Relevant bibliographies by topics / AI knowledge gap

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  1. Journal articles
  2. Books
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'AI knowledge gap'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AI knowledge gap"

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Bhattarai, Pragya, Prashant Nepal, Pravin Khatri, et al. "Knowledge, Attitude and Practice at AI in Education: Student’s Perception." NPRC Journal of Multidisciplinary Research 1, no. 7 (2024): 53–66. https://doi.org/10.3126/nprcjmr.v1i7.72463.

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As AI becomes a bigger part of our schools and universities, it's important to get a handle on how students feel about it, what they know, and how they use it. In this study, we combined surveys and interviews to dig into these areas. We asked students about their thoughts on AI, their understanding of how it works, and their experiences using AI tools in their classes. The interviews helped us understand their responses in more depth. We found that students are generally excited about AI and its potential to make learning more personalized. However, there’s a gap between their enthusiasm and
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Alharbi, Wael. "Mind the Gap, Please!" International Journal of Computer-Assisted Language Learning and Teaching 14, no. 1 (2024): 1–28. http://dx.doi.org/10.4018/ijcallt.351245.

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This mixed-methods study investigates the mismatch between student use of AI for academic tasks and teacher awareness in Saudi Arabian higher education. Surveys of 78 teachers and 243 students in English for Specific Purposes courses at public and private universities included Likert-scale items and an open-ended question on AI tools' benefits, challenges, and impact on learning and academic integrity. The study reveals a significant gap between teachers' perceptions of students' AI skills and the reality of AI integration, leading to discrepancies in judging student knowledge and assessment a
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Shuo Xu. "Innovating Artificial Intelligence for Workforce Preparation and Knowledge Development." Journal of Computer Science Research 6, no. 2 (2024): 12–17. http://dx.doi.org/10.30564/jcsr.v6i2.6663.

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Artificial intelligence (AI) transforms workplaces by streamlining operations, automating tasks, and enhancing decision-making. To bridge the knowledge gap in AI best practices, a workshop was created for executives, integrating change management principles. The workshop aimed to help participants understand AI's role, use AI tools for predictive analytics, and develop strategies for leveraging AI in change initiatives. This paper outlines the workshop's impact on building confidence, knowledge, and positive attitudes towards AI in the workplace.
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Ngoveni, Mbazima. "Bridging the AI Knowledge Gap: The Urgent Need for AI Literacy and Institutional Support." International Journal of Technologies in Learning 32, no. 2 (2025): 83–100. https://doi.org/10.18848/2327-0144/cgp/v32i02/83-100.

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Sharma, Rubal, Gaetano Di Petro, Sritha Rajupet, et al. "AI frontiers in oncology: Bridging the gap between humanity and machine." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13657-e13657. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13657.

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e13657 Background: Artificial intelligence (AI) is rapidly transforming medical education and how healthcare services are delivered. Oncology healthcare professionals (HCPs) can significantly benefit from the advances of AI including optimized diagnostic capabilities, treatment plans, resource allocation, patient outcomes and much more. The synergy between technology and healthcare is strengthening, heralding opportunities for a revolution in medical education and the broader healthcare industry. We developed an introductory AI workshop and conducted pre- and post-surveys, aiming to inform the
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Cash, Steven, and R. Young. "Bowyer: A Planning Tool for Bridging the Gap between Declarative and Procedural Domains." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 5, no. 1 (2009): 14–19. http://dx.doi.org/10.1609/aiide.v5i1.12357.

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Traditionally, there have been two large obstacles faced in attempting to apply AI techniques to games and other virtual environments. The first obstacle is the gap between the largely declarative representations used by many AI techniques and the largely procedural approaches used in virtual environments. The second obstacle is the gap between the skill sets and knowledge bases of the two domain experts with AI researchers often lacking experience using virtual environment APIs and development environments and virtual environments developers often lacking significant AI knowledge. In this pap
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Benk, Michaela, Léane Wettstein, Nadine Schlicker, Florian Von Wangenheim, and Nicolas Scharowski. "Bridging the Knowledge Gap: Understanding User Expectations for Trustworthy LLM Standards." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27197–205. https://doi.org/10.1609/aaai.v39i26.34928.

