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Journal articles on the topic 'AI-generated learning analytics'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Khosravi, Hassan, Antonette Shibani, Jelena Jovanovic, Zachary A. Pardos, and Lixiang Yan. "Generative AI and Learning Analytics." Journal of Learning Analytics 12, no. 1 (2025): 1–11. https://doi.org/10.18608/jla.2025.8961.

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The rapid adoption of generative AI (GenAI) in education has raised critical questions about its implications for learning and teaching. While GenAI tools offer new avenues for personalized learning, enhanced feedback, and increased efficiency, they also present challenges related to cognitive engagement, student agency, and ethical considerations. Learning analytics (LA) provides a crucial lens to examine how GenAI affects learning behaviours and outcomes by offering data-informed insights into GenAI’s impact on students, educators, and educational ecosystems. Thus, obtained insights allow fo

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Wagh, Ms Pranali, Sahil Desai, Purav Doshi, Chaitanya Gajoor, and Advait Narkar. "AI Generated Cricket Score using NLP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42922.

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To create an AI-based system for creating real- time cricket scorecards using live or recorded commentary, this study investigates the integration of Natural Language Processing (NLP), audio recognition, and machine learning approaches. Along with team names and venue information, users can stream live commentary using a microphone or upload audio files using the system’s user-friendly frontend, Streamlit. Speech recognition is used to process and turn the audio into text, which is subsequently tokenized and subjected to NLP techniques to extract important events like runs, wickets, and overs.

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Shreejaa, N., and Dr V. Sudha. "HARNESSING GENERATIVE AI: INNOVATING DATA ANALYTICS IN THE ANALYTICAL ERA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40578.

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The field of data analytics is being transformed by the use of generative artificial intelligence (AI) in today's rapidly changing digital landscape. This article explores the innovative applications and implications of generative AI in enhancing data analytics capabilities, with a focus on its impact in the analytical era. Generative AI refers to algorithms that can create new content, such as images, text, or entire datasets, based on patterns and examples it has been trained on. This technology has revolutionized traditional data analytics by allowing organizations to gain deeper insights,

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Dalat, Yvon. "LOOKING TO IMPLEMENT DATA ANALYTICS AND AI TO TRANSFORM LEARNING? CHECK FOR THESE POTENTIAL MINEFIELDS AND BEST PRACTICES." Performance Improvement Journal 62, no. 6 (2023): 189–91. http://dx.doi.org/10.56811/pfi-23-0018.

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The article discusses the implementation of data analytics and artificial intelligence (AI) in the context of learning and performance improvement. While it emphasizes the potential benefits of AI in personalized learning, process automation, and so forth, it also raises important considerations, such as data privacy, ethical usage, and intellectual property challenges associated with AI-generated content. The article presents a case study that demonstrates the effectiveness of personalized learning interventions in a real-world context. The article suggests several ways for individuals in the

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Raghu, Ram Chowdary Velevela. "Transforming AI: The Pivotal Impact Of Big Data On Innovation." Journal of Advancement in Data Computational Statistics and Data Analysis 1, no. 1 (2025): 17–25. https://doi.org/10.5281/zenodo.14936895.

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<em>Big Data refers to the extensive and complex datasets generated at unprecedented volumes, velocities, and varieties, which exceed the processing capacity of traditional data management tools. These datasets pose challenges in terms of capturing, storing, transferring, querying, and processing information efficiently and in real-time. To address these challenges, advanced analytics techniques have emerged, often integrating Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) methodologies. The synergy between Big Data and AI has enabled significant advancements, tran

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Eze, Chibuike Samuel, and Lior Shamir. "Analysis and Prevention of AI-Based Phishing Email Attacks." Electronics 13, no. 10 (2024): 1839. http://dx.doi.org/10.3390/electronics13101839.

