Artificial Intelligence Developers Research Articles

Exploring dental professionals’ outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan

ObjectiveThe purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a focus on the possible effects on dental education and clinical practice.MethodologyIn a mix-method cross-sectional quantitative and quantitative study conducted between June 1st and August 30th, 2023, the perspectives of faculty members from a public sector dental college in Pakistan regarding the function of AI were explored. This study used qualitative as well as quantitative techniques to analyse faculty’s viewpoints on the subject. The sample size was comprised of twenty-three faculty members. The quantitative data was analysed using descriptive statistics, while the qualitative data was analysed using theme analysis.ResultsPosition-specific differences in faculty familiarity underscore the value of individualized instruction. Surprisingly few had ever come across AI concepts in their professional lives. Nevertheless, many acknowledged that AI had the potential to improve patient outcomes. The majority thought AI would improve dentistry education. Participants suggested a few dental specialties where AI could be useful.ConclusionThe study emphasizes the significance of addressing in dental professionals’ knowledge gaps about AI. The promise of AI in dentistry calls for specialized training and teamwork between academic institutions and AI developers. Graduates of dentistry programs who use AI are better prepared to navigate shifting environments. The study highlights the positive effects of AI and the value of faculty involvement in maximizing its potential for better dental education and practice.

Pattern Recognition of EFL University Students’ Online Behaviors through Data Science: Any Investment on English Language Components or Skills?

Artificial intelligence (AI) has played a crucial role in many fields of study, and its merits and demerits have been investigated by related scholars. However, it seems a somewhat gray area when implementing AI in teaching and learning. This could be mainly because AI developers know little about learning varied sciences and they also lack pedagogical knowledge for implementing AI in teaching (Luckin & Cukurova, 2019). Therefore, the current research aimed at gathering data regarding the online behaviors of 21 Iranian EFL university students through installing an application on their mobile phones for a period of eight days. Afterwards, the collected data were analyzed through Data Science methods and varied patterns of behavior were recognized. The results exhibited that messaging and social media applications are two great parts of the students’ life. Also, there existed some common patterns regarding the applications students owned and the amount of time they spent on them. As a small case in point, the results of the study cannot be generalized; however, the outcomes highlight the proof that in an EFL context, serious steps need to be adopted by course designers and stakeholders in order to cultivate a greater and purposeful use of online learning applications, websites etc. among EFL university students so as to enhance the teaching-learning process. This gains prominence because any AI-mediated learning approach is believed to enhance students’ second/foreign language motivation as well as self-regulation in learning. AI is actually promising a revolution in the realm of language education.

An Interview System Using AI Technology

The rapid advancement of artificial intelligence (AI) has led to innovative applications across various industries, and one such application is the development of an AI-based interview system. This system aims to enhance and streamline the traditional interview process by leveraging AI technologies to assess and interact with candidates. The AI- based interview system incorporates natural language processing (NLP), machine learning (ML), and sentiment analysis algorithms to analyze both verbal and non-verbal cues during interviews. By employing AI, the system can assess a candidate's communication skills, problem-solving abilities, and cultural fit within the organization. The system's machine learning algorithms continuously learn and improve over time, enhancing their ability to accurately evaluate candidates and predict their potential success within the company. The benefits of an AI-based interview system include increased efficiency in the hiring process, reduction in human bias, and the ability to handle a large number of interviews simultaneously. Furthermore, the system can provide data-driven insights to recruiters, aiding in evidence-based decision-making. While the development and implementation of an AI-based interview system pose ethical considerations and challenges related to privacy and bias, ongoing research and ethical guidelines are essential to address these concerns and ensure fair and transparent use of the technology. As technology continues to evolve, further research and collaboration between AI developers and human resource professionals are crucial to harness the full potential of this innovative tool in the hiring process.

