ChatGPT, Scholarly Trust and Inconsistency

Generative Artificial intelligence tools like ChatGPT are to transform academic work in many ways. Among its many capabilities, one that particularly interests me is its potential to assist as a research assistant with compiling secondary sources—a time-consuming yet critical part of scholarly research. Curious about its performance, I recently conducted an experiment. The results left me impressed, and ultimately, perplexed. In this post, I’ll explain the experiment, share what went right (and wrong), and reflect on what these outcomes reveal about ChatGPT’s usefulness—and limitations—for academic work.

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The Experiment

I prompted ChatGPT (I used the free version and the Scholar GPT option) to compile lists of secondary sources for three very different topics: AI governance, the philosophy of human agency, and the Prince in Shakespeare’s Romeo and Juliet. My prompts were simple but specific, requiring bibliographic information, direct quotations, and page references.

For the first topic, AI governance, ChatGPT performed quite well. Most of the sources it provided were real, and many of the quotations were accurate. However, a small part of its output was mere hallucination, with non-existent articles and invented citations. Still, I found its accuracy in this domain surprisingly high.

The second topic, the philosophy of human agency, was where ChatGPT truly excelled. Not only were the sources relevant, but the quotations and even the page references were entirely accurate with one exception. I double-checked them – it was easy as most of the books were on my bookshelf – and found that everything aligned perfectly with the original texts.

As far as the third topic is concerned, the Prince in Romeo and Juliet, ChatGPT performed as expected. While it managed to name actual books and authors, the quotations attributed to these works were nothing but invention. Worse, the journal articles it cited—along with their supposed quotations—were entirely hallucinated.

I wasn’t shocked that ChatGPT hallucinated information. This is a well-documented weakness of GAI tools, and anyone using them for research should be prepared to verify everything. What really bothers me is the inconsistency. For one topic, the tool delivered a mixed bag: mostly correct, with some inaccuracies. For another, it achieved almost absolute precision, as if it had memorised entire books. And for the third, it produced nonsense that only looked plausible at first glance.

What’s maddening about this inconsistency is that it raises an uncomfortable question: why is ChatGPT able to excel in one area and fail so catastrophically in another? Is it a matter of training data? Some topics, like foundational philosophical texts, are likely well-represented in its corpus, whereas niche areas, like specific aspects of Shakespearean studies, are probably underrepresented. But even so, the drastic difference in accuracy is hard to let go. It is also important to mention though that when I let ChatGPT know about the mere hallucination with R&J, and prompted it to come up with five verified sources, it provided journal articles with links – all accurate and relevant.

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Can We Trust GAI?

This experiment left me with mixed feelings about ChatGPT’s potential as a research assistant. On the one hand, it’s capable of producing accurate and useful results—sometimes even impressively so. On the other, its tendency to hallucinate and its painful inconsistency make it unreliable.

As GAI tools become more embedded in academic workflows, this inconsistency presents a serious challenge. If ChatGPT can sometimes be astonishingly accurate, how are we to distinguish these moments from the ones where it confidently presents mere hallucinations? And more importantly, how much of our time will be spent trying to sort the real from the hallucinated? Should this be a matter of excellence in prompting, critical thinking, introducing filters on the machine end?

A Scholarly Home: Closing Speech of the Theatralia Conference

 It is a privilege for me to have been selected as the speaker to deliver one of the concluding addresses at our conference.

I would like to express my heartfelt gratitude to the institutions whose support made this conference a resounding success. Foremost among them is the Masaryk University and its exceptional Department of Theatre Studies. It is with great pleasure that I remark upon the venue, which proved to be nothing short of perfect—a harmonious blend of intellect and joy. The conference room itself was a haven where ideas blossomed, infused with flawless technological arrangements. However, let us not forget about the Other place—a sanctuary where primal needs such as hunger, thirst, and moments of small talk were gracefully accommodated with delicious food, drinks, coffee and tea, and occasionally wine. Furthermore, I extend my thanks to the Visegrad Fund for their financial support.