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Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning too

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Okhunov, Mukhammadyusuf. "INTEGRATING AI TOOLS IN CLASSROOMS OF TEACHING ENGLISH AT UNIVERSITIES." Oriental Renaissance: Innovative, educational, natural and social sciences 4, no. 24 (2024): 133–36. https://doi.org/10.5281/zenodo.14208838.

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<em>The integration of Artificial Intelligence (AI) in university can offer advanced tools for personalized learning, engagement, and real-time feedback. This article examines the methodologies behind the use of AI in English language teaching and focuses on AI-powered applications such as chatbots, adaptive learning platforms, and speech recognition software. It highlights the pedagogical benefits, such as tailored instruction and AI-driven assessments, and addresses potential challenges, including data privacy concerns and the digital divide. The future of AI in ELT points to even more sophi

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Aravind Ayyagiri, Anshika Aggarwal, and Shalu Jain. "Enhancing DNA Sequencing Workflow with AI-Driven Analytics." International Journal for Research Publication and Seminar 15, no. 3 (2024): 203–16. http://dx.doi.org/10.36676/jrps.v15.i3.1484.

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The rapid advancements in DNA sequencing technologies have revolutionized genomics, enabling a deeper understanding of genetic information and its implications in various fields such as medicine, agriculture, and evolutionary biology. However, the exponential increase in sequencing data presents significant challenges in terms of data management, analysis, and interpretation. Traditional methods often fall short in handling the complexity and volume of data generated, necessitating the integration of advanced technologies like Artificial Intelligence (AI) to optimize the DNA sequencing workflo

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Awad, Iman, Afnan Al-Ghamdi, Azza Al-Ghamdi, and Lina Al-Farani. "The Impact of Artificial Intelligence Technologies in Educational Informatics on Improving Learners Performance: A Meta-Analysis." Journal of Umm Al-Qura University for Educational & Psychological Sciences 16, no. 3 (2024): 349–64. https://doi.org/10.54940/ep41266805.

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In light of rapid technological advancements, the vast amount of educational data generated on e-learning platforms has become a rich field for research and development in educational institutions. As a result, the field of Educational Informatics has emerged as an interdisciplinary domain combining education and informatics. This study aims to investigate the impact of artificial intelligence (AI) techniques in Educational Informatics through a meta-analysis of 27 studies conducted between 2020 and 2022. These studies specifically explored the effects of AI techniques, including learning anal

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Srinivasa, Reddy Vuyyuru. "Unlocking Future Consumer Insights: Using Predictive Analytics and AI to Shape Proactive Retail Strategies." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 110–15. https://doi.org/10.5281/zenodo.15560633.

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Companies within the retail sector experience fundamental transformations through the combination of predictive analytics alongside artificial intelligence (AI) for developing new strategic directions. The research delves into technology-enabled methods for retailers to acquire consumer data that produces business strategies for inventory enhancements, together with personalized customer service and performance optimization. The current AI and predictive analytics trends serve as research subjects to understand operational retail opportunities for developing proactive decision-making approache

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Naseer, Fawad, and Sarwar Khawaja. "Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners." Applied Sciences 15, no. 8 (2025): 4473. https://doi.org/10.3390/app15084473.

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Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adap

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Kim, Jiha, You-jin Park, and Joo-eun Hyun. "Exploring Learning Analytics Data for Supporting Self-Directed Learning in Smart Learning Environment." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 11 (2023): 787–800. http://dx.doi.org/10.22251/jlcci.2023.23.11.787.

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Objectives The purpose of this study was to explore and define the factors that constitute self-directed learning in smart learning from a learning analytics perspective and to validate the appropriateness of the type and characteristics of the learning data. Methods To ensure the validity of the learning analytics data, we used an expert validation method. For this purpose, a total of seven experts with over 8 years of experience in e-learning, educational research, and educational practice participated. We explained the target service of this study and the purpose and tools of the vali

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Prashanth, Cecil. "AI-POWERED DEMAND FORECASTING AND SLOTTING OPTIMIZATION IN WAREHOUSE OPERATIONS." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 496–505. https://doi.org/10.5281/zenodo.15545399.

