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About the usage of artificial intelligence for writing on academia



The integration of artificial intelligence (AI) in academia, specifically in the field of writing, is a topic of interest and debate among researchers. Several articles explore different aspects of this phenomenon.


In the study by Irfan et al. (ABNT citation: IRFAN et al., 2022), the authors examine how AI literacy and ChatGPT-3, an AI chatbot, enhance critical thinking and journalistic writing skills among journalism students. The results show a significant improvement in these skills with the use of ChatGPT-3. Similarly, in the article by Sheng et al. (SHENG et al., 2022), the authors propose SongMASS, an AI system for automatic songwriting. The system utilizes masked sequence to sequence (MASS) pre-training and attention-based alignment modeling to generate lyrics and melodies. Objective and subjective evaluations demonstrate that SongMASS produces higher quality outputs compared to baseline methods.

On the other hand, Lee (LEE, 2022) discusses the ethical implications of including AI chatbots as co-authors in scholarly articles. Nature and Science have expressed their position that AI chatbots cannot be listed as authors due to their non-human nature and inability to take responsibility for their writing. In the field of academic publishing, AI has shown potential in automating certain writing tasks, as discussed in the article by Vuong et al. (VUONG et al., 2022). The authors examine the current state and future of AI in academia, highlighting its ability to generate written content similar to human-made products. They also present their experiences working with ChatGPT in academic writing.

The use of AI in academic writing poses challenges and raises questions about the role of technology in shaping research practices, as discussed in the article by Stacey (STACEY, 2022). The article explores the impact of AI on academic conventions and assumptions, including referencing and plagiarism, and suggests that current norms may need to be reevaluated. These articles collectively demonstrate the growing influence of AI in academia and the potential benefits and ethical considerations associated with its usage in writing tasks.



 