The achievement of this momentous occasion is owed not only to the collective efforts of institutions, but also to the dedicated individuals who have tirelessly contributed in the days leading up to this event. I would like to express my sincere gratitude to our colleagues who have played an integral role in reaching this point. Firstly, I extend my heartfelt appreciation to the members of the organizing committee, namely Sarka, Ivona, Klara, Eliska, and Lenka. Their unwavering commitment and meticulous planning have laid the foundation for the success of this conference. Additionally, I am deeply grateful to the chairs of the various panels and plenary lectures, namely Nico, Anna, Jacek, and Ivona, whose expertise and guidance have enriched our discussions. Furthermore, I would like to extend my thanks to the students who have offered invaluable assistance throughout this journey: Klara, Tereza, Johana, and Aneta. Finally, I extend my most sincere appreciation to the participants of this conference and the enthusiastic audience. Your active engagement and support have truly contributed to the vibrancy and success of this event. To each and every one of you, I extend my gratitude. Thank you all!

In addition to the institutions and people, I must express my gratitude towards the conference itself—a magnificent gathering that had a single objective, i.e. to bring us together after months of working together but not meeting in person. This event has brought us together under the common objective of exploring our Visegrad Fund sponsored project, which aims to unravel the relevance of Shakespeare in our region, how his works continue to resonate with us, enriching our lives in multifarious ways. The structure of the conference mirrored our shared purpose, featuring a harmonious blend of plenary and shorter presentations and engaging discussions on the four position papers that have been laboriously crafted over the past few months and weeks. And let us not forget about the coffee breaks where discussions went on, ideas were shared about Shakespeare, the project and stories of more personal nature.

Within the confines of this intellectual and infrastructural framework, I am immensely grateful for the invaluable insights I have gained. Pavels’s poignant words continue to reverberate within me, compelling me to contemplate the multifaceted personas that have shaped Shakespeare’s identity: the actor’s Shakespeare, the emancipatory Shakespeare Intelligentia’s Shakespeare, Folk’s Shakespeare, Publishers’s Shakespeare, Shakespeare for the initiates, Shakespeare as custom and routine. Along with these, that our approaches may be geared towards dramacentric, metaphorical, and to a relation to avoid hierarchies. Marta’s exploration of an innovative YouTube Henry V and its relevance in the Polish academic and theatrical context, and how much the play is “horrifyingly actual.”

The shorter conference papers also illuminated aspects of Shakespeare’s reception. Martin’s discussion of Machacek’s Henry V, David’s Fortinbrases, Filip’s discussion of the roles of the Porter and Lady Macbeth’s simply put alternatives offered to Macbeth. Anna’s (impersonated by Filip) presentation of dramaturgical translation, Kinga’s exploration of Shakespeare’s presence in three films about Romani people, and last but not least David’s Dark Ladies via Emilia and Lucy Negro.

In conjunction with these captivating presentations, it was truly a feast for the intellect to delve into the world of position papers, exploring the myriad ways in which the concept of border crossing can be conceptualized. Engaging in spirited discussions, both in writing and during the round table sessions. The discussions started and will continue in the form of commentaries added to the google documents. And also during the round table sessions the teams really have been hungry for the feedback to improve the papers. It has been a feast to understand what there is in a name, what connotations are carried by a choice of how to refer to the cultural region. How politics emerges on the stage and how this emergence can be seen in a variety of border crossings, and how cultural memory can be enhanced to shape the present by coming to terms with the past. How Shakespeare has been popularized around us, what the common features are and what the national, territorial differences are to be seen, from mobility to the burlesque, from cabaret and television to experimental theatre practices. How theatrical blending, i.e. textual/intermedial/transmedial hybridity takes place with Shakespeare, from ballet and dance theatre, through puppetry, to musical, and opera, in which the movement away from the text enables inter-and transnationality. Furthermore, it has been a fascinating endeavour to identify the areas of overlap, recognizing the gaps that present themselves as opportunities for enrichment, and exploring ways in which we can harness the achievements of other teams.

What is it that we are going to take home? Peace and happiness? Yes, because we have achieved much as far as the project is concerned. No, because we also see that there is much work ahead before the position papers can be handed in on 31 August.

But more importantly what we take away is a sense of home. Amidst all the differences that we carry with us, it is remarkable to recognize that we can find a sense of belonging within this region. It is a delightful reminder that even while being far away from our familiar surroundings, here in Brno, at the Masaryk University’s Department of Theatre Studies, we can create a collective feeling of being at home. This sense of home emerges from our unwavering ability to continually discover the shared threads that bind us, transforming us into a scholarly family. Together, we embark on an intellectual journey where humanist friendship flourishes, and our diverse perspectives converge, creating a harmonious tapestry that transcends physical, cultural and personal borders. It is through this profound connection that we find solace, kinship, and a sense of belonging, despite the distances that may separate us. So let us take this sense of scholarly home with us when heading home!