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In the evolving landscape of supply chain management, warehouse operations are increasingly adopting artificialintelligence (AI) to enhance efficiency and responsiveness. This article explores how AI-powered solutions aretransforming two critical aspects of warehouse logistics: demand forecasting and slotting optimization. Demandforecasting enables organizations to predict future inventory requirements more accurately, thereby reducingstockouts and overstocking. Slotting optimization, on the other hand, enhances storage efficiency by assigningoptimal locations for inventory based on AI-generat

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Obiajuru Triumph Nwadiokwu. "Ethical AI and machine learning integration in health innovation information systems for clinical excellence." Computer Science & IT Research Journal 6, no. 3 (2025): 125–42. https://doi.org/10.51594/csitrj.v6i3.1872.

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The integration of Ethical Artificial Intelligence (AI) and Machine Learning (ML) in Health Innovation Information Systems is transforming clinical excellence by enhancing decision-making, optimizing healthcare workflows, and improving patient outcomes. AI-driven health technologies, such as predictive analytics, automated diagnostics, and personalized medicine, offer significant benefits in disease detection, treatment planning, and patient monitoring. However, their implementation raises ethical concerns related to data privacy, algorithmic bias, transparency, and accountability, necessitati

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Redeer, Avdal Saleh, and Maseeh Yasin Hajar. "Comparative Analysis of AI and Machine Learning Applications in Modern Database." Engineering and Technology Journal 10, no. 03 (2025): 4112–23. https://doi.org/10.5281/zenodo.15062617.

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Using artificial intelligence (AI) and machine learning (ML) in contemporary database systems is the topic of discussion in this article. It examines a variety of scientific publications that investigate the breakthroughs that have been generated by artificial intelligence in a variety of fields, including agriculture, healthcare, military, and cloud computing. In this assessment, the transformational potential of artificial intelligence is highlighted within the context of improving data management, predictive analytics, decision-making, and automation. In addition, the debate discusses impor

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Tang, Shan Shan, Wen Fen Beh, and Kenny S. L. Cheah. "The Role of Lecturers’ AI Leadership in Enhancing Postgraduate Student Teachers’ Integration of Mobile AI Tools: A Mixed-Methods Study in Malaysian Education Faculties." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 07 (2025): 136–58. https://doi.org/10.3991/ijim.v19i07.51971.

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This study examined the influence of lecturers’ artificial intelligence (AI) leadership on postgraduate student teachers’ motivation to integrate AI into their curricula in Malaysian higher education. Using a sample of 62 participants, the study employed a mixed-methods approach to explore ethical implications and the alignment of AI with traditional teaching practices. By means of open-ended questions and online surveys, the study generated both quantitative and qualitative understanding of how leadership influences acceptance of AI in educational settings. Key findings showed that transforma

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Goyal, Mahesh Kumar. "Synthetic Data Revolutionizes Rare Disease Research: How Large Language Models and Generative AI are Overcoming Data Scarcity and Privacy Challenges." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 1368–80. https://doi.org/10.17762/ijritcc.v11i11.11411.

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The scarcity of patient data has been a bottleneck for finding solutions in healthcare, and in the challenging field of rare diseases, this bottleneck is worsening due to the increasing use of data analytics. With medical research anchored problems—limited availability of data and stringent privacy regulations preventing data sharing—synthetic data is becoming a developing solution. Using advanced generative AI models, synthetic data can accurately represent the statistical properties of real-world patient data, while preserving the privacy of patients. Gan and VAEs are among other powerful ai

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Venkat, Kalyan Uppala. "Enhancing Diagnostic Accuracy: The Role of Artificial Intelligence and Machine Learning in Imaging Analysis and Predictive Analytics for Personalized Medicine." European Journal of Advances in Engineering and Technology 7, no. 7 (2020): 101–6. https://doi.org/10.5281/zenodo.13790107.

