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Academic literature on the topic 'ChatGPT Login'

Author: Grafiati
Published: 26 July 2024
Last updated: 28 July 2024

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Journal articles on the topic "ChatGPT Login"

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Fitria, Tira Nur. "Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay." ELT Forum: Journal of English Language Teaching 12, no. 1 (March 31, 2023): 44–58. http://dx.doi.org/10.15294/elt.v12i1.64069.

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ChatGPT is a product of AI that is currently being widely discussed on Twitter. This research reviews how ChatGPT writes English essays. This research is descriptive qualitative. The analysis shows that we can access ChatGPT on openai.com or chat.openai.com on the browser. If we do not have an account, we register via email, Google, or Microsoft account. After login, enter a question or statement in the conversation column provided. Send it and ChatGPT will respond and the answer appear quickly. The researcher tries ChatGPT “Can you help me in doing my English assignment?", and the ChatBot replies "Of course! I'd be happy to help you with your English assignment. What do you need help with? Do you have a specific question or task that you're working on, or is there a broader topic that you'd like help with? It would be helpful to have some more information so that I can better understand how I can assist you". Based on several tries, ChatGPT can answer all questions on various topics such as English essays including a descriptive text about Solo and My Family, recount text about personal experience and unforgettable moments, resolution in 2023, and future career. ChatGPT considers the event orders and writing order, including using main, explanatory sentences, and a conclusion. It uses two voices both active and passive voice. Besides, it considers tenses use related to the given topic essay. However, from examples of English essays produced by ChatGPT, it certainly requires further research to find out that the essay results are grammatically accurate.
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Idham, Arif Rahman, and M.Rizkillah. "ANALISIS KEEFEKTIFAN CHATGPT DALAM PERANCANGAN APLIKASI." Jurnal Informatika Teknologi dan Sains (Jinteks) 6, no. 2 (May 25, 2024): 115–21. http://dx.doi.org/10.51401/jinteks.v6i2.4050.

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Berdasarkan hasil penelitian yang telah direview, ChatGPT mampu menghasilkan aplikasi sesuai dengan kebutuhan bisnis yang telah ditetapkan. Proses dimulai dari perancangan Use Case Diagram, pembuatan desain database dengan Teknik PDM, hingga penghasilan kode program sesuai dengan permintaan pengguna. Namun, kekurangan dari penelitian-penelitian tersebut terletak pak pembenaran hasi yang diperoleh, karena mayoritas penelitian yang berkaitan dengan ChatGPT dalam bidang IT lebih difokuskan pada analisis keefektifan pembuatan kode program. Sedikit penelitian yang mendetail pada tahapan SDLC menggunakan ChatGPT. Proses penelitian dimulai dengan pembuatan Use Case Diagram, desain database menggunakan Teknik PDM, pembuatan kode program, dan pembuatan skenario pengujian dengan menggunakan ChatGPT. Hasil penelitian menunjukan bahwa ChatGPT efektif dalam pembuatan Use Case Diagram, desain database dengan Teknik PDM, dan pembuatan skenario pengujian. Namun, efektivitasnya tergantung pada spesifikasi kebutuhan yang detail dan penjelasan fitur yang lengkap dari aplikasi yang ingin dirancang untuk pembuatan skenario pengujian. Dalam pembuatan program, ChatGPT efektif dalam pembuatan program sederhana seperti login, dengan yarat intruksi yang diberikan harus mencakup database yang diguunakan dan variable kode program yang diperlukan. Namun, untuk pembuatan program yang kompleks, ChatGPT belum spenuhnya efektif. Hal ini karena ChatGPT dapat menciptakan kode program tanpa masalah, programmer harus memberikan intruksi secara sistematis.
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NAGAR, Lavisha. "What Is the Impact of ChatGPT on Education?" INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32175.