(7 June, 2023, Masaryk University, Brno)

ChatGPT and Higher Education

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As the arrival of ChatGPT has opened up new possibilities for students to complete written assignments, universities must carefully consider how to approach this new tool in their policies. This careful consideration is even more important for universities where the students are not native speakers of English, where linguistic proficiency is part of the evaluation of student performance. There are several options available, each with its own advantages and drawbacks, but my preference is some forward-looking option.

One option is to ban the use of ChatGPT and to implement severe punishments for any students caught using it. This a choice made by a few educational institutions worldwide, and the advantage is that this is clear and simple. However, this approach may not be practical, as it may be difficult to monitor the use of this tool effectively, even if there have been tools developed to spot the use of ChatGPT. Moreover, some students may still find ways to use ChatGPT discreetly, making it difficult to enforce such a policy effectively.

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Another option is to do nothing and to treat ChatGPT as a tool similar to spell, grammar, and style checkers. If students can use the latter tools, why not let them deploy a somewhat more advanced tool as well. While this approach may seem reasonable at first glance, it raises several issues. For one, it makes it challenging to evaluate the student's actual writing ability, especially in the case of non-native speakers of English. Moreover, ChatGPT's ability to produce perfectly written papers may be a threat to academic integrity, as it could be difficult to differentiate between work produced by students and that produced by AI. Thus, when it comes to evaluation, it is difficult to tell who is evaluated.

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A third option is to abandon written assignments that are not done in class altogether. This is again a simple and effective choice. However, this approach may not be beneficial, as written assignments are often an essential part of many courses and can provide valuable opportunities for students to develop their writing skills, skills that are transferable skills, so can be used in every walk of life. And if the future of content creation is the use of applications similar to ChatGPT, then the educational programme enforces a methodology that is alien to real life.

A more forward-thinking approach may be to teach students how to use ChatGPT effectively, responsibly, and more importantly critically, recognizing that AI-generated texts may well be the way of the future. This approach may involve revising the evaluation of written assignments to take into account the use of AI tools, emphasizing the importance of critical thinking and the ability to integrate information from multiple sources in the writing process. It may also involve teaching students how to evaluate the reliability and accuracy of information generated by AI. And when the students’ linguistic abilities

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are to be evaluated, the in-class methodology can be used. So teaching the responsible use of ChatGPT and revising the evaluation methodology go hand in hand.

Whatever the chosen approach, universities must also consider what to do with students who choose not to use ChatGPT. This is even more so, if students’ choice is motivated by the lack of technological resources. Instructors must make it clear that students have a choice in how they complete their assignments, but that they will be evaluated on the quality of their work, regardless of the tools they use. But again, the quality of the product may well depend on the students financial background or technological interests, so the avoidance of deepening the digital divide should be in the focus of our attention.

In conclusion, the arrival of ChatGPT presents both opportunities and challenges for universities in their approach to written assignments. While it may be tempting to ban the use of this tool, it is essential to recognize its potential benefits and to find ways to integrate it effectively into the learning process. This requires careful consideration of the implications of AI-generated texts and the development of policies that promote responsible use of this technology. What would be your choice?

 PS. I have given very detailed prompts and introduced quite a few sentences, clauses. So again, it is rather difficult to differentiate what is mine and what is exclusively that of ChatGPT.

Text Synthesis and the Beehive

In the previous blogposts with the help of the OpenAi chatbot I launched a series of posts casting light on how AI may/will change academic work. In this post the starting point is that the way we speak about AI will inevitably determine how we think about it, so it seems important to create a vocabulary which will enable a rational discourse on OpenAI chatbot. For this end I am going to focus on the vocabulary to represent machine and human text creation. As far as the method is concerned, this post is the result of composing the responses of the OpenAI chatbot to a variety of questions into a coherent post. Again colours will distinguish between the voice of the bot and mine. Let’s get down to details then.

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There are several terms and metaphors that can be used to describe the process of text creation by AI. One common term is "text generation," as it accurately describes what AI is doing: generating text. Some people also describe AI text generation as "text composition," which emphasizes the idea that AI is composing text from various elements in a structured way, much like a composer composes music. Another term is "text synthesis," which emphasizes the idea that AI is synthesizing text from various sources and patterns that it has learned. In this post the focus will be on “text synthesis” and a metaphor related to it.