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Researchers, policymakers, and developers of artificial intelligence (AI) are actively collaborating to establish trustworthy AI standards that align with broader societal values, particularly in the context of large language models (LLMs). However, the critical discourse on bridging the vast knowledge gap between experts who shape and implement standards for LLMs and users whose values are at stake remains largely unaddressed. Taking a "bottom-up" perspective and using a mixed-method approach, we first conducted interviews (N = 12) to engage with users' perceptions of normative standards in t
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Dai, Boxiao. "Global Knowledge Inequality from AI-driven Media: A Study Based on Knowledge Acquisition Dilemmas in Developing Countries." Lecture Notes in Education Psychology and Public Media 80, no. 1 (2025): 168–73. https://doi.org/10.54254/2753-7048/2024.20494.

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In the era of rapid globalization and informatization, AI-driven media is profoundly transforming how information is disseminated. However, using AI algorithms in the media has introduced new disparities in knowledge access, especially impacting developing countries. To recognize these challenges, the paper aims to explore the global knowledge inequality in the age of AI-driven media: challenges for developing countries. The study focused on AI-driven media users in developing countries and adopted a qualitative analysis approach based on literature review. The article commences with a definit
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Oliveira, Gustavo, Matheus Argôlo, Carlos Eduardo Barbosa, et al. "Applying Knowledge Management to Support Artificial Intelligence Chatbot Applications." European Conference on Knowledge Management 25, no. 1 (2024): 582–90. http://dx.doi.org/10.34190/eckm.25.1.2482.

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As the diversity and complexity of Artificial Intelligence (AI) systems increase, there is a growing need for advanced knowledge representation methods to enhance decision-making capabilities. Existing research indicates a gap between AI and Knowledge Management (KM), emphasizing the necessity of coordinating learning and knowledge creation processes between humans and machines. Despite the widespread use of generative AI, as seen through the growing popularity of conversational AI tools like chatbots powered by Large Language Models in recent years, the absence of a theoretical framework for
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Rajabi, Enayat, and Somayeh Kafaie. "Knowledge Graphs and Explainable AI in Healthcare." Information 13, no. 10 (2022): 459. http://dx.doi.org/10.3390/info13100459.

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Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interac
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Books on the topic "AI knowledge gap"

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ASME Robotics Roadmap. ASME, 2024. http://dx.doi.org/10.1115/1.887912.

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The ASME Robotics Technology Group (RTG) and its Working Groups present, herein a robotics roadmap written with the aim 1) to embody mechanical and physical necessities and bridge the gaps between AI and integration challenges; 2) to identify critical trends, top-of-market challenges, and knowledge gaps; and 3) to identify opportunities in the journey – path to get there and in strengthening the mechanical engineering footprint in robotics technology.
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Verschure, Paul F. M. J., and Tony J. Prescott. A Living Machines approach to the sciences of mind and brain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0002.

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How do the sciences of mind and brain—neuroscience, psychology, cognitive science, and artificial intelligence (AI)—stand in relation to each other in the 21st century? This chapter proposes that despite our knowledge expanding at ever-accelerating rates, our understanding of the relationship between mind and brain is, in some important sense, becoming less and less. An increasing explanatory gap can only be bridged by a multi-tiered and integrated theoretical framework that recognizes the value of developing explanations at different levels, combining these into cross-level integrated theorie
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Martens, David. Data Science Ethics. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192847263.001.0001.

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Data science ethics is all about what is right and wrong when conducting data science. Data science has so far mainly been used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. This book looks at the different concepts and techniques related to data science ethics. Data scientists and business managers are not inherently unethical, but at the same time
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Sarantou, Melanie, Satu Miettinen, and Titta Jylkäs, eds. Empathic Service Design. Bloomsbury Publishing India Pvt. Ltd, 2025. https://doi.org/10.5040/9781350476424.

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Through case studies from across the globe, this book helps designers understand how forward-looking service prototyping, analysis and tools can promote and create empathic, emotion-oriented and relevant services. Despite services being at the core of organisations, to create service flows, designers, groups and their diverse stakeholders must negotiate ever more complex and hybrid interactions between human and more-than-human role players in service delivery. The challenge lies in delivering empathic, relevant and desirable services, despite these complexities. New knowledge is needed to bri
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Book chapters on the topic "AI knowledge gap"

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Zhao, Jin. "Research on factors influencing AI-usage knowledge gap in China." In Connecting Ideas, Cultures, and Communities. Routledge, 2025. https://doi.org/10.1201/9781003591511-4.