The Role of Artificial Intelligence in Creation of Future Education: Possibilities and Challenges

Artificial intelligence has significant potentials in revolutionizing education by enhancing teaching methods, personalizing learning experiences, and improving educational outcomes. The aim of the research is to specify the role of AI in creation of future education and to outline the potentials and challenges artificial intelligence can bring within the educational process. To answer the research questions the case study on the role of AI in the creation of future education was conducted. The methods included structured interviews among institutions administrators, faculty members, students, and AI developers; survey among students and faculty to collect quantitative data on their use of and attitudes towards AI technologies; classroom observations. Over 50 recent scientific works were selected to evaluate the role of artificial intelligence in education and to conduct the comparative educational analysis. The research was conducted among 56 participants, in particularly 9 institutions administrators, 19 faculty members, 24 students, and 4 artificial intelligence developers. The results showed that artificial intelligence integrated in the educational process are used to design simulation-based activities, organize personalized learning, form specific skills among students, and automate the administrative process. artificial intelligence leads to enhancement of learning outcomes among students, increased efficiency, and greater accessibility of education, during war in particularly when the educational process can be disrupted. Using artificial intelligence in education presents several challenges that need to be addressed for its effective implementation. Also, it was found that efficient using artificial intelligence tools within the educational process will lead to the creation of positive artificial intelligence based learning landscape at the institutions of higher education which is characterized by efficiency, interactivity, inclusivity, innovation, and accessibility. The results of research can be implied by educational institutions to improve the educational process and by EdTech companies to develop educational tools and platforms.

Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI

AbstractMuch has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at times with a multitude of diverse requirements; or at other times, no specification at all. In philosophical circles, there is doubt that the concept of trust should be applied at all to technologies rather than their human creators. Nevertheless, people continue to intuitively reason about trust in technologies in their everyday language. This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews. We found that different accounts of trust (rational, affective, credentialist, norms based, relational) served as the basis for individual granting of trust in technologies and operators. Ultimately, the most significant requirement for user trust and assessment of trustworthiness was the accountability of AI developers for the outputs of AI systems, hinging on the identification of accountable moral agents and perceived value alignment between the user and developer’s interests.

The perspectives of eye care professionals on the integration of artificial intelligence in eye care practices: A systematic review

Artificial intelligence (AI) technology has recently been integrated into the health-care industry, including in optometry and ophthalmology. This systematic review assessed the opinions (i.e., perspectives, concerns, and degrees of acceptance) of eye care professionals regarding AI integration into eye care practices. The literature search was conducted using the PubMed and MEDLINE databases. A total of 780 related articles were identified. Among these articles, 304 duplicates were removed, 450 articles were excluded after reviewing the abstract, and 18 articles were excluded after reviewing the full text as these articles were not relevant and/or did not report surveys. The remaining eight included studies were assessed accordingly. Most ophthalmologists and optometrists had a positive perception toward incorporating AI into eye care practices, and these professionals shared that AI would effectively enhance clinical eye care practices. However, certain eye care professionals were concerned about the diagnostic accuracy of AI, the high implementation costs, privacy issues, and the quality of AI-integrated patient care. Several eye care professionals also expressed concerns that AI technology could eventually replace some of their major responsibilities in the practice, suggesting that stakeholders should essentially address these concerns and ensure that AI integration in eye care practices is implemented thoughtfully and ethically to maximize its benefits while preserving the quality of patient care. Nonetheless, this systematic review highlighted the predominantly positive attitude among eye care professionals toward AI integration into eye care practices, warranting further research and collaboration between AI developers and eye care professionals to effectively address the current challenges.

Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4.

This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.

Collaborative innovations in Artificial Intelligence (AI): Partnering with leading U.S. tech firms to combat human trafficking

This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.

Perspectives of Oncologists on the Ethical Implications of Using Artificial Intelligence for Cancer Care

Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. To evaluate oncologists' views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

The ethical wisdom of AI developers

AbstractThis paper explores ethical wisdom in the artificial intelligence (AI) developer community. Despite robust literature about the need for virtue ethics approaches in AI development, little research has directly engaged with the developer community about their progress in this regard. We have thus conducted semi-structured interviews with a worldwide cohort of 40 developers, which focused on their awareness of ethics issues, how they navigate ethical challenges, and the barriers they encounter in developing ethical wisdom. We find developers are largely aware of the ethical territories they must navigate and the moral dilemmas they personally encounter, but they face limited and inconsistent resources for ethical guidance or training. Furthermore, there are significant barriers inhibiting the development of ethical wisdom in the AI developer community, including the industry’s fixation on innovation, the narrow scope of technical practice, limited provisions for reflection and dialogue, and incentive structures that prioritize profits and prestige. The paper concludes by emphasizing the need to address the gap in domain-specific ethical skill and provides recommendations for organizations, educators, and the AI developer community.

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis

ObjectivesTo evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding...

TachAId-An interactive tool supporting the design of human-centered AI solutions.

