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The study aims to bridge the research gap in real-world usability assessments of generative AI tools. A total of 11,549 reviews were extracted and analyzed from January to March 2024 for five generative AI apps: ChatGPT, Bing AI, Microsoft Copilot, Gemini AI, and Da Vinci AI. The dataset has been made publicly available, allowing for further analysis by other researchers. The evaluation follows ISO 9241 usability standards, focusing on effectiveness, efficiency, and user satisfaction. This study is believed to be the first usability evaluation for generative AI applications using user reviews across digital marketplaces. The results show that ChatGPT achieved the highest compound usability scores among Android and iOS users, with scores of 0.504 and 0.462, respectively. Conversely, Gemini AI scored the lowest among Android apps at 0.016, and Da Vinci AI had the lowest among iOS apps at 0.275. Satisfaction scores were critical in usability assessments, with ChatGPT obtaining the highest rates of 0.590 for Android and 0.565 for iOS, while Gemini AI had the lowest satisfaction rate at \u22120.138 for Android users. The findings revealed usability issues related to ease of use, functionality, and reliability in generative AI tools, providing valuable insights from user opinions and feedback. Based on the analysis, actionable recommendations were proposed to enhance the usability of generative AI tools, aiming to address identified usability issues and improve the overall user experience. This study contributes to a deeper understanding of user experiences and offers valuable guidance for enhancing the usability of generative AI applications.<\/jats:p>","DOI":"10.7717\/peerj-cs.2421","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T07:21:59Z","timestamp":1729840919000},"page":"e2421","source":"Crossref","is-referenced-by-count":10,"title":["User-centric AI: evaluating the usability of generative AI applications through user reviews on app stores"],"prefix":"10.7717","volume":"10","author":[{"given":"Reham","family":"Alabduljabbar","sequence":"first","affiliation":[{"name":"Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}]}],"member":"4443","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"10.7717\/peerj-cs.2421\/ref-1","doi-asserted-by":"publisher","first-page":"102132","DOI":"10.1016\/j.ijinfomgt.2020.102132","article-title":"A 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this work, the authors provide a novel framework for the effectiveness of AI writing assessment systems by embedding state-of-the-art deep learning networks, user feedback mechanisms, and knowledge graph frameworks. Most writing assessment tools cannot give personalized, detailed feedback. To tackle this problem, we employ writing assessment transformer models BERT and GPT-3, which allow exploring and scoring the writing on various features, including phrase structure, semantics, vocabulary usage, <jats:italic>etc<\/jats:italic>. In our system, we propose a dynamic relational knowledge graph that incorporates writing concepts and their relations, making it easier for the system to devise contextualized thesaurus-wise suggestions. The addition of graph neural networks (GNNs) empowers the model by boosting the GNN\u2019s learning ability regarding the knowledge graph and improving comprehension of complex semantics. Additionally, we have included an iterative design whereby user feedback is collected, and the system adjusts the feedback given in light of historical feedback and changes in a user\u2019s writing behavior over time. The system reconceptualizes the problem of user AI interaction by incorporating its dynamic nature and movement towards the known user and not <jats:italic>vice-versa<\/jats:italic>, achieving higher efficiency. To assess user satisfaction and improvements in the quality of the prepared texts, the authors conduct a series of user studies evaluating the efficiency of this integrated system. However, the preliminary data obtained from the task performance analysis show that the results of the proposed framework are far better than those of traditional methods, achieving a better level of engagement and feedback while performing the assessment. This study underscores the potential of deep learning, feedback, and knowledge graph integration in leveraging writing education. It can potentially reform learners\u2019 capabilities, enabling them to write better and more effectively.