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Assante, Dario, Claudio Fornaro, Luigi Laura, Daniele Pirrone, Ali Gokdemir, and Veselina Jecheva. "Bridging the AI Knowledge Gap with Open Online Education in Europe." In Smart Technologies for a Sustainable Future. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61905-2_35.

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Serbanescu, Anca. "Human-AI System Co-creativity to Build Interactive Digital Narratives." In Springer Series in Design and Innovation. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49811-4_37.

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AbstractThe interactive digital narrative (IDN) is an interdisciplinary field connected to narrative studies, design, human-computer interaction, and gaming. In this contribution, IDN is investigated from the designer’s perspective; it deals with the use of prolific artificial intelligence (AI) support systems in this area, highlighting the need to examine their potential. However, the features and essential components of the AI support system are not systematically categorised. This paper fills the aforementioned gap by providing a literature review of twenty academic contributions that make
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Loh, Chao Hong, Sophia Wei, Chee Teck Phua, Azizah Mohd, Albert Tan, and Boon Khoon Seow. "Nacelle: Knowledge Graph-Based Conversational AI for Skills Gap Analysis to Achieve Sustainable Learning at Workplace." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6332-1_31.

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Kousa, Päivi, and Hannele Niemi. "Artificial Intelligence Ethics from the Perspective of Educational Technology Companies and Schools." In AI in Learning: Designing the Future. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_17.

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AbstractThis chapter discusses the ethical issues and solutions that educational technology (EdTech) companies and schools consider during their daily work. As an example, two Finnish cases are provided, in which companies and schools were interviewed about the problems they have experienced. The chapter first reviews the regulations and guidelines behind ethical AI. There are a vast number of guidelines, regulations, and principles for ethical AI, but implementation guidelines for how that knowledge should be put into practices are lacking. The problem is acute because, with the quick pace of
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Tewari, Veena, Abdulrahman Mohammed Abdullah Al Ismaili, Swapnil Morande, Shaik Mastanvali, Amitabh Mishra, and Jayakumar Aswathaman. "Accelerating Innovation Through AI-Driven Knowledge Transfer: Bridging the Gap Between R&D Labs and Entrepreneurial Markets." In Sustainable Economy and Ecotechnology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92942-7_56.

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Bouslama, Faouzi, Lana Hiasat, and Christine Coombe. "Rethinking Career Development Post COVID-19: The Career Profile of the Future Framework (CPFF), an E.I.-Based Human Skills Approach." In Future Trends in Education Post COVID-19. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1927-7_21.

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AbstractPresently, many organizations are going through a lot of changes brought about using emerging technologies, such as Artificial Intelligence (AI), Machine Learning, and Smart Automation, to streamline their businesses and improve productivity. Moreover, environmental factors, the COVID-19 pandemic, brought attention to the importance of mental health and Emotional Intelligence (E.I.). Therefore, several existing job roles are being redefined and many young professionals in non-supervisory roles are at risk of losing their jobs. These organizations are now more than ever very keen on ide
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Margariti, Katerina, Pantelis Velanas, Christos Malliarakis, John Soldatos, and Vassileios Roussakis. "LAW-GAME: Elevating Experiential Training Through Gamification Technologies." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_23.

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AbstractThis research paper introduces the LAW-GAME project, an innovative initiative aimed at improving law enforcement professional training using gamification technologies. LAW-GAME’s primary goal is to bridge the theoretical knowledge and practical application gap by immersing police officers in a safe and controlled virtual environment. LAW-GAME is made up of four distinct “mini games,” each of which is designed to train and evaluate police officers in critical areas of law enforcement. Within an immersive virtual environment, these modules provide comprehensive training on forensic exami
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Kimmig, Andreas, Jieyang Peng, and Jivka Ovtcharova. "Capacity Building for Digital Work – A Case from Sino-German Cooperation." In New Digital Work. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26490-0_15.

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AbstractThe way humans work is constantly changing. This has always been the case, especially in dynamic environments. In the context of Industry 4.0 and the Internet of Things (IoT), collaborative platforms, accelerated by Artificial Intelligence (AI) technologies, give rise to new automation opportunities of complex and previously labor-intensive tasks, while also creating new business models for multiple stakeholders.Due to accelerated product innovation, the manufacturing industry needs to be able to generate solutions in a timely manner and quickly move them into production according to c
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Rehm, Georg, and Andy Way. "Strategic Research, Innovation and Implementation Agenda for Digital Language Equality in Europe by 2030." In European Language Equality. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28819-7_45.