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The advent of artificial intelligence (AI) and machine learning (ML) has initiated a transformative shift in the field of diagnostics, offering unprecedented opportunities to enhance the accuracy, efficiency, and personalization of medical assessments. These technologies have found significant applications in diagnostic imaging and predictive analytics, where they assist clinicians in detecting and diagnosing diseases with a level of precision that often surpasses human capabilities. AI and ML algorithms are particularly effective in analyzing complex medical images, such as those generated in

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Arnold, Niwarinda. "Smart Health Records: Integrating AI for Efficient Data Management." NEWPORT INTERNATIONAL JOURNAL OF RESEARCH IN MEDICAL SCIENCES 6, no. 1 (2025): 14–18. https://doi.org/10.59298/nijrms/2025/6.1.141800.

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The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and en

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Aas, Kjersti, Arthur Charpentier, Fei Huang, and Ronald Richman. "Insurance analytics: prediction, explainability, and fairness." Annals of Actuarial Science 18, no. 3 (2024): 535–39. https://doi.org/10.1017/s1748499524000289.

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AbstractThe expanding application of advanced analytics in insurance has generated numerous opportunities, such as more accurate predictive modeling powered by machine learning and artificial intelligence (AI) methods, the utilization of novel and unstructured datasets, and the automation of key operations. Significant advances in these areas are being made through novel applications and adaptations of predictive modeling techniques for insurance purposes, while, concurrently, rapid advances in machine learning methods are being made outside of the insurance sector. However, these innovations

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Zahaib Nabeel, Muhammad. "Big Data Analytics-Driven Project Management Strategies." Journal of Science & Technology 5, no. 1 (2024): 117–63. http://dx.doi.org/10.55662/jst.2024.5104.

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The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuri

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Joanne, Nabwire Lyanda, and Oliech Owidi Salmon. "Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning." International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) 12, no. 2 (2025): 1–10. https://doi.org/10.5281/zenodo.15130275.

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<strong>Abstract:</strong><em> </em>The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to imp

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Subhasis, Kundu. "AI-Generated Predictive Cloud Optimization: Preemptively Detecting and Preventing System Failures for Enhanced Cloud Reliability." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.15084283.

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This study examines the application of AI-driven predictive cloud optimization to enhance cloud reliability by forecasting and preventing system failures. An innovative method is proposed, employing machine learning algorithms to analyze extensive cloud infrastructure data, identify potential issues, and implement proactive measures. This approach integrates real-time monitoring, predictive analytics, and automated solutions to minimize downtime and improve resource management. A case study is presented, demonstrating the method's success in a large-scale cloud environment, with significant im

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Dai, Junjie, Xiaoyan Mao, Pengyue Wu, Huijie Zhou, and Lei Cao. "Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry." PLOS ONE 19, no. 12 (2024): e0305639. https://doi.org/10.1371/journal.pone.0305639.

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This paper investigates the impact of artificial intelligence (AI) and big data analytics on optimizing cross-border e-commerce efficiency for straw hat manufacturers in Zhejiang Province, China. It identifies market and consumer demand trends through machine learning analysis of comprehensive e-commerce data and leverages generative AI to revolutionize production and marketing processes. The integration of AI-generated content (AIGC) technology facilitates streamlined design-to-production cycles and rapid adaptation to market changes and consumer feedback. Findings demonstrate that the applic

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Tiwari, Tanuja, and Pallavi. "The AI-powered Cleanup: A Revolution in Solid Waste Management." International Journal of Environment and Climate Change 15, no. 3 (2025): 481–90. https://doi.org/10.9734/ijecc/2025/v15i34787.