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n artificial intelligence-based chatbot, it is invented by open AI it is one of the fascinating and amazing application and that is free of cost any one can access this; people have to just login your details. It is launched in November 30 2022. It is based on the GPT-3 language model, which is trained on a massive dataset of text and code. ChatGPT can generate human-like text in response to a wide range of prompts and questions, such as writing poems, answering questions, and creating stories. It is beginning in 2018 This AI is informative and comprehensive. its work like a human body . Although human design this chatgpt but still its power is more than a manual men. It generate the significant information it gives human like responses in which any one can input and it generates the output . How this chatGpt can use in education, what kinds of issues arises in initials. (i.e., December 2022 to February 2023).I search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). ChatGPT has been used for a variety of purposes, including customer service, education, and entertainment. It has also been used to generate creative content, such as poems, stories, and scripts. ChatGPT is still under development, but it has the potential to revolutionize the way we interact with computers. The service is available 24x7 it helps in their assignments , academic exams and many more .
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Nguyen, Hong Nhung, Duy Nguyen, Luu Phuc Thinh Tran, and Thi Hoang Nguyen Tran. "Exploring English Vocabulary Learning of Vietnamese Secondary School Students with VoiceGPT Assistance." AsiaCALL Online Journal 15, no. 1 (February 28, 2024): 55–70. http://dx.doi.org/10.54855/acoj.241514.

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With the advent of AI chatbots, many teachers’ teaching practices of English as a foreign language have undergone many changes. Many of them have become accustomed to employing ChatGPT to assist their work, bringing many benefits and potential challenges that, to date, have yet to be fully tested in any aspect. Particularly, two notable research gaps involve how Vietnamese secondary school students use VoiceGPT, the Vietnamese version of ChatGPT, to assist them in learning new English words and how they perceive this support. The current case study aimed to address these gaps by employing a quasi-experimental design at Lam Son Secondary School in Ho Chi Minh City with the participation of ten sixth-grade students in two English-intensive classes. In this investigation, the teacher used the Presentation-Practice-Production teaching method to teach vocabulary to her students, who were randomly assigned into two groups with the same number of members in each group, and the data for analysis was collected from their writing samples and semi-structured interviews. The findings indicate that sixth-grade students had different ways of using VoiceGPT to help them learn English words. The participants with VoiceGPT assistance outperformed those without this A.I. support in terms of lexical performance in the writing productions on five topics surveyed. In addition, they expressed favorable attitudes toward VoiceGPT’s benefits, but some concerns were raised about login difficulties, vocabulary range, and long response time.
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Gilson, Aidan, Conrad W. Safranek, Thomas Huang, Vimig Socrates, Ling Chi, Richard Andrew Taylor, and David Chartash. "How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment." JMIR Medical Education 9 (February 8, 2023): e45312. http://dx.doi.org/10.2196/45312.

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Background Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively. Conclusions ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT’s capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.
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Hines, Jasara. "Review of "Writing in the Clouds: Inventing and Composing in Internetworked Writing Spaces by John Logie," Logie, J. (2021). Writing in the clouds: Inventing and composing in internetworked writing spaces. Parlor Press." Communication Design Quarterly 11, no. 3 (September 2023): 80–81. http://dx.doi.org/10.1145/3592367.3617935.

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In the wake of the controversy surrounding the new AI chatbot application, ChatGPT, I wonder how Logie would seek to include this new technology in his work. I ponder this because, throughout the book, Logie presents compelling evidence for why the concepts of invention, composition, and internetworked writing should be embraced and not feared. While some denounce the application and take to social media to disparage the possible negative impact on students, creativity, and composition, ChatGPT, I believe Logie would argue, would be a powerful tool we can implement to become "composers." He believes that through cloud computing services we are now more apt to collaborate, use, remix, and create rhetorical modes that extend far beyond the formulaic argument, therefore we are composers. So, Logie applies the idea of a composer as someone who is a "prosumer" (Toffler). This composer is media literate and transforms traditional rhetorical canons into multimodal compositions such as memes, Google Docs, and digital collages. However, his overarching argument is that internetworked writing tools have democratized writing through that same offering of innovative outlets. His book is arranged in a way that walks the reader through this argument.
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Plevris, Vagelis, George Papazafeiropoulos, and Alejandro Jiménez Rios. "Chatbots Put to the Test in Math and Logic Problems: A Comparison and Assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard." AI 4, no. 4 (October 24, 2023): 949–69. http://dx.doi.org/10.3390/ai4040048.