The term "text synthesis" refers to the process of generating a new text by combining and transforming existing texts. In the case of AI, text synthesis is the process of using algorithms and models to generate coherent and meaningful text based on patterns and structures learned from training data, which can include a variety of written content such as news articles, books, websites, and more. The AI system then uses this training data to learn patterns and relationships between words, phrases, and sentences, and can use this knowledge to generate a new text.

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Two significant aspects of text synthesis may illuminate the process. One key aspect is that text synthesis is a probabilistic process. This means that the AI system generates a text based on the probability of certain words, phrases, and sentences appearing together, rather than by following a strict set of rules. Another important aspect of text synthesis in AI is that it can be controlled and fine-tuned through various means, such as adjusting the amount of randomness in the output or controlling the length of the generated text. This can help to ensure that the generated text is coherent, relevant, and meets certain quality criteria.

To further explore the idea of text synthesis I will use an analogy from the animal world, namely that of a beehive. Just as a beehive is a collective,”  networked “entity made up of individual bees working together, AI text synthesis is a process in which individual pieces of information are combined and transformed into a cohesive whole. Just as bees gather nectar and pollen from flowers, AI text generators gather information from a wide range of sources. This information is then processed and transformed into a numerical representation that the AI can use to generate responses. Just as a beehive is able to produce honey through the collective efforts of its individual bees, AI text synthesis is able to produce coherent and informative text through the collective efforts of the information it has gathered. And just as the honey produced by a beehive is a unique and original product that reflects the collective efforts of the bees, the text produced by AI text synthesis is a unique and original product that reflects the collective efforts of the information that was used to train the AI.

The analogy with the beehive points towards the notion of the network, as the activity of the bees is a networked activity. Text synthesis is thus similar to working with a network, a network that is made up of nodes and edges. So how does text synthesis work if described as working with edges and nodes?

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Think of each word or phrase in a text as a node in a network, and the connections between these nodes as edges. In text generation, the AI's algorithms use these nodes and edges to generate a new, coherent text. The nodes in the network can represent different types of information, such as parts of speech, concepts, emotions, and more. The edges between the nodes can represent relationships between these elements, such as associations, dependencies, and similarities.

When the AI generates text, it uses this network to determine which words or phrases should be included in the text, and in what order they should appear. It can generate text by connecting existing nodes in new ways, or by creating new nodes and edges to represent new information. For example, if the AI is generating a story, it might start with a node representing a protagonist, and then use edges to connect this node to other nodes representing events, characters, and locations in the story. As the AI generates new text, it can continue to build upon this network, adding new nodes and edges to create a complete, coherent story. In this way, text generation by AI can be thought of as a networked activity, as the AI uses nodes and edges to connect and build upon existing information in order to generate new text.

In conclusion, after having seen that AI or more precisely Machine Learning instead of writing, creating texts only synthesises other texts into a new one, two considerations may follow. One that the concept of “new” is to be elaborated on. To what extent can we talk about a new and genuine text if it is a synthesis of relevant texts? Does ML processes echo the ideas of texts that it has been trained on? Two, and this is a little unsettling, I created this text by synthesizing the responses that the OpenAI chatbot provided in reaction to my inquiries and prompts. This then complicates rather than simplifies the discourse on text synthesis as a distinction between human and machine text creation, doesn’t it?

Creative process: Machine learning vs Human

 Artificial intelligence (AI) has come a long way in recent years, and one area in which it has made significant strides is in the realm of image generation. AI image generators like DALL-E, Stable Difusion are able to create images from text prompts, and the results can be quite impressive. Although both machine learning and artists rely on images made before them, it's important to note that the creative process behind images constructed by image generators is quite different from that of an artist.

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Artists often look to the work of other artists for inspiration. They might study the techniques and styles of their predecessors and contemporaries, and use this knowledge to inform their own creative process. Artists might also look to the natural world, as well as their own experiences and emotions, for inspiration. The goal is to find new ways to express themselves and create something that is unique and original in light of the traditions.

In contrast, AI image generators like DALL-E use a different approach. They are trained on a vast dataset of images, and they use this data to learn patterns and relationships between different elements. When presented with a text prompt, the AI uses these patterns to create an image that best matches the prompt. The AI does not have the ability to look at an image and find inspiration in the way an artist would. Instead, it finds common denominators based on the text prompt and creates an image accordingly.

