AI Chatbots: Threat or Opportunity?

Topic Information

Dear Colleagues,

ChatGPT, based on GPT-3, was launched by OpenAI in November 2022. On their website it is described as ‘a language model … designed to respond to text-based queries and generate natural language responses. It is part of the broader field of artificial intelligence known as natural language processing (NLP), which seeks to teach computers to understand and interpret human language’. More significantly, it is stated that ‘One of the main applications of ChatGPT is in chatbots, where it can be used to provide automated customer service, answer FAQs, or even engage in more free-flowing conversations with users. However, it can also be used in other NLP applications such as text summarization, language translation, and content creation. Overall, ChatGPT represents a significant advancement in the field of NLP and has the potential to revolutionize the way we interact with computers and digital systems’.

These claims, although containing relatively innocuous terms, have been seen by many as potentially ominous and with far-reaching ramifications. Teachers, already facing the issues of cut-and-paste-off-the-internet plagiarism, ghost-writing, and contract cheating, foresaw that AI chatbots such as ChatGPT, Bard, and Bing, would offer students new and more powerful opportunities to produce work for assessment. For some this was not a problem, but for others it appeared to be the beginning of the end for anything other than in-person assessments, including hand-written exams and vivas.

People began to experiment with ChatGPT, using it to produce computer code, speeches, and academic papers. In some cases, users expressed their astonishment at the high quality of the outputs, but others were far more skeptical. In the meantime, OpenAI released GPT-4, which is now incorporated into ChatGPT Plus. It is expected that GPT-5 will be available later this year, on top of which, autonomous AI agents such as Auto-GPT and Agent-GPT are now available. These developments, and others in the general area of AI, have led to calls for a pause in such developments, although others have expressed doubts that this will have any impact.

The issues raised by AI chatbots such as ChatGPT impact upon a range of practices and disciplines, as well as many facets of our everyday lives and interactions. Hence, this invitation to submit work comes from editors associated with a wide variety of MDPI journals, encompassing a range of inter-related perspectives on the topic. We are keen to receive submissions relating to the technologies behind the advance in these AI chatbots, and also with regard to the wider implications of their use in social, technical, and educational contexts.

We are open to all manner of submissions, but to give some indication of the aspects of key interest we list the following questions and issues.

• The development of AI chatbots has been claimed to herald a new era, offering significant advances in the incorporation of technology into people’s lives and interactions. Is this likely to be the case, and if so, where are these impacts going to be the most pervasive and effective?
• Is it possible to strike a balance regarding the impact of these technologies so that any potential harms are minimized, while potential benefits are maximized and shared?
• How should educators respond to the challenge of AI chatbots? Should they welcome this technology and re-orient teaching and learning strategies around it, or seek to safeguard traditional practices from what is seen as a major threat?
• There is a growing body of evidence that the design and implementation of many AI applications, i.e., algorithms, incorporate bias and prejudice. How can this be countered and corrected?
• How can publishers and editors recognize the difference between manuscripts that have been written by a chatbot and "genuine" articles written by researchers? Is training to recognize the difference required? If so, who could offer such training?
• How can the academic world and the wider public be protected against the creation of "alternative facts" by AI? Should researchers be required to submit their data with manuscripts to show that the data are authentic? What is the role of ethics committees in protecting the integrity of research?
• Can the technology underlying AI chatbots be enhanced to guard against misuse and vulnerabilities?
• Novel models and algorithms for using AI chatbots in cognitive computing;
• Techniques for training and optimizing AI chatbots for cognitive computing tasks;
• Evaluation methods for assessing the performance of AI chatbot-based - cognitive computing systems;
• Case studies and experiences in developing and deploying AI chatbot-based cognitive computing systems in real-world scenarios;
• Social and ethical issues related to the use of AI chatbots for cognitive computing.