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AbstractThis chapter presents the ELE Programme (ELE Consortium 2022). Reacting to the landmark resolution (European Parliament 2018), its vision is to achieve digital language equality in Europe by 2030. The programme was prepared jointly with many stakeholders from the European Language Technology, Natural Language Processing, Computational Linguistics and language-centric AI communities, as well as with representatives of relevant initiatives and associations, and language communities. Europe still suffers from strong inequalities in terms of technology support of its languages. English is
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Conference papers on the topic "AI knowledge gap"

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Ordyniak, Sebastian, Giacomo Paesani, Mateusz Rychlicki, and Stefan Szeider. "Explaining Decisions in ML Models: A Parameterized Complexity Analysis." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/53.

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This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the prevalent black-box perception, our study focuses on models with transparent internal mechanisms. We address two principal types of explanation problems: abductive and contrastive, both in their local and global variants. Our analysis encompasses diverse ML models, including Decision Trees, Decision Sets, Decision Lists, Ordered Binary Decision Diagrams, Random Forests, and Boolean Circuits, and ensembles thereof, each
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Stefanakis, A. S., D. Kolokotsa, E. Kapartzianis, J. Bonis, and J. K. Kaldellis. "Novel PSE applications and knowledge transfer in joint industry - university energy-related postgraduate education." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.164061.

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The field of Process Systems Engineering (PSE) is undergoing a renaissance through the integration of artificial intelligence (AI) and machine learning (ML). This transformation is driven by the vast availability of industrial data and advanced computing power, enabling the practical application of sophisticated ML models. These models enhance PSE capabilities in design, control, optimization, and safety. The progress of ML and ever-present data collection address previously intractable problems, particularly in system integration and life-cycle modeling. ML-powered predictive algorithms are a
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Zhu, Wangda, Wanli Xing, Bailing Lyu, Chenglu Li, Fan Zhang, and Hai Li. "Bridging the Gender Gap: The Role of AI-Powered Math Story Creation in Learning Outcomes." In LAK '25: The 15th International Learning Analytics and Knowledge Conference. ACM, 2025. https://doi.org/10.1145/3706468.3706539.

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Ara, Zinat, Hossein Salemi, Sungsoo Ray Hong, et al. "Closing the Knowledge Gap in Designing Data Annotation Interfaces for AI-powered Disaster Management Analytic Systems." In IUI '24: 29th International Conference on Intelligent User Interfaces. ACM, 2024. http://dx.doi.org/10.1145/3640543.3645214.

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Smith, Mahdis, Luke Houghton, Carla Riverola, and Ali Intezari. "Autonomy at the Crossroads: Knowledge Workers Teamed with Intelligent Machines: A Qualitative Systematic Review." In 12th International Conference on Human Interaction and Emerging Technologies (IHIET 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005458.

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Purpose – This study aims to identify risks in adopting artificial intelligence (AI) for organizational decision-making by examining empirical studies. AI, is increasingly applied to automate tasks and decisions which were traditionally made by humans, posing challenges to sense of autonomy. Design/methodology – A total of 28 empirical studies were selected using predefined inclusion and exclusion criteria. To this end, this research systematically explored the processes of inquiry, identification, selection, critical appraisal, and the synthesis of empirical studies. This study is undertaken
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Smith, Mahdis, Luke Houghton, Carla Riverola, and Ali Intezari. "Autonomy at the Crossroads: Knowledge Workers Teamed with Intelligent Machines: A Qualitative Systematic Review." In 6th International Conference on Human Systems Engineering and Design Future Trends and Applications (IHSED 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005553.

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Purpose – This study aims to identify risks in adopting artificial intelligence (AI) for organizational decision-making by examining empirical studies. AI, is increasingly applied to automate tasks and decisions which were traditionally made by humans, posing challenges to sense of autonomy. Design/methodology – A total of 28 empirical studies were selected using predefined inclusion and exclusion criteria. To this end, this research systematically explored the processes of inquiry, identification, selection, critical appraisal, and the synthesis of empirical studies. This study is undertaken
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Javed, Ms Zenab, and Ms Insha Mirza. "ANALYZING THE INFLUENCE OF MYNTRA'S AI-POWERED VISUAL SEARCH ON YOUTH FASHION TRENDS AND SHOPPING HABITS IN BHOPAL." In Transforming Knowledge: A Multidisciplinary Research on Integrative Learning Across Disciplines. The Bhopal School of Social Sciences, 2025. https://doi.org/10.51767/ic250131.