This difference in approach is reflected in the images produced by the two methods. Images generated by AI tend to be highly detailed, but they can also be somewhat formulaic. They often lack the sense of spontaneity and individuality that is often present in the work of an artist. In contrast, images created by an artist tend to be more expressive and unique, reflecting the artist's personal vision and creative process.

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It's worth noting that AI image generators can be a useful tool for artists, and can be used to create images that might not be possible with traditional techniques. For example, an artist might use AI to generate a complex pattern that they can then incorporate into their own work. Additionally, AI can be used to create variations on a theme, which can be a useful starting point for an artist to create their own unique work.

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In conclusion, while AI image generators like DALL-E can create impressive images, the creative process behind them is quite different from that of an artist “even if both processes rely on previous traditions”. Artists look for inspiration in the work of other artists, the natural world and their own experiences, and emotions, whereas AI image generators rely on patterns learned from a vast dataset of images. The resulting images are highly detailed and formulaic, lacking the sense of spontaneity and individuality that is often present in the work of an artist. However, AI can be a useful tool for artists to generate variations on a theme and create images that might not be possible with traditional techniques.

PS Similar to the previous post, I kept the words I wrote and those of the OpenAI chatbot separate when I was writing this one. As it is visible, in this post, my words are almost insignificantly few. This is true, but not because the chatbot has evolved between posts so quickly that it no longer needs my input. This post differs from the previous one in that I provided a more detailed prompt this time, outlining the genre, the required length, the claim, as well as the supporting evidence and applications. This raises even another issue, namely how to conceptually and visually separate the various voices and determine what is my work and what is that of OpenAIchat. It is very likely that in the forthcoming posts I will explore this problem in more detail.

ChatOpenAI, Copyright, Editorial work

In the previous post I used quotation marks to indicate the text has been generated by chat OpenAI. This practice seems to be somewhat disorienting, so I am going to use different colours for the two voices: my words will be in the standard green of this blog and those of chat OpenAI in purple. This distinction in voices is particularly crucial here, as in this blog post I am going to start meditating about copyright issues.

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When generating the text below, first I asked questions from OpenAI, requested clarifications, further explanations. Then I put the responses into a prompt before requesting OpenAI to create a blog post. The outcome wasn't terrible, but it wasn't perfect either, so I made OpenAI rewrite the post with a little shift in emphasis. Although the outcome was already better, there was still need for improvement, so I copy-pasted into it an answer to a previous question, rearranged the order of the paragraphs, united and divided paragraphs when necessary, and deleted sentences. This is what I do as an editor to journals, too, so I treated the texts as one written by a human being. And below is the result.

With AI, it is possible to generate high-quality content quickly and efficiently, making it an attractive tool for businesses and individuals alike. However, there are some concerns about copyright when it comes to AI-generated text. In this blog post, I will explore the importance of properly acknowledging sources and avoiding plagiarism when using AI for text generation.

First and foremost, it is important to ensure that the sources that the AI is using are properly acknowledged and cited. This means that any content that is generated by the AI should include proper attribution to the sources that were used. This can typically be done by including in-text citations and a list of references at the end of the generated content, just as you would with any other type of written work. It is also important to ensure that you have the necessary permissions to use the sources in your content, and to give credit to the original authors in a way that is consistent with copyright laws and best practices. This is important for two reasons: first, it ensures that the content is not plagiarized, and second, it allows readers to easily verify the accuracy and reliability of the information presented.

When, however, it comes to training the OpenAI model, it draws on patterns and structures that it learned from vast amounts of text data that were fed into it during its training process. This data can include a wide range of text, such as news articles, books, websites, and other types of written content. The model processes this data and transforms it into a numerical representation that it can use to generate responses. However, it's important to remember that because the AI model does not have access to the specific texts that were used to train it, it is not possible for the model to identify specific texts or sources that were used in its training process. That's why it is a good idea to perform a thorough check of your content before publishing it to ensure that it does not contain any potentially infringing material. This can include running your content through a plagiarism checker or reviewing it manually to ensure that all sources are properly cited and acknowledged.

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In addition to acknowledging the sources that the AI uses, it is also important to acknowledge the work of the AI itself. This can be done by including a disclaimer or statement in the content that is generated, indicating that it was produced using AI. This ensures that the work of the AI is recognized and credited, and helps to avoid any potential confusion about the authorship of the content.