In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present "technical assistance concerning human-centered AI development" (tachAId), an interactive advisory tool which comprehensively guides AI developers and decision makers in navigating the machine learning lifecycle with a focus on human-centered design. tachAId motivates and presents concrete technical advice to ensure human-centeredness across the phases of AI development. The tool's effectiveness is evaluated through a catalog of criteria for human-centered AI in the form of relevant challenges and goals, derived from existing methodologies and guidelines. Lastly, tachAId and one other comparable advisory tool were examined to determine their adherence to these criteria in order to provide an overview of the human-centered aspects covered by these tools and to allow interested parties to quickly assess whether the tools meet their needs.

Artificial Intelligence in nursing: trustworthy or reliable?

Background: Trustworthiness in Artificial Intelligence (AI) innovation is a priority for governments, researchers and clinicians; however, clinicians have highlighted trust and confidence as barriers to their acceptance of AI within a clinical application. While there is a call to design and develop AI that is considered trustworthy, AI still lacks the emotional capability to facilitate the reciprocal nature of trust. Aim: This paper aims to highlight and discuss the enigma of seeking or expecting trust attributes from a machine and, secondly, reframe the interpretation of trustworthiness for AI through evaluating its reliability and validity as consistent with the use of other clinical instruments. Results: AI interventions should be described in terms of competence, reliability and validity as expected of other clinical tools where quality and safety are a priority. Nurses should be presented with treatment recommendations that describe the validity and confidence of prediction with the final decision for care made by nurses. Future research should be framed to better understand how AI is used to deliver care. Finally, there is a responsibility for developers and researchers to influence the conversation about AI and its power towards improving outcomes. Conclusion: The sole focus on demonstrating trust rather than the business-as-usual requirement for reliability and validity attributes during implementation phases may result in negative experiences for nurses and clinical users. Implications for practice: This research will have significant implications for the way in which future nursing is practised. As AI-based systems become a part of routine practice, nurses will be faced with an increasing number of interventions that require complex trust systems to operate. For any AI researchers and developers, understanding the complexity of trust and creditability in the use of AI in nursing will be crucial for successful implementation. This research will contribute and assist in understanding nurses’ role in this change.

Ethical considerations in AI-enhanced medical decision support systems: A review

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America

This review paper critically examines the ethical considerations involved in implementing generative Artificial Intelligence (AI) in healthcare supply chain optimization across three distinct regions: India, the United Kingdom, and the United States of America. The study synthesizes findings from various case studies and academic research to highlight both common and unique ethical challenges faced in these countries. Key themes such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare solutions are explored, alongside the unique socio-cultural, legal, and regulatory challenges specific to each region. The paper proposes a set of best practices for incorporating ethical considerations into the deployment of generative AI in healthcare. These include the development of inclusive ethical frameworks, regular ethical audits, comprehensive training and education programs, public engagement initiatives, and interdisciplinary collaboration. The paper also delves into future research directions and policy development, emphasizing the need to address healthcare disparities, adapt legal and regulatory frameworks, enhance generative AI explainability, and evaluate long-term outcomes.The study concludes by underscoring the importance of ethical design and deployment of generative AI systems in healthcare, advocating for a balanced approach that aligns technological advancements with ethical standards and global healthcare needs. This comprehensive review aims to contribute to the discourse on ethical generative AI implementation, offering insights and recommendations for policymakers, healthcare professionals, and generative AI developers to foster responsible and beneficial use of generative AI in healthcare globally.

The text and data mining opt-out in Article 4(3) CDSMD: Adequate veto right for rightholders or a suffocating blanket for European artificial intelligence innovations?

Abstract By introducing Article 4 in Directive 2019/790 (CDSMD), the European Union legislator intended to both encourage innovation and to provide more legal certainty for text and data mining (TDM) activities. That said, it appears that this provision does not strike a fair balance between the interests of rightholders and Artificial Intelligence (AI) developers. This article argues that Article 4(3) CDSMD does not necessarily strengthen the rightholders’ position whilst potentially hindering the advancement of AI developments in the European Union. It is unclear how the reservation of rights shall be realized in practice. By imposing transparency obligations on AI system providers, the upcoming Artificial Intelligence Act may however allow rightholders to make more effective use of the opt-out mechanism.