The potential impact of these AI chatbots on the topics covered by journals is twofold: on the one hand, there is a need for research on the technological bases underlying AI chatbots, including the algorithmic aspects behind the AI; on the other hand, there are many aspects related to the support and assistance that these AI chatbots can provide to algorithm designers, code developers and others operating in the many fields and practices encompassed by this collection of journals.

Prof. Dr. Antony Bryant, Editor-in-Chief of Informatics
Prof. Dr. Roberto Montemanni, Section Editor-in-Chief of Algorithms
Prof. Dr. Min Chen, Editor-in-Chief of BDCC
Prof. Dr. Paolo Bellavista, Section Editor-in-Chief of Future Internet
Prof. Dr. Kenji Suzuki, Editor-in-Chief of AI
Prof. Dr. Jeanine Treffers-Daller, Editor-in-Chief of Languages
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	17.6 Days	CHF 1600
Algorithms algorithms	1.8	4.1	2008	15 Days	CHF 1600
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800
Future Internet futureinternet	2.8	7.1	2009	13.1 Days	CHF 1600
Informatics informatics	3.4	6.6	2014	33 Days	CHF 1800
Information information	2.4	6.9	2010	14.9 Days	CHF 1600
Languages languages	0.9	1.4	2016	49.6 Days	CHF 1400
Publications publications	4.6	6.5	2013	35.8 Days	CHF 1400

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Published Papers (13 papers)

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18 pages, 2039 KiB  

Open AccessArticle

AI Language Models: An Opportunity to Enhance Language Learning

by Yan Cong

Informatics 2024, 11(3), 49; https://doi.org/10.3390/informatics11030049 - 19 Jul 2024

Viewed by 1703

Abstract

AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In [...] Read more.

AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computational analysis in this work is hypothesis-driven. The current work predicts how similarities should be distributed in a second language learning setting. The results suggest that similarity metrics are informative of writing proficiency assessment and interlanguage development. Statistically significant effects were found across multiple AI models. Most of the metrics could distinguish language learners’ proficiency levels. Significant correlations were also found between similarity metrics and learners’ writing test scores provided by human experts in the domain. However, not all such effects were strong or interpretable. Several results could not be consistently explained under the proposed second language learning hypotheses. Overall, the current investigation indicates that with careful configuration and systematic metrics design, AI language models can be promising tools in advancing language education. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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22 pages, 353 KiB  

Open AccessArticle

GPTs or Grim Position Threats? The Potential Impacts of Large Language Models on Non-Managerial Jobs and Certifications in Cybersecurity

by Raza Nowrozy

Informatics 2024, 11(3), 45; https://doi.org/10.3390/informatics11030045 - 11 Jul 2024

Viewed by 1262

Abstract

ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. This study assesses ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. It also explores its [...] Read more.

ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. This study assesses ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. It also explores its potential to pass top cybersecurity certification exams. Findings reveal ChatGPT’s promise to streamline some jobs, especially those requiring memorization. Moreover, this paper highlights ChatGPT’s challenges and limitations, such as ethical implications, LLM limitations, and Artificial Intelligence (AI) security. The study suggests that LLMs like ChatGPT could transform the cybersecurity landscape, causing job losses, skill obsolescence, labor market shifts, and mixed socioeconomic impacts. A shift in focus from memorization to critical thinking, and collaboration between LLM developers and cybersecurity professionals, is recommended. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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29 pages, 860 KiB  

Open AccessArticle

ChatGPT Code Detection: Techniques for Uncovering the Source of Code

by Marc Oedingen, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer and Wolfgang Konen

AI 2024, 5(3), 1066-1094; https://doi.org/10.3390/ai5030053 - 2 Jul 2024

Viewed by 4690

Abstract

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence [...] Read more.

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of $98\%$. For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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14 pages, 611 KiB  

Open AccessArticle

Analysing the Impact of Generative AI in Arts Education: A Cross-Disciplinary Perspective of Educators and Students in Higher Education

by Sara Sáez-Velasco, Mario Alaguero-Rodríguez, Vanesa Delgado-Benito and Sonia Rodríguez-Cano

Informatics 2024, 11(2), 37; https://doi.org/10.3390/informatics11020037 - 3 Jun 2024

Cited by 1 | Viewed by 4480

Abstract

Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators [...] Read more.

Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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20 pages, 3041 KiB  

Open AccessArticle

Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis

by Johannes Pernaa, Topias Ikävalko, Aleksi Takala, Emmi Vuorio, Reija Pesonen and Outi Haatainen

Informatics 2024, 11(2), 20; https://doi.org/10.3390/informatics11020020 - 18 Apr 2024

Cited by 2 | Viewed by 2264

Abstract

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into [...] Read more.

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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14 pages, 960 KiB  

Open AccessArticle

ChatGPT in Education: Empowering Educators through Methods for Recognition and Assessment

by Joost C. F. de Winter, Dimitra Dodou and Arno H. A. Stienen

Informatics 2023, 10(4), 87; https://doi.org/10.3390/informatics10040087 - 29 Nov 2023

Cited by 14 | Viewed by 7134

Abstract

ChatGPT is widely used among students, a situation that challenges educators. The current paper presents two strategies that do not push educators into a defensive role but can empower them. Firstly, we show, based on statistical analysis, that ChatGPT use can be recognized [...] Read more.

ChatGPT is widely used among students, a situation that challenges educators. The current paper presents two strategies that do not push educators into a defensive role but can empower them. Firstly, we show, based on statistical analysis, that ChatGPT use can be recognized from certain keywords such as ‘delves’ and ‘crucial’. This insight allows educators to detect ChatGPT-assisted work more effectively. Secondly, we illustrate that ChatGPT can be used to assess texts written by students. The latter topic was presented in two interactive workshops provided to educators and educational specialists. The results of the workshops, where prompts were tested live, indicated that ChatGPT, provided a targeted prompt is used, is good at recognizing errors in texts but not consistent in grading. Ethical and copyright concerns were raised as well in the workshops. In conclusion, the methods presented in this paper may help fortify the teaching methods of educators. The computer scripts that we used for live prompting are available and enable educators to give similar workshops. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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16 pages, 335 KiB  

Open AccessReview

AI Chatbots in Digital Mental Health

by Luke Balcombe

Informatics 2023, 10(4), 82; https://doi.org/10.3390/informatics10040082 - 27 Oct 2023

Cited by 13 | Viewed by 19540

Abstract

Artificial intelligence (AI) chatbots have gained prominence since 2022. Powered by big data, natural language processing (NLP) and machine learning (ML) algorithms, they offer the potential to expand capabilities, improve productivity and provide guidance and support in various domains. Human–Artificial Intelligence (HAI) is [...] Read more.

Artificial intelligence (AI) chatbots have gained prominence since 2022. Powered by big data, natural language processing (NLP) and machine learning (ML) algorithms, they offer the potential to expand capabilities, improve productivity and provide guidance and support in various domains. Human–Artificial Intelligence (HAI) is proposed to help with the integration of human values, empathy and ethical considerations into AI in order to address the limitations of AI chatbots and enhance their effectiveness. Mental health is a critical global concern, with a substantial impact on individuals, communities and economies. Digital mental health solutions, leveraging AI and ML, have emerged to address the challenges of access, stigma and cost in mental health care. Despite their potential, ethical and legal implications surrounding these technologies remain uncertain. This narrative literature review explores the potential of AI chatbots to revolutionize digital mental health while emphasizing the need for ethical, responsible and trustworthy AI algorithms. The review is guided by three key research questions: the impact of AI chatbots on technology integration, the balance between benefits and harms, and the mitigation of bias and prejudice in AI applications. Methodologically, the review involves extensive database and search engine searches, utilizing keywords related to AI chatbots and digital mental health. Peer-reviewed journal articles and media sources were purposively selected to address the research questions, resulting in a comprehensive analysis of the current state of knowledge on this evolving topic. In conclusion, AI chatbots hold promise in transforming digital mental health but must navigate complex ethical and practical challenges. The integration of HAI principles, responsible regulation and scoping reviews are crucial to maximizing their benefits while minimizing potential risks. Collaborative approaches and modern educational solutions may enhance responsible use and mitigate biases in AI applications, ensuring a more inclusive and effective digital mental health landscape. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