Integration of Artificial Intelligence in Academia: A Case Study of Critical Teaching and Learning in Higher Education M. Irfan Liam Murray Sajjad Ali This study scrutinizes the role of AI literacy and ChatGPT-3 in enhancing critical reasoning and journalistic writing competencies among 50 third-term journalism students at Tajik National University. Given the escalating relevance of AI across sectors, including journalism, we aim to highlight the potential advantages of incorporating AI utilities in journalism pedagogy. We utilized a mixed- methods approach, comprising both quantitative and qualitative data collection techniques, for a comprehensive examination of the influence of AI literacy and ChatGPT-3 on student skill development.We gathered insights via surveys and interviews, revealing the impact of AI on learning outcomes. Our findings suggest a significant improvement in students' critical thinking and journalistic writing skills with ChatGPT-3 usage. The integration of AI tools in the classroom encourages in-depth analysis and collaboration, thereby enhancing students' writing skills. The results underline the importance of AI literacy in journalism education, preparing students for the rapidly transforming, AI-centric journalism industry. Selected Trends in Artificial Intelligence for Space Applications D. Izzo Gabriele Meoni Pablo G'omez Domink Dold Alexander Zoechbauer The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced. As more and more aerospace engineers are becoming aware of new trends in AI, traditional approaches are revisited to consider the applications of emerging AI technologies. Already at the time of writing, the scope of AI-related activities across academia, the aerospace industry and space agencies is so wide that an in-depth review would not fit in these pages. In this chapter we focus instead on two main emerging trends we believe capture the most relevant and exciting activities in the field: differentiable intelligence and on-board machine learning. Differentiable intelligence, in a nutshell, refers to works making extensive use of automatic differentiation frameworks to learn the parameters of machine learning or related models. Onboard machine learning considers the problem of moving inference, as well as learning, onboard. Within these fields, we discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT), giving priority to advanced topics going beyond the transposition of established AI techniques and practices to the space domain. Can an artificial intelligence chatbot be the author of a scholarly article? Ju Yeon Lee At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics. Are we at the start of the artificial intelligence era in academic publishing? Q. Vuong Viet-Phuong La Minh-Hoang Nguyen Ruining Jin T. Le Machine-based automation has long been a key factor in the modern era. However, lately, many people have been shocked by artificial intelligence (AI) applications, such as ChatGPT (OpenAI), that can perform tasks previously thought to be human-exclusive. With recent advances in natural language processing (NLP) technologies, AI can generate written content that is similar to human-made products, and this ability has a variety of applications. As the technology of large language models continues to progress by making use of colossal reservoirs of digital information, AI is becoming more capable of recognizing patterns and associations within given contexts, making it especially helpful in assisting with various professional writing tasks [1]. In this paper, we will discuss several key points regarding the current state and future of AI in academia. Furthermore, since in certain aspects, acts can speak louder than words, we will also present some of our hands-on experiences of working with ChatGPT. Artificial Intelligence, ChatGPT and Organizational Studies Josiane Silva de Oliveira Ianaira Barretto Souza Neves Abstract The advancement of the use of Artificial intelligence in the scientific field, such as Connectedpapers and ChatGPT, has allowed us to reflect on how technological tools have become mediators and participants in the context of education and academia. In the field of organizational theories, despite the different perspectives on understanding the incorporation of AIs in academic practice, we highlight two challenges in our daily academic life. The first challenge refers to confronting the digital colonialism that AIs impose on us, considering that they constitute themselves through the reproduction of language models programmed in countries of the "global north” The second challenge concerns its unfoldings in the process of automation of academic writing in administration. We consider the need to reflect on how the uses of AIs can contemporarily reproduce our place in the field of science as one of scientific data extractivism, the limitation of the teaching of academic writing in administration as the reproduction of an "assisted programming" of hegemonic language models, and the possibilities of disentangling as a way of counteracting this dynamic of automation of article writing in administration. Reimagining Academic Writing in Academia 4.0 to De‑incentivise Plagiarism A. Stacey Academic research and scientific publication are being influenced irreversibly by what is referred to as the fourth industrial revolution. The exponential growth in the number of research publications continues, information and communication technology (including artificial intelligence) is making available research data and tools with unprecedented capabilities, and online open access to publications has enabled greater and more rapid access by other researchers. Changes of research practice and the behaviour of researchers and authors as a result of these developments are evident, and are challenging the criteria, norms and standards by which the quality and integrity of research has historically been judged. The manner in which prior research is being accessed, reproduced, applied and acknowledged is an example of such changes. In academia, the presentation of the ideas or writings of another without them being explicitly attributed to the original source has always been regarded as plagiarism and considered serious misconduct. Yet when such ideas and writings are freely available and in the public domain, they arguably fulfil the criteria for being considered common knowledge which don’t necessarily need to be referenced. This article presents examples of acceptable replication and reuse of the work of others, and examples of how plagiarism is manifesting differently because of information and communication technologies, including plagiarism software. It is argued that while paraphrasing previous authors result from understanding and applying their prior research, paraphrasing may simply be a grammatical or mechanistic process that does not attest understanding and application. It is provocatively suggested that current norms and standards of academic writing, including referencing, may no longer be appropriate. Relatively modest amendments to academic conventions and assumptions are proposed that could lead to a new paradigm of more efficient research and scientific publications, acknowledging that this would place greater burden of responsibility on the users, reviewers, editors and examiners of research to be familiar with extant knowledge. Artificial or Augmented Authorship? A Conversation with a Chatbot on Base of Thumb Arthritis Ishith Seth Peter Sinkjær Kenney G. Bulloch D. Hunter-Smith Jørn Bo Thomsen W. Rozen Summary: ChatGPT is an open artificial intelligence chat box that could revolutionize academia and augment research writing. This study had an open conversation with ChatGPT and invited the platform to evaluate this article through series of five questions on base of thumb arthritis to test if its contributions and contents merely add artificial unusable input or help us augment the quality of the article. The information ChatGPT-3 provided was accurate, albeit surface-level, and lacks analytical ability to dissect for important limitations about base of thumb arthritis, which would not be conducive to potentiating creative ideas and solutions in plastic surgery. ChatGPT failed to provide relevant references and even “created” references instead of indicating its inability to perform the task. This highlights that as an AI-generator for medical publishing text, ChatGPT-3 should be used cautiously. AI Technology and Academic Writing Valerie A. Storey Evidence shows that artificial intelligence (AI) has become an important subject in academia, representing about 2.2% of all scientific publications. One concern for doctoral programs is the future role of AI in doctoral writing due to the increase in AI-generated content, such as text and images. Apprehensions have been expressed that the use of AI may have a negative impact on a doctoral student's ability to think critically and creatively. In contrast, others argue that using AI tools can provide various benefits resulting in rigorous research. This conceptual article first discusses the developing relationship between AI and dissertation writing skills. Second, the article explores the origins of the traditional dissertation and outlines 21st-century dissertation options which reflect contextual needs and utilization of AI. Third, identified writing challenges are highlighted before turning to an in-depth examination of AI-generated tools and writing craft skills required to complete the five chapters of a traditional dissertation. SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint Zhonghao Sheng Kaitao Song Xu Tan Yi Ren Wei Ye Shikun Zhang Tao Qin Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the paired lyric and melody data are limited, which affects the generation quality of the two tasks, considering a lot of paired training data are needed due to the weak correlation between lyric and melody; 2) Strict alignments are required between lyric and melody, which relies on specific alignment modeling. In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. Specifically, 1) we extend the original sentence-level MASS pre-training to song level to better capture long contextual information in music, and use a separate encoder and decoder for each modality (lyric or melody); 2) we leverage sentence-level attention mask and token-level attention constraint during training to enhance the alignment between lyric and melody. During inference, we use a dynamic programming strategy to obtain the alignment between each word/syllable in lyric and note in melody. We pre-train SongMASS on unpaired lyric and melody datasets, and both objective and subjective evaluations demonstrate that SongMASS generates lyric and melody with significantly better quality than the baseline method. Can AI Mitigate Bias in Writing Letters of Recommendation? Tiffany I Leung A. Sagar Swati Shroff Tracey L. Henry Letters of recommendation play a significant role in higher education and career progression, particularly for women and underrepresented groups in medicine and science. Already, there is evidence to suggest that written letters of recommendation contain language that expresses implicit biases, or unconscious biases, and that these biases occur for all recommenders regardless of the recommender’s sex. Given that all individuals have implicit biases that may influence language use, there may be opportunities to apply contemporary technologies, such as large language models or other forms of generative artificial intelligence (AI), to augment and potentially reduce implicit biases in the written language of letters of recommendation. In this editorial, we provide a brief overview of existing literature on the manifestations of implicit bias in letters of recommendation, with a focus on academia and medical education. We then highlight potential opportunities and drawbacks of applying this emerging technology in augmenting the focused, professional task of writing letters of recommendation. We also offer best practices for integrating their use into the routine writing of letters of recommendation and conclude with our outlook for the future of generative AI applications in supporting this task. Download




 

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