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In an age where artificial intelligence is reshaping the landscape of education and problem solving, our study unveils the secrets behind three digital wizards, ChatGPT-3.5, ChatGPT-4, and Google Bard, as they engage in a thrilling showdown of mathematical and logical prowess. We assess the ability of the chatbots to understand the given problem, employ appropriate algorithms or methods to solve it, and generate coherent responses with correct answers. We conducted our study using a set of 30 questions. These questions were carefully crafted to be clear, unambiguous, and fully described using plain text only. Each question has a unique and well-defined correct answer. The questions were divided into two sets of 15: Set A consists of “Original” problems that cannot be found online, while Set B includes “Published” problems that are readily available online, often with their solutions. Each question was presented to each chatbot three times in May 2023. We recorded and analyzed their responses, highlighting their strengths and weaknesses. Our findings indicate that chatbots can provide accurate solutions for straightforward arithmetic, algebraic expressions, and basic logic puzzles, although they may not be consistently accurate in every attempt. However, for more complex mathematical problems or advanced logic tasks, the chatbots’ answers, although they appear convincing, may not be reliable. Furthermore, consistency is a concern as chatbots often provide conflicting answers when presented with the same question multiple times. To evaluate and compare the performance of the three chatbots, we conducted a quantitative analysis by scoring their final answers based on correctness. Our results show that ChatGPT-4 performs better than ChatGPT-3.5 in both sets of questions. Bard ranks third in the original questions of Set A, trailing behind the other two chatbots. However, Bard achieves the best performance, taking first place in the published questions of Set B. This is likely due to Bard’s direct access to the internet, unlike the ChatGPT chatbots, which, due to their designs, do not have external communication capabilities.
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Spennemann, Dirk H. R. "ChatGPT and the Generation of Digitally Born “Knowledge”: How Does a Generative AI Language Model Interpret Cultural Heritage Values?" Knowledge 3, no. 3 (September 18, 2023): 480–512. http://dx.doi.org/10.3390/knowledge3030032.

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The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT-4 (released May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT wrote essays of limited length with about 50% of the stipulated word count being primarily descriptive and without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present, ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior high school assignment but fall short of what would be expected in senior school, let alone at a college or university level.
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Trepczyński, Marcin. "Religija, teologija i filozofske vještine automatiziranih programa za čavrljanje (chatbotova) pogonjenima velikim jezičnim modelima (LLM)." Disputatio philosophica 25, no. 1 (February 7, 2024): 19–36. http://dx.doi.org/10.32701/dp.25.1.2.

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In this study, I demonstrate how religion and theology can be useful for testing the performance of LLMs or LLM–powered chatbots, focusing on the measurement of philosophical skills. I present the results of testing four selected chatbots: ChatGPT, Bing, Bard, and Llama2. I utilize three examples of possible sources of inspiration from religion or theology: 1) the theory of the four senses of Scripture; 2) abstract theological statements; 3) an abstract logic formula derived from a religious text, to show that these sources are good materials for tasks that can effectively measure philosophical skills such as interpretation of a given fragment, creative deductive reasoning, and identification of ontological limitations. This approach enabled sensitive testing, revealing differences among the performances of the four chatbots. I also provide an example showing how we can create a benchmark to rate and compare such skills, using the assessment criteria and simplified scales to rate each chatbot with respect to each criterion.
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McKee, Forrest, and David Noever. "The Evolving Landscape of Cybersecurity: Red Teams, Large Language Models, and the Emergence of New AI Attack Surfaces." International Journal on Cryptography and Information Security 13, no. 1 (March 30, 2023): 1–34. http://dx.doi.org/10.5121/ijcis.2023.13101.

