focuses more on rich groups and some pedagogies, mostly those within STEM. He is therefore advocating for interdisciplinary collaboration and responsible research frameworks such as engaging tutors in the guiding of ethical and equitable AIEd development. He is also highlighting that it is important to attain a balance between innovation and the educational values where he suggest that it is important to have inclusive and cautious approaches to attain AIEd integration.
**References**
Elali, F. R., & Rachid, L. N. (2023). AI-generated research paper fabrication and plagiarism in the scientific community.
_Patterns_,
_4_(3), 1–4. https://doi.org/10.1016/j.patter.2023.100706
Kleebayoon, A., & Wiwanitkit, V. (2023). Artificial Intelligence, Chatbots, plagiarism and basic honesty: Comment.
_Cellular and Molecular Bioengineering_,
_16_(2), 173–174. https://doi.org/10.1007/s12195-023-00759-x
Schiff, D. (2020). Out of the laboratory and into the classroom: The Future of Artificial Intelligence in Education.
_AI & SOCIETY_,
_36_(1), 331–348. https://doi.org/10.1007/s00146-020-01033-8
Zimba, O., & Gasparyan, A. (2021). Plagiarism detection and prevention: A primer for researchers.
_Rheumatology_,
_59_(3), 132–137. https://doi.org/10.5114/reum.2021.105974
**## Discussion4.docx**
2
**Discussion 4**
Name
Institution
Course
Instructor
Date
**Discussion 4**
**Part 1**
The article titled “AI-generated research paper fabrication and plagiarism in the scientific community” written by Elali and Rachid in 2023 is addressing the impact of AI-generated research fabrication where it is focusing on its power of running from detection from algorithm and human. They are showing the way language models such as ChatGPT can be used to write studies that are fabricated and yet look convincing to be scientifically legitimate. The authors describe the way this fabricated work can offer information that misguides the systematic reviews impact clinical decision-making and scientific literature. Zimba and Gasparyan (2021) also highlight that even some of these works are difficult for AI detectors and experienced reviewers to detect, mostly when the individuals have used the paraphrasing tools or reworded the content. The paper also evaluates the motivation that is causing people to engage in this misconduct, which includes the desire to have prestige, academic pressure, and competition within medical training (Elali & Rachid, 2023). They suggest that one way to address this is to have mandatory data proof when submitting, invest in advancing detection tools, and have stronger editorial vigilance to maintain integrity within research.
**Part 2**
The article titled “Artificial Intelligence, Chatbots, Plagiarism and Basic Honesty: Comment” authored by Kleebayoon and Wiwanitkit published in 2023 is evaluating the ethical impacts that people will have through using AI and Chatbots when doing academic tasks, mostly focusing on plagiarism. They highlight that even though AI is a tool, it can also meet the traditional definitions regarding plagiarism, mostly when the creator hides that they have used AI and represents the work as if they have entirely done it themselves (Kleebayoon & Wiwanitkit, 2023). They explain that the ethical issue does not result from people using AI but due to the individuals failing to acknowledge that they have used it when doing scientific or academic research. These authors are therefore comparing this approach to academic misconduct, where some students have been tasking other individuals to do their work for them, which is a misrepresentation and fraud. However, they are still acknowledging that technological tools such as AI are used for legitimate contexts, such as automated grammar checker bioinformatics, mostly when their use has been correctly declared. They, therefore, hold a position that honesty should remain to maintain scientific integrity, and when there is AI advancement, it is important for students and professionals to know how to apply it ethically.
**Part 3**
The article titled “Out of the laboratory and into the classroom: the future of artificial intelligence in education” authored by Daniel Schiff published in 2020 is addressing the role of artificial intelligence in education (AIEd) where it is comparing the promises that it has and the risks that is likely to expose to the individuals. Even though AIEd technologies such as intelligent tutoring systems are providing scalability and personalized instructions, the article is raising an alarm for people to assume that this success could not have happened without them addressing the challenges faced during implementation and how important is the teacher-student interaction (Schiff, 2020). Schiff also highlights the contrast that the AIEd's optimistic visions are having in trying to change education, where some of the issues raised include bias, deprofessionalization, and standardization of this teaching. He also highlights that there is a likelihood of AIEd reinforcing the current inequities when the development focuses more on rich groups and some pedagogies, mostly those within STEM. He is therefore advocating for interdisciplinary collaboration and responsible research frameworks such as engaging tutors in the guiding of ethical and equitable AIEd development. He is also highlighting that it is important to attain a balance between innovation and the educational values where he suggest that it is important to have inclusive and cautious approaches to attain AIEd integration.
**References**
Elali, F. R., & Rachid, L. N. (2023). AI-generated research paper fabrication and plagiarism in the scientific community.
_Patterns_,
_4_(3), 1–4. https://doi.org/10.1016/j.patter.2023.100706
Kleebayoon, A., & Wiwanitkit, V. (2023). Artificial Intelligence, Chatbots, plagiarism and basic honesty: Comment.
_Cellular and Molecular Bioengineering_,
_16_(2), 173–174. https://doi.org/10.1007/s12195-023-00759-x
Schiff, D. (2020). Out of the laboratory and into the classroom: The Future of Artificial Intelligence in Education.
_AI & SOCIETY_,
_36_(1), 331–348. https://doi.org/10.1007/s00146-020-01033-8
Zimba, O., & Gasparyan, A. (2021). Plagiarism detection and prevention: A primer for researchers.
_Rheumatology_,
_59_(3), 132–137. https://doi.org/10.5114/reum.2021.105974