21 pages, 1002 KiB  

Open AccessArticle

Chatbots Put to the Test in Math and Logic Problems: A Comparison and Assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard

by Vagelis Plevris, George Papazafeiropoulos and Alejandro Jiménez Rios

AI 2023, 4(4), 949-969; https://doi.org/10.3390/ai4040048 - 24 Oct 2023

Cited by 22 | Viewed by 9216

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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26 pages, 4052 KiB  

Open AccessEditor’s ChoiceArticle

Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities

by Edisa Lozić and Benjamin Štular

Future Internet 2023, 15(10), 336; https://doi.org/10.3390/fi15100336 - 13 Oct 2023

Cited by 18 | Viewed by 7850

Abstract

Historically, mastery of writing was deemed essential to human progress. However, recent advances in generative AI have marked an inflection point in this narrative, including for scientific writing. This article provides a comprehensive analysis of the capabilities and limitations of six AI chatbots [...] Read more.

Historically, mastery of writing was deemed essential to human progress. However, recent advances in generative AI have marked an inflection point in this narrative, including for scientific writing. This article provides a comprehensive analysis of the capabilities and limitations of six AI chatbots in scholarly writing in the humanities and archaeology. The methodology was based on tagging AI-generated content for quantitative accuracy and qualitative precision by human experts. Quantitative accuracy assessed the factual correctness in a manner similar to grading students, while qualitative precision gauged the scientific contribution similar to reviewing a scientific article. In the quantitative test, ChatGPT-4 scored near the passing grade (−5) whereas ChatGPT-3.5 (−18), Bing (−21) and Bard (−31) were not far behind. Claude 2 (−75) and Aria (−80) scored much lower. In the qualitative test, all AI chatbots, but especially ChatGPT-4, demonstrated proficiency in recombining existing knowledge, but all failed to generate original scientific content. As a side note, our results suggest that with ChatGPT-4, the size of large language models has reached a plateau. Furthermore, this paper underscores the intricate and recursive nature of human research. This process of transforming raw data into refined knowledge is computationally irreducible, highlighting the challenges AI chatbots face in emulating human originality in scientific writing. Our results apply to the state of affairs in the third quarter of 2023. In conclusion, while large language models have revolutionised content generation, their ability to produce original scientific contributions in the humanities remains limited. We expect this to change in the near future as current large language model-based AI chatbots evolve into large language model-powered software. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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16 pages, 872 KiB  

Open AccessArticle

Qualitative Research Methods for Large Language Models: Conducting Semi-Structured Interviews with ChatGPT and BARD on Computer Science Education

by Andreas Dengel, Rupert Gehrlein, David Fernes, Sebastian Görlich, Jonas Maurer, Hai Hoang Pham, Gabriel Großmann and Niklas Dietrich genannt Eisermann

Informatics 2023, 10(4), 78; https://doi.org/10.3390/informatics10040078 - 12 Oct 2023

Cited by 10 | Viewed by 7501

Abstract

In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including [...] Read more.