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Managing solid waste is a critical global issue that demands innovative strategies to enhance efficiency, sustainability, and environmental impact. Waste generation varies across sectors and regions, both in quantity and composition, making its management a critical environmental issue. The escalating decline of ecological quality directs the scientific community toward analyzing and optimizing waste management strategies. Artificial Intelligence (AI) has appeared to bring a revolution in this area by improving the processes of waste collection, segregation, recycling, and disposal. Various mo

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Bulut, Okan, and Tarid Wongvorachan. "Feedback Generation through Artificial Intelligence." Open/Technology in Education, Society, and Scholarship Association Conference 2, no. 1 (2022): 1–9. http://dx.doi.org/10.18357/otessac.2022.2.1.125.

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Feedback is an essential part of the educational assessment that improves student learning. As education changes with the advancement of technology, educational assessment has also adapted to the advent of Artificial Intelligence (AI). Despite the increasing use of online assessments during the last decade, a limited number of studies have discussed the feedback generation process as implemented through AI. To address this gap, we propose a conceptual paper to organize and discuss the application of AI in the feedback generation and delivery processes. Among different branches of AI, Natural L

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Nayan, Uchhana, Ranjan Ravi, Sharma Shashank, Agrawal Deepak, and Punde Anurag. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 6 (2021): 101–8. https://doi.org/10.35940/ijitee.C8400.0410621.

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Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that suppor

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Chianumba, Ernest Chinonso, Nura Ikhalea, Ashiata Yetunde Mustapha, and Adelaide Yeboah Forkuo. "A Conceptual Model for Addressing Healthcare Inequality Using AI-Based Decision Support Systems." Journal of Frontiers in Multidisciplinary Research 3, no. 1 (2022): 72–88. https://doi.org/10.54660/.ijfmr.2022.3.1.72-88.

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Healthcare inequality remains a persistent global challenge, particularly affecting marginalized and underserved populations. Disparities in access, quality, and health outcomes are exacerbated by socioeconomic, geographic, and systemic barriers. This paper proposes a conceptual model for addressing healthcare inequality through the implementation of Artificial Intelligence (AI)-based Decision Support Systems (DSS). The model integrates advanced data analytics, machine learning algorithms, and real-time health information to support clinical and policy decisions aimed at reducing disparities i

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A R, SALIM MALIK. "AI Continues to be a Game Changer in the Intellectual Property Management in India." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41289.

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The integration of Artificial Intelligence (AI) into Intellectual Property (IP) management has significantly reshaped the landscape of IP administration in India. With the growing emphasis on innovation and technological advancement, India's IP ecosystem is rapidly adopting AI-driven solutions to streamline processes, enhance accuracy, and reduce human intervention. AI-powered tools for prior art searches, patent classification, and infringement detection have significantly accelerated IP workflows while reducing the backlog of pending applications. This paper explores the transformative role

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Uchhana, Nayan, Ravi Ranjan, Shashank Sharma, Deepak Agrawal, and Anurag Punde. "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 101–8. http://dx.doi.org/10.35940/ijitee.c8400.0410621.

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Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprisewide data storage capabilities that suppor

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Demartini, Claudio Giovanni, Luciano Sciascia, Andrea Bosso, and Federico Manuri. "Artificial Intelligence Bringing Improvements to Adaptive Learning in Education: A Case Study." Sustainability 16, no. 3 (2024): 1347. http://dx.doi.org/10.3390/su16031347.

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Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and re

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Alshebani, Meshal Menahi, Mohammed Qismi Alanazi, Abdulrahman Eidhah Alanazi, et al. "Application of Artificial Intelligence in Paramedic Education: Current Scenario and Future Perspective: A Narrative Review." Journal of Medicine, Law & Public Health 4, no. 1 (2023): 299–306. http://dx.doi.org/10.52609/jmlph.v4i1.98.

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Background: Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics. Objective: To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes. Methods: This review presented the role of AI in paramedic education and its perspective over the past tw

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Baynit, Marystela, Cosmas B. F. Mnyanyi, and Mohamed Salum Msoroka. "Digital Learning in the Age of Artificial Intelligence: Insights from Selected Higher Learning Institutions in Tanzania." African Quarterly Social Science Review 2, no. 2 (2025): 96–112. https://doi.org/10.51867/aqssr.2.2.9.