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This study explores cybersecurity questions using a question-and-answer format with the advanced ChatGPT model from OpenAI. Unlike previous chatbots, ChatGPT demonstrates an enhanced understanding of complex coding questions. We present thirteen coding tasks aligned with various stages of the MITRE ATT&CK framework, covering areas such as credential access and defense evasion. The experimental prompts generate keyloggers, logic bombs, obfuscated worms, and ransomware with payment fulfillment, showcasing an impressive range of functionality, including self-replication, self-modification, and evasion. Despite being a language-only model, a notable feature of ChatGPT showcases its coding approaches to produce images with obfuscated or embedded executable programming steps or links.
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Books on the topic "ChatGPT Login"

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Maximizando el Potencial del ChatGPT Gratis: Perspectivas de Davis Romero: Maximizando el Potencial del ChatGPT Gratis: Perspectivas de Davis Romero. vinaros: Davis Romero, 2024.

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Book chapters on the topic "ChatGPT Login"

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Meyer, Lars-Peter, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert, and Michael Martin. "LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT." In Informatik aktuell, 103–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-43705-3_8.

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ZusammenfassungKnowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work.Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.Zusammenfassung. Wissensgraphen (englisch Knowledge Graphs, KGs), bieten uns eine strukturierte, flexible, transparente, systemübergreifende und kollaborative Möglichkeit, unser Wissen und unsere Daten über verschiedene Bereiche der Gesellschaft und der industriellen sowie wissenschaftlichen Disziplinen hinweg zu organisieren. KGs übertreffen jede andere Form der Repräsentation in Bezug auf die Effektivität. Die Entwicklung von Wissensgraphen (englisch Knowledge Graph Engineering, KGE) erfordert jedoch fundierte Erfahrungen mit Graphstrukturen, Webtechnologien, bestehenden Modellen und Vokabularen, Regelwerken, Logik sowie Best Practices. Es erfordert auch einen erheblichen Arbeitsaufwand.In Anbetracht der Fortschritte bei großen Sprachmodellen (englisch Large Language Modells, LLMs) und ihren Schnittstellen und Anwendungen in den letzten Jahren haben wir umfassende Experimente mit ChatGPT durchgeführt, um sein Potenzial zur Unterstützung von KGE zu untersuchen. In diesem Artikel stellen wir eine Auswahl dieser Experimente und ihre Ergebnisse vor, um zu zeigen, wie ChatGPT uns bei der Entwicklung und Verwaltung von KGs unterstützen kann.
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Bhattacharya, Shounak, Abhishek Kushwaha, Sharmila K. Banu, and B. K. Tripathy. "Customizing Backend Logic Using a Chatbot." In Artificial Intelligence and Sustainable Computing, 567–77. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1653-3_43.

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Heitzinger, Clemens, and Stefan Woltran. "A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations." In Introduction to Digital Humanism, 135–49. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45304-5_9.

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AbstractThis chapter gives an overview of the most important methods in artificial intelligence (AI). The methods of symbolic AI are rooted in logic, and finding possible solutions by search is a central aspect. The main challenge is the combinatorial explosion in search, but the focus on the satisfiability problem of propositional logic (SAT) since the 1990s and the accompanying algorithmic improvements have made it possible to solve problems on the scale needed in industrial applications. In machine learning (ML), self-learning algorithms extract information from data and represent the solutions in convenient forms. ML broadly consists of supervised learning, unsupervised learning, and reinforcement learning. Successes in the 2010s and early 2020s such as solving Go, chess, and many computer games as well as large language models such as ChatGPT are due to huge computational resources and algorithmic advances in ML. Finally, we reflect on current developments and draw conclusions.
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Filippi, Mauro, Salvatore Di Dio, Domenico Schillaci, Stefano Malorni, Angelo Scuderi, and Sabrina Guzzo. "Conversation Design for Raising Awareness on the Responsible Use of the Internet." In Springer Series in Design and Innovation, 773–82. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49811-4_74.