In conclusion, AI can be a powerful and efficient tool for text generation, but it is important to navigate copyright issues in a responsible and ethical manner. This includes properly acknowledging the sources that the AI uses, acknowledging the work of the AI itself and avoiding plagiarism. By following these best practices, we can ensure that the benefits of AI are realized while also respecting the rights of others.”

It should also be emphasized though that it is rather challenging to accurately credit OpenAI’s efforts because I was unable to colour-code the editorial efforts and prompts that went into the creation of this particular text. Therefore, in terms of execution of the task and drawing of conclusions, this article may be considered a failure in and of itself. Even if this is the case, I hope it has at least provided some food for thought. If you have suggestions for how to properly attribute OpenAI’s work, please leave them in the comment area below. You may also do this if you think there is something in the text that needs more explanation.

Artificial Intelligence and Academic Work – introductory thoughts

 “As someone who is interested in the intersection of artificial intelligence and academia, I have been closely following the development of AI text and image generation tools and the ways in which they are beginning to influence the way we work and create” in academia. And also as 2023  will be clearly about AI. “In this series of blog posts, I want to delve into the potential impacts these tools may have on academia, specifically in terms of academic work and workflow.” What is going to be, however, more interesting is that I am going to give prompts to these tools about what they should write and what images they should generate, and this way we’ll also see how effective the tools are. This post has been written mostly by OpenAI chatGPT, and the images have been generated exclusively by NighCafe Studio. To distinguish the different voices, I'll put the words that Open AI chatGPT generated between quotation marks and my own lines without them.

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“What are AI text and image generation tools?

AI text generation refers to the use of artificial intelligence to produce written content. These tools utilize machine learning algorithms to analyse and understand language patterns, allowing them to generate coherent and often convincing text on a variety of topics. This can be something as simple as a social media post or as complex as a research paper.

AI image generation involves the use of AI to create original images or manipulate existing ones. This can range from simple tasks like adding a watermark to a photo to more complex tasks like generating entirely new images based on a set of specified characteristics.

How are these tools being used in academia?

One area where I have seen AI text generation being utilized in academia is through the use of automated abstract and summary writing “and also in higher education potentially by students to generate home assignments. These tools can analyse a research paper and produce a concise summary of its key points, saving researchers time and effort in the process.” Furthermore, as I teach at a university in Hungary, I know students will start using it for their written assignments. Instead of being afraid of this, I will teach them how to use it, and show that even to be able to appreciate it we have to learn much together. As I teach essay writing, I will introduce this to my students. Also I will thematise its use, its fair use.

“AI image generation is also being used in academia, particularly in fields such as biology and medicine. For example, AI can be used to create realistic 3D models of biological structures, which can be useful for education and research purposes.” Also for the sake of creating illustrations, one may well use these tools. And also, I will use the images as a model to chart cultural, visual biases, as the images are related closely to the corpus that machine has been trained on. For example, it is interesting to explore the image of this post: the colour scheme, the human hand (why a man and not a woman – the prompt was gender neutral), why a wooden desk, no ring on the fingers, why a pullover and not a shirt, why a laptop and not a PC, the perspective?

“What are the potential impacts of these tools on academic work and workflow?

As someone who has seen first hand the demands placed on academics to publish research and teach, I can see the potential for AI text and image generation tools to increase efficiency and productivity. By automating certain tasks, researchers can save time and focus on more important and complex aspects of their work.

Another potential impact is the democratization of information. AI text generation tools have the potential to make research more accessible by generating summaries and abstracts that can be easily understood by a wider audience. Similarly, AI image generation tools can help to make scientific concepts more visual and easier to understand for a wider audience.

However, it's important to note that these tools also have the potential to be misused. For example, there have been instances of AI-generated text being used to spread misinformation or propaganda.” And it is also possible that papers, assignments will be written with the help of AI. “In order to mitigate these risks, it will be important for researchers and university educators to establish guidelines for the ethical use of these tools.”

“Conclusion:

As AI text and image generation tools continue to develop and improve, it's crucial that we consider their potential impacts on academia. While these tools have the potential to increase efficiency and productivity, as well as democratize information, it's important to also consider the potential risks and establish guidelines for their ethical use. In this series of blog posts, I plan to explore these topics in greater depth and examine the ways in which AI text and image generation tools are shaping the future of academia.” If interested in these cooperative meditations, please, read and maybe comment on the posts!