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The rapid advancement of artificial intelligence (AI) technologies is reshaping education globally, with it is significant implications on teaching, learning and administrative processes in higher learning institutions. The higher learning institutions in Tanzania are increasingly adopting digital learning solutions powered by AI to address challenges such as limited teaching resources, less interactive delivery approaches, lengthy assessment approaches and the need for personalized education. This study explores the integration of AI in digital learning within the selected Tanzanian higher le

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Chhetri, Swastik, and S. M. Milan. "Role of AI in Predicting Consumer Behavior." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44399.

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The emergence of Artificial Intelligence (AI) has significantly transformed the understanding and prediction of consumer behavior. Due to swift progress in machine learning and data analytics, AI allows companies to analyze extensive consumer data, identify trends, and make informed decisions based on insights derived from data. This study look into the methods in which AI improves consumer behavior analysis by inspect critical marketing functions such as customer segmentation, altered recommendations, demand forecasting, and sentiment analysis. This research make use of a survey approach, wit

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D’mello 1, Declan Anthony. "AI-Driven Detection and Support for Hidden Addiction Patterns in Remote Workers: A Multimodal Approach." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47910.

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Abstract The widespread adoption of remote work has brought new flexibility to organizations but has also complicated the monitoring of employee well-being, particularly in identifying hidden addiction patterns such as substance abuse, behavioral compulsions, and digital overuse. This study introduces a privacy-preserving, AI-driven framework that leverages lightweight transformer models and multimodal behavioral analytics to detect and support addiction risks within distributed workforces. The proposed system integrates methodologies inspired by the mhGPT (Mental Health GPT) model and incorpo

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Pooja Perlekar and Amruta Desai. "The Role of Artificial Intelligence in Personalized Medicine: Challenges and Opportunities." Metallurgical and Materials Engineering 31, no. 3 (2025): 85–92. https://doi.org/10.63278/1322.

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The integration of Artificial Intelligence (AI) in personalized medicine has revolutionized healthcare by enabling precise, data-driven, and patient-specific treatment strategies. AI-powered algorithms, particularly those leveraging machine learning (ML) and deep learning (DL), have enhanced the ability to analyze vast datasets, uncover hidden patterns, and generate predictive models that facilitate early disease detection, drug discovery, and customized treatment regimens. AI applications in genomics, medical imaging, and electronic health records (EHRs) have significantly contributed to the

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Nagarajan. "AI-Driven E-Commerce Optimization in Customer Acquisition: Enhancing E-Commerce Frontends with Artificial Intelligence." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 12, no. 4 (2024): 1–6. https://doi.org/10.5281/zenodo.15084406.

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The rapid growth of e-commerce has led to an increased reliance on artificial intelligence (AI) to optimize customer acquisition, user engagement, and conversion rates. AI-driven web frontends have transformed the way businesses approach search engine optimization (SEO), Google Shopping integration, and user experience (UX) improvements to enhance online visibility and customer retention. This paper explores the role of machine learning, predictive analytics, and AI-driven automation in streamlining the customer journey, improving personalization, and increasing conversion rates. By analy

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Saleh, Sherine Nagy, Mazen Nabil Elagamy, Yasmine N. M. Saleh, and Radwa Ahmed Osman. "An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks." Future Internet 16, no. 11 (2024): 411. http://dx.doi.org/10.3390/fi16110411.

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Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI mode

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Xuancheng, Tao. "From Tool to Subject: AI's Participation in Film Production." Sustainable Development Research 7, no. 2 (2025): p148. https://doi.org/10.30560/sdr.v7n2p148.