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AbstractThe rapid process of digital transformation and servitization experienced on a global scale in recent decades, further accelerated by the pandemic crisis, has radically altered the life experience of societies, with technological innovations leading to the emergence of new ethical and legal challenges. The issue of digital literacy and the acquisition of basic skills for responsible use of the internet has become one of the most urgent prerogatives in international government programs to address psychosocial phenomena such as those related to online grooming, cyberbullying, cybersuicide, cyber racing, or online scamming. The proposed work introduces the use of participatory game design as an empowerment tool for young students. The case study of the NetWalking project, developed by an interdisciplinary team of practitioners and researchers in several secondary schools in the city of Palermo (Italy), describes the experimentation of co-design activities of an edugame with a conversational interface (chatbot), which exploits the logic of nonlinear storytelling to actively engage students in playful learning activities, contributing to the development of hard skills (STEM) and soft skills (self-assessment, teamwork and information management).
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"Chapter 2: Conditional Logic, Loops, and Functions." In Python 3 using ChatGPT/GPT4, 31–56. De Gruyter, 2023. http://dx.doi.org/10.1515/9781501518737-003.

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PhaniRaghava, B., and S. Ashok Kumar. "An Improved Chatbot for Predicting Disease and Medicines Using Natural Language Processing with Fuzzy Logic." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220035.

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The aim is to create an artificial conversation entity(chatbot) using python to predict disease and medicine for healthcare treatments. Two algorithms fuzzy support vector machine algorithms are compared with Decision tree algorithm sample size taken 28. G power of 81% and sample size is calculated using the G power tool. Performances of the score model validated test set accuracy with 95% confidence interval for fuzzy support vector machine algorithm with different sub-samples has 91.60% accuracy comparing with Decision tree which has 87.90% accuracy.Independent Sample T-test a significance difference in accuracy and loss is observed p<0.005.From the results it is concluded that proposed algorithm Fuzzy support vector machine will produce better results than the existing algorithm.
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Maurya, Akash, Pooja Shetty, Ganesh Yadava, and Leena Ladge. "College Election System using Facial Authentication." In Artificial Intelligence and Communication Technologies, 501–13. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-48.

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Voting is commonly related to politics and is often ended with exploitation. Manual voting may lead to malpractices sometimes, so there is a need to implement a secured online voting system. The voting system that we propose is a web portal which is designed using the MERN stack and the authentication of the voter is done using facial recognition. For this, the Eigen-face algorithm is used. The user will have to register and login into the portal. After that, the users will upload their respective documents which will be verified by the admin. For the voter’s assistance, the portal has several features like slot booking for the voting day, reminders for the voters, viewing of the candidates standing for the elections and a FAQ chatbot. The candidates can do online campaigning, set online rallies and build their profiles for the voters to see. The whole website is secured using a JWT token which ensures that every request made to the website is made by a legitimate and admin verified user. The voter is verified using facial authentication before he is allowed to vote. Our system uses the Eigen-Face algorithm with an accuracy of around 87%.
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Conference papers on the topic "ChatGPT Login"

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Koziolek, Heiko, Sten Gruener, and Virendra Ashiwal. "ChatGPT for PLC/DCS Control Logic Generation." In 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2023. http://dx.doi.org/10.1109/etfa54631.2023.10275411.

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Liu, Henry Muyang, and Jenny Do. "Originlens: A Real-Time AI-Generated Text and Plagiarism Detection using Deep Learning and Augmented Reality." In 5th International Conference on Artificial Intelligence and Big Data (AIBD 2024). Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140405.