Development of a Content Framework of Artificial Intelligence Integrated Education Considering Ethical Factors

The rapid advancement of Artificial Intelligence (AI) technology has brought about significant positive changes across society. However, it has also introduced challenges like privacy breaches, data bias, and spreading fake news. In response, several countries, including South Korea, have provided ethical guidelines and policies for AI. Yet, these measures often fall short of keeping pace with the speed and diversity of AI development. To address these issues, this study developed a comprehensive approach by integrating ethical considerations into AI education, covering the entire AI technology development and application process. It involved analyzing previous research on AI and AI ethics education and creating a draft of an integrated AI education program focused on problem-solving based on computational thinking and ethical practicality. The draft was refined and supplemented through two rounds of expert Delphi surveys, ultimately leading to an “Integrated AI Education Program.” This proposed program emphasizes that the knowledge content of AI technology and ethical considerations should not be treated separately but addressed together. It aims to enhance moral and social capacities alongside the experience of thinking like a computer scientist when solving real-life problems. Through this, the program supports the ultimate goal of AI education: to foster computational thinking while providing an educational experience that considers both technology and ethics. This study is expected to advance the discussion on integrating AI ethics into AI education curricula and contribute to developing socially responsible AI developers and users.

Exploring the Correlation between Students' Attitudes towards AI and Their Learning Outcomes

This research study aims to investigate the intricate connection between students' attitudes toward Artificial Intelligence (AI) and the correlates of their learning outcomes. With the growing integration of AI in various educational settings, understanding how students perceive AI and how these perceptions correlate with their academic performance is of vital importance. This research employs a descriptive and correlational approach, combining surveys and learning outcomes data analysis to delve into the changing aspects of this relationship. By exploring students' attitudes toward AI, their acceptance levels, and their learning outcomes, this study seeks to provide insights that can inform educators, policymakers, and AI developers on optimizing AI's role in education for enhanced student success. Also, the findings indicate a significant relationship between students' attitudes toward AI and their learning outcomes, emphasizing the importance of considering these attitudes in understanding and predicting educational outcomes. One of the key recommendations is that institutions should consider integrating more comprehensive AI education into their curriculum to bridge the gap in actively seeking out information about AI. This proactive approach can contribute to a more informed and adaptive student body, ultimately fostering a positive and constructive environment for AI integration in education.

Hierarchy of Ethical Principles for the use of Artificial Intelligence in Medicine and Healthcare

The article researches the problem of ethical support of the application of artificial intelligence (AI) in medicine and healthcare, which is topical for modern sci­ence. Despite a significant number of foreign and domestic publications devoted to the topic of AI, the conceptual justification of the ethics of AI application in medicine and healthcare remains poorly developed. Relying on international recommendations and articles, as well as on their own experience of research activities, work in research ethics committees, the results of a pilot survey of health care workers, etc., the authors define and analyze the basic ethical principles of using AI in medicine and health care. The proposed principles are considered in the context of their practical application to protect human and natural rights and interests, which includes preservation of patient confidentiality, prevention of discrimination, protection from AI errors, respect for in­formed consent, as well as compliance with the norms of “open science”, mutual trust of developers and users, etc. The proposed principles are analyzed in the context of their practical application. The application of the proposed principles will orient scientists, AI developers, ethical committees conducting expert review of research, society as a whole to the priorities of humanization of healthcare, respect for human beings and nature, as well as to educate society, create a regulatory framework, ethical recommen­dations and codes of ethics for the use of AI in medicine and healthcare.

Enhancing User Experience With a Generative AI Chatbot

With the rapid evolution of artificial intelligence (AI), this study aims to examine the interplay among the perceived usability, perceived enjoyment, perceived responsiveness, and intention to continue using ChatGPT. Structural equation modeling (SEM) was used to investigate our proposed model. We recruited 446 ChatGPT users through an online survey conducted on the Connect platform, powered by CloudResearch. Perceived usability (β = 0.254) and enjoyment (β = 0.438) significantly influence satisfaction with ChatGPT. However, perceived responsiveness is not significantly related to perceived attachment or satisfaction. Furthermore, we established that perceived attachment (β = 0.405) and satisfaction (β = 0.447) are pivotal in shaping users’ intentions to continue using ChatGPT, providing insights into human–AI interactions. The practical implications of our findings suggest that generative AI chatbots should be crafted with a focus on enjoyable, user-centered interfaces that foster long-term user satisfaction and engagement. AI developers should design AI conversation flows and chatbot personas to optimize the user experience.