<\/jats:p>","DOI":"10.7717\/peerj-cs.2893","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T08:00:26Z","timestamp":1752652826000},"page":"e2893","source":"Crossref","is-referenced-by-count":0,"title":["Optimising AI writing assessment using feedback and knowledge graph integration"],"prefix":"10.7717","volume":"11","author":[{"given":"Ci","family":"Zhang","sequence":"first","affiliation":[]}],"member":"4443","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"10.7717\/peerj-cs.2893\/ref-1","first-page":"722","article-title":"DBpedia: a nucleus for a web of open data","author":"Auer","year":"2007"},{"issue":"3","key":"10.7717\/peerj-cs.2893\/ref-2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1002\/berj.4120","article-title":"The potential of deep learning in improving k-12 students\u2019 writing skills: a 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study examines the ethical dimensions of artificial intelligence (AI) and explores the awareness and understanding of how users interact with AI and the potential consequences of these interactions. In recent years, growing awareness of the risks of AI has driven the development of ethical guidelines by various organizations. These guidelines aim to ensure that AI is developed and deployed in a responsible manner, with a focus on human well-being, societal benefits and minimizing risks. However, despite this global movement, there is a lack of consensus on key ethical issues such as bias, discrimination, privacy and human rights issues. The study focuses on the perceptions of 127 participants from 11 countries with diverse professional backgrounds in technology, education and finance. A survey with a 5-point Likert scale was used to assess participants\u2019 attitudes towards AI ethics in relation to various topics such as transparency. The study examines differences in responses across professions and countries using Multivariate Analysis of Variance (MANOVA) test. The results reveal variations in ethical priorities, suggesting that while global ethical frameworks are emerging, further efforts are needed to achieve uniformity in AI ethical standards. The findings emphasize the importance of increasing awareness and understanding of AI ethics to mitigate potential harms.<\/jats:p>","DOI":"10.7717\/peerj-cs.3504","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:22:25Z","timestamp":1769761345000},"page":"e3504","source":"Crossref","is-referenced-by-count":0,"title":["A cross-cultural examination of ethical issues in AI development"],"prefix":"10.7717","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9483-6164","authenticated-orcid":true,"given":"Meltem","family":"Eryilmaz","sequence":"first","affiliation":[]}],"member":"4443","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"issue":"1","key":"10.7717\/peerj-cs.3504\/ref-1","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1186\/s12912-024-02143-0","article-title":"The moderating role of ethical awareness in the relationship between nurses\u2019 artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study","volume":"23","author":"Atalla","year":"2024","journal-title":"BMC Nursing"},{"key":"10.7717\/peerj-cs.3504\/ref-2","volume-title":"Risk society: towards a new modernity","author":"Beck","year":"1992"},{"issue":"1","key":"10.7717\/peerj-cs.3504\/ref-3","doi-asserted-by":"publisher","first-page":"1032","DOI":"10.1057\/s41599-025-05391-w","article-title":"Thinking AI or feeling AI? 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As AI technologies increasingly shape consequential decisions in domains such as healthcare, finance, employment, and judicial processes, ensuring transparency, equity, and legitimacy has become paramount. Drawing on a comprehensive review of 152 peer-reviewed studies, this research synthesized conceptual foundations, methodological advancements, and empirical findings to build a robust framework for understanding how explain ability and fairness jointly contribute to trustworthiness. A quantitative research design was employed, incorporating large-scale datasets and multi-phase statistical analyses to evaluate how explanation fidelity, stability, and sparsity influence comprehension, trust, and perceived fairness, and how fairness interventions impact model performance and equity outcomes. Results demonstrated that explanation fidelity significantly enhanced user comprehension, while stability strongly predicted trust, highlighting the importance of consistent and faithful explanations in shaping user confidence. Fairness metrics such as demographic parity and equal opportunity gaps were powerful predictors of perceived fairness, and reductions in these disparities substantially increased user acceptance of AI decisions. Interaction analyses revealed that combining counterfactual explanations with fairness constraints produced synergistic effects, improving both equity and trust without excessively compromising predictive performance. The study also quantified trade-offs, showing that fairness interventions slightly reduced accuracy but delivered substantial gains in legitimacy and social acceptability. Human-cantered outcomes such as trust and reliance were closely linked to technical measures, illustrating that the social impact of AI is deeply intertwined with its design. By integrating findings across technical, ethical, and behavioural dimensions, this study contributed new empirical evidence and theoretical insights into how explain ability and fairness shape trustworthy AI. The results provide a comprehensive foundation for designing, evaluating, and governing AI systems that are transparent, equitable, and socially aligned in large-scale decision-making contexts.