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ChatGPT has integrated itself into the academic space in an unprecedented timeframe, as the promise of hours of work done in seconds can outweigh the sense of honor and logic. This project determines whether a student is cheating or not by reducing the presence of human decision-making while simultaneously acting as a deterrent for future usage of AI technology. Utilizing databases such as Kaggle, we can procure several samples of human writingin conjunction with ChatGPT's API to generate artificial intelligence instances, which are then stored for usage in machine learning algorithms. Employing powerful Python libraries such as Sklearn and NLTK, we can utilize natural learning processing, the ability for computers to understand human writing, to yield an algorithm that can predict with approximately 96% certainty. The result is a probability ratio, with one side displaying the percentage chance of the sample being human-written, whereas the other displays the likelihood of AI-generated instances. Furthermore, innovation lies in integrating this algorithm with wearable augmented reality technology, allowing usersto efficiently scan and assess text elements. This approach amalgamates and helps reduce the delay between text input and response, empowering users to contribute to the decision-making process in identifying academic dishonesty without any loss in efficiency. The result that is shown displays pieces of information to the user that can all play a large role when determining the possibility of cheating, granting the user a role in making the decision along with simple scanning of each text element.
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De la Cruz Martínez, Gustavo, Ana Libia Eslava Cervantes, and Selene Marisol Martínez Ramírez. "ANALYSIS OF SOLUTIONS OF CHATGPT TO LOGIC PROBLEMS BASED ON CRITICAL THINKING." In 16th International Conference on Education and New Learning Technologies. IATED, 2024. http://dx.doi.org/10.21125/edulearn.2024.2525.

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Daniel Simić, Srđan, Toni Starčić, Aldo Ferlatti, Darko Etinger, and Nikola Tanković. "A Business Process Model Driven Chatbot Architecture." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100956.

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A successful business operates on many interacting processes to meet the business goals; thus, organizing these processes in a structured business model is essential. Business process modeling notation (BPMN) is a widely used business modeling technique based on simple business and logic specifications understandable not only by domain experts but also to the broader audience. Although it provides support for the process execution, many BPMN models serve only for documentation purposes. This paper positions the lack of coordination between the process execution and the frontend interface as drivers of slow BPMN adoption. To close the gap between execution and interface, we present a system architecture that provides external task automation and interactive human task completion using a chatbot structure powered by natural language processing (NLP) and BPMN. We have evaluated the performance of the proposed architecture on the process of internship applications for the Faculty of Informatics in Pula, Croatia.
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Troussas, Christos, Akrivi Krouska, Phivos Mylonas, and Cleo Sgouropoulou. "Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling." In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). IEEE, 2023. http://dx.doi.org/10.1109/smap59435.2023.10255169.

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Short, Ada-Rhodes. "The Generation of Novel Art Using Collaborative ML Models." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-116825.

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Abstract:
Abstract In the past year there have been paradigm shifting developments in the feasibility and availability of machine learning tools for the creation of visual and textual works. Two of the most prominent examples of this has been Large-Language models like chatGPT and methods like stable diffusion for generating art from text prompts. Both visual and language arts are often thought of as human activities, so exploring the possibilities and limitations of these tools is important for both understanding automation and improving our understanding of human cognition. In this paper I use a Large-Language Model and stable diffusion in tandem to develop an understanding of what new possibilities exist in computational cognition and design automation through their application. While no single model can recreate the complexities of a biological brain at this time, they can be thought of as analogous to individual neurological structures. For example, a Large-Language Model that is able to reason out and communicate the solutions to simple logic puzzles could recreate some of the functionality of the frontal lobe of the cerebrum. Additionally approaches like stable diffusion can recreate some of the functions of the occipital and parietal lobes. By combining them more complex behaviors and capabilities can be achieved than are possible from the individual parts. This work is in its early stages but is foundational for later developments in design automation, robotics, and computational cognition.
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