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Myntra's AI-powered visual search technology is revolutionizing the shopping experience for Bhopal's youth by seamlessly integrating innovation with personalization. This study delves into the transformative impact of this advanced tool on consumer behavior, emphasizing its ability to streamline fashion discovery and purchasing while adapting to dynamic trends. The feature enhances convenience, efficiency, and user satisfaction by leveraging state-of-the-art AI algorithms, fostering deeper customer engagement. The research examines demographic factors such as age, gender, education, and income
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Amour, Myriam, B. Kent Rachmat, Agustin Soriano Rementeria, Valerian Guillot, and Ekaterina Millan. "Empowering Drilling and Optimization with Generative AI." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/221862-ms.

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Abstract Over the last 10 years, no industry has faced more pressure and scrutiny to gain efficiency than the oil and gas (O&G) industry. Drilling engineers are tasked with leveraging data to optimize design and operations. O&G companies have collected and stored large amounts of high-frequency sensor data and reporting data. Efficient data access is fundamental for upstream projects, and data operations skills are critical for drilling engineers. However, data operations have not become a core competency for most of the drilling engineering community. A novel solution to this problem
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Nevoso, Isabella, Elena Polleri, and Caterina Battaglia. "Bridging the gap: workshop results on the interaction between human creativity and artificial intelligence." In 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005909.

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In the expansive realm of contemporary Artificial Intelligence (AI) technologies, designers and architects are challenged to collaborate synergistically with these powerful tools. While the potential of AI is considerable, it also gives rise to significant questions regarding the intellectual property of generated works and the nature of interaction between humans and technology. Furthermore, this technologies challenge the definition of creativity, prompting the question of how to distinguish the role of a human from that of a machine. This prompts the question of how the transition from huma
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Shen, Ruimin, Yan Zheng, Jianye Hao, et al. "Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/466.

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Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles
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Reports on the topic "AI knowledge gap"

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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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Korell, Johanna Lea, Subin Nijhawan, Roland Alexander Ißler, and Britta Viebrock. Fragebogen für Lernende "Künstliche Intelligenz und Fremdsprachen", im Rahmen des Artikels "Fremdsprachenlernen im Zeitalter Künstlicher Intelligenz – eine empirische Untersuchung zu Kenntnissen, Meinungen und Nutzungsweisen von Englisch-, Französisch- und Spanischschüler*innen der Sekundarstufen I und II". Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2025. https://doi.org/10.21248/gups.88393.

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This empirical study investigates AI knowledge, beliefs, and reported practices among secondary school learners of English, French, and Spanish in Germany (n=226). A survey revealed significant gaps between students' self-perceived and actual understanding of AI as well as their use and critical reflection on it. The findings suggest that integrating AI into foreign language learning, initially through targeted teacher training, is instrumental to develop both functional and evaluative skills among students, thereby sustainably fostering critical digital literacy.
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Porciello, Jaron, Volha Skidan, Ramya Ambikapathi, et al. The State of the Field for Research on AgrifoodSystems. Juno Reports. CABI, 2024. http://dx.doi.org/10.1079/junoreports.2024.0001.

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Science is called upon in times of significant change and uncertainty to respond to global challenges and opportunities. Novel approaches are needed to connect science with policy targets so that we can dedicate some of the scientific knowledge that we’ve accumulated over the course of human history on being able to save the world—while we still have a world left to save. Converging crises of hunger, climate, and political unrest remind us there is no time to waste. The impacts of climate change are increasingly evident worldwide, particularly affecting farmers and rural communities in regions
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AI and the Knowledge Economy: Transforming APO Members. Asian Productivity Organization, 2025. https://doi.org/10.61145/lsys8690.

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AI is becoming a key driver of the knowledge economy, accelerating innovation, automating routine tasks, and enabling deeper human–machine collaboration. This APO report explores how member economies are adopting AI, where gaps remain, and what strategies can support inclusive, responsible, and future-ready development. It also presents tailored policy options to support national strategies, strengthen digital ecosystems, and drive inclusive productivity growth to help ensure that all members can benefit from the AI-driven transformation.
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