In the current era of artificial intelligence, large language models such as ChatGPT and BARD are being increasingly used for various applications, such as language translation, text generation, and human-like conversation. The fact that these models consist of large amounts of data, including many different opinions and perspectives, could introduce the possibility of a new qualitative research approach: Due to the probabilistic character of their answers, “interviewing” these large language models could give insights into public opinions in a way that otherwise only interviews with large groups of subjects could deliver. However, it is not yet clear if qualitative content analysis research methods can be applied to interviews with these models. Evaluating the applicability of qualitative research methods to interviews with large language models could foster our understanding of their abilities and limitations. In this paper, we examine the applicability of qualitative content analysis research methods to interviews with ChatGPT in English, ChatGPT in German, and BARD in English on the relevance of computer science in K-12 education, which was used as an exemplary topic. We found that the answers produced by these models strongly depended on the provided context, and the same model could produce heavily differing results for the same questions. From these results and the insights throughout the process, we formulated guidelines for conducting and analyzing interviews with large language models. Our findings suggest that qualitative content analysis research methods can indeed be applied to interviews with large language models, but with careful consideration of contextual factors that may affect the responses produced by these models. The guidelines we provide can aid researchers and practitioners in conducting more nuanced and insightful interviews with large language models. From an overall view of our results, we generally do not recommend using interviews with large language models for research purposes, due to their highly unpredictable results. However, we suggest using these models as exploration tools for gaining different perspectives on research topics and for testing interview guidelines before conducting real-world interviews. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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6 pages, 500 KiB  

Open AccessCommunication

Children of AI: A Protocol for Managing the Born-Digital Ephemera Spawned by Generative AI Language Models

by Dirk H. R. Spennemann

Publications 2023, 11(3), 45; https://doi.org/10.3390/publications11030045 - 21 Sep 2023

Cited by 3 | Viewed by 2194

Abstract

The recent public release of the generative AI language model ChatGPT has captured the public imagination and has resulted in a rapid uptake and widespread experimentation by the general public and academia alike. The number of academic publications focusing on the capabilities as [...] Read more.

The recent public release of the generative AI language model ChatGPT has captured the public imagination and has resulted in a rapid uptake and widespread experimentation by the general public and academia alike. The number of academic publications focusing on the capabilities as well as practical and ethical implications of generative AI has been growing exponentially. One of the concerns with this unprecedented growth in scholarship related to generative AI, in particular, ChatGPT, is that, in most cases, the raw data, which is the text of the original ‘conversations,’ have not been made available to the audience of the papers and thus cannot be drawn on to assess the veracity of the arguments made and the conclusions drawn therefrom. This paper provides a protocol for the documentation and archiving of these raw data. Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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18 pages, 3680 KiB  

Open AccessArticle

Application of ChatGPT-Based Digital Human in Animation Creation

by Chong Lan, Yongsheng Wang, Chengze Wang, Shirong Song and Zheng Gong

Future Internet 2023, 15(9), 300; https://doi.org/10.3390/fi15090300 - 2 Sep 2023

Cited by 11 | Viewed by 5457

Abstract

Traditional 3D animation creation involves a process of motion acquisition, dubbing, and mouth movement data binding for each character. To streamline animation creation, we propose combining artificial intelligence (AI) with a motion capture system. This integration aims to reduce the time, workload, and [...] Read more.

Traditional 3D animation creation involves a process of motion acquisition, dubbing, and mouth movement data binding for each character. To streamline animation creation, we propose combining artificial intelligence (AI) with a motion capture system. This integration aims to reduce the time, workload, and cost associated with animation creation. By utilizing AI and natural language processing, the characters can engage in independent learning, generating their own responses and interactions, thus moving away from the traditional method of creating digital characters with pre-defined behaviors. In this paper, we present an approach that employs a digital person’s animation environment. We utilized Unity plug-ins to drive the character’s mouth Blendshape, synchronize the character’s voice and mouth movements in Unity, and connect the digital person to an AI system. This integration enables AI-driven language interactions within animation production. Through experimentation, we evaluated the correctness of the natural language interaction of the digital human in the animated scene, the real-time synchronization of mouth movements, the potential for singularity in guiding users during digital human animation creation, and its ability to guide user interactions through its own thought process. Full article

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8 pages, 231 KiB  

Open AccessEditorial

AI Chatbots: Threat or Opportunity?

by Antony Bryant

Informatics 2023, 10(2), 49; https://doi.org/10.3390/informatics10020049 - 12 Jun 2023

Cited by 6 | Viewed by 4240

Abstract

In November 2022, OpenAI launched ChatGPT, an AI chatbot that gained over 100 million users by February 2023 [...] Full article

(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)

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