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Artificial intelligence (AI) has been applied to a variety of industries, and the industry is no stranger. AI was once considered a supporting actor in areas such as CGI renderings and modifications. However, now it becomes an active participant at all processes throughout a film. from writing the script to directing, from casting to visual effects and engaging the audience ai has gone from merely assisting, to being a part of the creative process, and in some cases even leading it. This paper examines how AI is transforming the film process and focuses on the shift for AI as more of a subject

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Verma, Indresh Kumar. "Leveraging AI to Unleash Creativity and Innovation in Design Education and Research." International Journal of Design and Allied Sciences 2, no. 2 (2023): 66–67. https://doi.org/10.5281/zenodo.10447507.

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<strong>Abstract:</strong> Artificial Intelligence (AI) is poised to revolutionize design education and research, opening up exciting possibilities for enhanced creativity and innovation. AI integration offers personalized learning experiences, virtual reality environments, intelligent design assistance, automated assessment, and data-driven insights. Adaptive learning systems, immersive VR/AR environments, and AI-powered design assistants can benefit us. AI enables deeper insights through data analysis and generative design techniques in design research. AI also enhances the user experience a

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Sriram, Ravichandra, Siva Sathya Sundaram, and S. LourduMarie Sophie. "Experimental evaluation of bidirectional encoder representations from transformers models for de-identification of clinical document images." IAES International Journal of Robotics and Automation (IJRA) 14, no. 2 (2025): 273. https://doi.org/10.11591/ijra.v14i2.pp273-280.

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Many health institutes maintain patients’ diagnosis and treatment reports as scanned images. For healthcare analytics and research, large volumes of digitally stored patient information have to be accessed, but the privacy requirements of protected health information (PHI) limit the research opportunities. Particularly in this artificial intelligence (AI) era, deep learning models require large datasets for training purposes, which hospitals cannot share unless the PHI fields are de-identified. Manual de-identification is beyond possible, with millions of patient records generated in hospitals

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Németh, Renáta, Annamária Tátrai, Miklós Szabó, and Árpád Tamási. "Using a RAG-enhanced large language model in a virtual teaching assistant role: Experiences from a pilot project in statistics education." Hungarian Statistical Review 7, no. 2 (2024): 3–27. https://doi.org/10.35618/hsr2024.02.en003.

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The role of artificial intelligence (AI) in education is expected to grow, but how it transforms teaching and learning remains unclear. This study explores the use of an AI tutor that is similar to ChatGPT enhanced with retrieval-augmented generation (RAG), in a pilot project at the Faculty of Social Sciences of Eötvös Loránd University in Budapest, Hungary. The tutor provided a searchable knowledge base for students preparing for admission to the MSc in Survey Statistics and Data Analytics. Instructor feedback highlighted the tutor’s ability to deliver accurate, textbook-based responses, but

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Prof. Jyoti Gaikwad, Aniket Manohare, Shweta Munde, Anwar Shaikh, and Diksha Subhedar. "AI-Based Exploratory Data Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3876–84. https://doi.org/10.32628/cseit25112860.

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In today's world, where data is being generated at an unprecedented rate, organizations often struggle to make sense of the vast and complex information they collect. Extracting valuable insights from such massive datasets has become a major challenge. Traditionally, Exploratory Data Analysis (EDA) has relied on statistical techniques and manual processes. While effective, these methods can be slow, tedious, and difficult to scale when dealing with big data. This paper explores how Artificial Intelligence (AI) is transforming the way we approach EDA. By integrating AI technologies, such as Mac

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Singu, Santosh Kumar. "A Comprehensive Approach to Machine Learning Integration in Data Warehousing." Journal of Technology and Systems 6, no. 6 (2024): 28–37. http://dx.doi.org/10.47941/jts.2239.