<\/jats:p>","DOI":"10.63125\/3w9v5e52","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:41:52Z","timestamp":1760949712000},"page":"54-93","source":"Crossref","is-referenced-by-count":0,"title":["TRUSTWORTHY AI: EXPLAINABILITY &amp; FAIRNESS IN LARGE-SCALE DECISION SYSTEMS"],"prefix":"10.63125","volume":"02","author":[{"name":"Ms in CS Candidate, Campbellsville University, USA","sequence":"first","affiliation":[]},{"given":"Sai Srinivas","family":"Matta","sequence":"first","affiliation":[]},{"given":"Manish","family":"Bolli","sequence":"additional","affiliation":[]},{"name":"MS in CS Candidate, University of Central Missouri, USA","sequence":"additional","affiliation":[]}],"member":"52477","published-online":{"date-parts":[[2023,12,1]]},"container-title":["Review of Applied Science and Technology"],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:41:52Z","timestamp":1760949712000},"score":15.604975,"resource":{"primary":{"URL":"https:\/\/rast-journal.org\/index.php\/RAST\/article\/view\/47"}},"issued":{"date-parts":[[2023,12,1]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2023,12,1]]}},"URL":"https:\/\/doi.org\/10.63125\/3w9v5e52","published":{"date-parts":[[2023,12,1]]}},{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T22:40:28Z","timestamp":1772664028347,"version":"3.50.1"},"reference-count":0,"publisher":"AIRCC Publishing Corporation","content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2020,9,26]]},"DOI":"10.5121\/csit.2020.101120","type":"proceedings-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T17:57:42Z","timestamp":1601056662000},"page":"235-253","source":"Crossref","is-referenced-by-count":11,"title":["Survey on Federated Learning Towards Privacy Preserving AI"],"prefix":"10.5121","author":[{"given":"Sheela Raju","family":"Kurupathi","sequence":"first","affiliation":[]},{"given":"Wolfgang","family":"Maass","sequence":"additional","affiliation":[]}],"member":"3062","published-online":{"date-parts":[[2020,9,26]]},"event":{"name":"7th International Conference on Computer Science, Engineering and Information Technology (CSEIT 2020)","acronym":"CSEIT 2020"},"container-title":["Computer Science &amp; Information Technology (CS &amp; IT)"],"deposited":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T17:57:50Z","timestamp":1601056670000},"score":15.555056,"resource":{"primary":{"URL":"https:\/\/aircconline.com\/csit\/papers\/vol10\/csit101120.pdf"}},"issued":{"date-parts":[[2020,9,26]]},"references-count":0,"URL":"https:\/\/doi.org\/10.5121\/csit.2020.101120","published":{"date-parts":[[2020,9,26]]}},{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T16:51:15Z","timestamp":1773679875481,"version":"3.50.1"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":251,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"<jats:p>In this paper, we report the results of a detailed survey of 20 standard contracts for chat-based generative AI services from 13 leading providers. We cover both US providers, such as Anthropic, Google, Microsoft, and OpenAI, and European and Chinese providers, such as Alibaba, DeepSeek, and Mistral. We analyse the terms and conditions that govern both commercial and consumer use of the services for customers in the UK and across Europe. We cover a range of contractual issues including the choice of law and forum for disputes; provider duties and liability for breach of contract; acceptable use policies; and termination. We compare trends across providers and analyse the survey findings in light of English and EU law, including consumer protection law, the Data Act, and the AI Act.<\/jats:p>","DOI":"10.2139\/ssrn.6350698","type":"posted-content","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:46:03Z","timestamp":1773074763000},"source":"Crossref","is-referenced-by-count":0,"title":["Chat Ts&amp;amp;Cs: A Survey of 20 Standard Contracts for Generative AI Services"],"prefix":"10.2139","author":[{"given":"Johan David","family":"Michels","sequence":"first","affiliation":[]},{"given":"Christopher","family":"Millard","sequence":"additional","affiliation":[]},{"given":"Camille","family":"abou Farhat","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"ref1","journal-title":"Examples include Midjourney, Adobe Firefly, Google Veo3, and Kling"},{"key":"ref2","journal-title":"Examples include Harvey AI for the legal sector; or FinLLM for financial services"},{"key":"ref3","author":"Eu Ai Act","journal-title":"Art.3(63) and (66)"},{"key":"ref4","year":"2025","journal-title":"The same applies to OpenAI's ChatGPT services and its GPT OSS models and DeepSeek's chat-based service and its open-source model"},{"issue":"6","key":"ref5","article-title":"Governing law","author":"Grok Tos","journal-title":"Europe Specific Terms"},{"key":"ref6"},{"issue":"7","key":"ref7","journal-title":"Meta AIs UK ToS"},{"key":"ref8","article-title":"This applied to use \"in any other capacity","journal-title":"Meta AIs UK ToS"},{"key":"ref9","author":"See E G Openai","journal-title":"Introducing ChatGPT Enterprise"},{"key":"ref10","author":"Chat-Gpt Europe Tou","journal-title":"General Terms\" and \"Business use of the Services addendum"},{"key":"ref11","volume":"16","author":"Chat-Gpt Enterprise Openai","journal-title":"17 under \"Governing Laws"},{"key":"ref12","author":"U K Gemini","journal-title":"Settling disputes, governing law, and courts"},{"key":"ref13","author":"Qwen Chat Tos"},{"key":"ref14","volume":"5","author":"Perplexity Tos-C"},{"key":"ref15","year":"2016","journal-title":"See also Verein f\ufffdr Konsumenteninformation v. 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