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Purpose: This research examines the utilization of machine learning (ML) in data warehousing systems and the extent to which it will transform business intelligence and analytics. It aims to know how ML improves conventional data warehousing systems to support prediction and forecasting. Methodology: This research uses a literature review together with a case analysis. It discusses the issues that may arise when implementing Machine Learning models with data warehouses, such as issues to do with data quality, scalability, and real-time processing. The work examines integration patterns like in

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Ganesh, Shankar Gowekar. "How oil and gas industry are transforming with AI and ML." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1234–38. https://doi.org/10.5281/zenodo.14942924.

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The Oil and Gas Industry is witnessing a major transformation due to the advent of Artificial Intelligence (AI) and Machine Learning (ML). These advances are changing operations by making things work better, cheaper and safer.  Companies are using Artificial intelligence and Machine Learning to analyze large data points generated due to exploration, production and distribution activities which generate actionable insights leading to data driven decision making. AI and ML techniques for exploration, well placement optimization such as mount point selection or drilling parameters suggest ba

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Hussain, Sarwat. "Artificial Intelligence, the Need of the Hour." esculapio 17, no. 1 (2021): 1–2. http://dx.doi.org/10.51273/esc21.2517122.

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Fourth Industrial revolution is currently sweeping the high-income countries (HIC) with Artificial Intelli- gence (AI) based automation affecting virtually every aspect of life. The term AI was first coined by McCar- thy in 1956. It was not until 2000s that AI began to thrive. The evolution of AI into the current status occurred in the last decade owing to the enhanced computing power using Graphic Processing Units (GPU), development of high-powered computer languages, and the emergence of the Big Data. The latter is generated through wireless communication between ‘Smart’ sensors/devices and

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Nneka Adaobi Ochuba, David Olanrewaju Olutimehin, Olusegun Gbenga Odunaiya, and Oluwatobi Timothy Soyombo. "Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments." Magna Scientia Advanced Research and Reviews 10, no. 2 (2024): 111–19. http://dx.doi.org/10.30574/msarr.2024.10.2.0048.

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The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure opti

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Sunil Kumar Mishra. "Predicting Human Resource Trends in Technical Education Through ERP Data and Machine Learning Models." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 830–51. https://doi.org/10.52783/jisem.v10i23s.3785.

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Enterprise Resource Planning (ERP) systems have become integral in streamlining academic and administrative processes in technical education institutions. However, their impact on human resource (HR) trends, including faculty performance, student outcomes, and job placements, remains underexplored. This study leverages ERP-generated data to predict HR trends in Odisha’s technical education sector using machine learning models. The dataset comprises institutional records, faculty evaluations, student performance metrics, and employment statistics collected from ERP systems. We employ Random For

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Sidddiky Pinky, Aysha. "Artificial Intelligence and Business Transition: Paving the Way for Development." Westcliff International Journal of Applied Research 9, no. 1 (2025): 56–63. https://doi.org/10.47670//wuwijar20251asp.

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Artificial Intelligence (AI) is steadily becoming the new normal in doing business through increasing operational performance, improving customer relations, and increasing predictive accuracy. This quantitative exploratory research employed a mixed-methods approach, integrating qualitative insights into organizational trends, best practices, and challenges with quantitative assessments of performance measures, cost savings, and business outcomes. Several surveys were administered to a diverse group of business professionals. The study, situated within the field of applied research, explores ho

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Sandeep Kulkarni, Prathmesh Rahul kurumkar, Vansh Sanjeev Kadam, and Vinut Prabhu Maradur. "AI-powered resume screening system." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2263–77. https://doi.org/10.30574/wjaets.2025.15.1.0413.

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The advent of artificial intelligence (AI) has revolutionized talent acquisition through the development of AI-powered resume screening systems. These advanced tools utilize machine learning, natural language processing and data analytics to automate the initial evaluation of job applicants’ resumes, significantly enhancing the efficiency and objectivity of the hiring process. By analyzing key elements such as skills, experience, education and job-specific keywords, these systems filter and rank candidates, delivering a shortlist of top matches to recruiters. This technology reduces manual eff

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