Intelligent Technology Research Articles

Policy analysis combining artificial intelligence and text mining technology in the perspective of educational informatization

Policy analysis combining artificial intelligence and text mining technology in the perspective of educational informatization

Disciplinary differences in undergraduate students' engagement with generative artificial intelligence

AbstractThe rapid development of generative artificial intelligence (GenAI) technologies has sparked widespread discussions about their potential applications in higher education. However, little is known about how students from various disciplines engage with GenAI tools. This study explores undergraduate students' GenAI knowledge, usage intentions, and task-specific engagement across academic disciplines. Using a disciplinary categorization framework, we examine how the hard/soft and pure/applied dimensions relate to students' interactions with GenAI. We surveyed 193 undergraduates from diverse disciplines at a university in Singapore. The questionnaire assessed students' GenAI knowledge, usage intentions, and engagement with GenAI for cognitive and routine tasks against their disciplinary background. The results indicate substantial disciplinary disparities in the level of engagement of students with GenAI. Compared to pure fields, applied fields (both hard and soft) consistently exhibit higher levels of GenAI knowledge and utilization intentions. Furthermore, the engagement of GenAI in routine tasks is relatively consistent across disciplines; however, there are substantial disparities in cognitive tasks, with applied fields exhibiting higher engagement. These results suggest that the practical orientation of applied fields drives GenAI adoption and utilization in academic settings. The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.

Harnessing Artificial Intelligence (AI) and Blockchain Technology for the Advancement of Finance Technology (FinTech) in Businesses

This brief investigation seeks to explore the significant impact and offer a thorough analysis of the transformative potential of artificial intelligence (AI) and blockchain technology in revolutionizing financial technologies (FinTech) within the context of African business environments. Central to this study is an examination of how AI-driven solutions and blockchain technology can improve efficiency, accessibility, and innovation in financial services. Currently, African businesses face numerous challenges that hinder their overall success. Among these are the prevalence of fraud in Africa’s economy and the absence of credit scoring and reliable risk management, leading to bad debts and poor capital management. The businesses also lack data infrastructure, regulatory compliances, customer engagement and personalisation data privacy, cybersecurity and volatile currencies. Through intensive evaluation, we will be venturing into and showcasing how applications of AI and blockchain technology address these hurdles via techniques involving fraud detection, risk mitigation, advanced credit assessments and scoring for underserved communities, loan origination systems, and automated customer support utilising tools like chatbots, bolstered market analytics, streamlined regulation conformance, and augmented financial inclusivity encompassing digital payment platforms, saving mechanisms, loans, investments, and insurance products. By leveraging AI, African businesses can propel FinTech innovation and progression, foster economic prosperity, and drive sustainable development. The paper comprehensively analyses the current state of AI-driven FinTech solutions in Africa, identifying key factors that can unlock a new era of financial innovation, potential challenges businesses may face in implementing these technologies and our comprehensive research on their solutions.

Moving towards the use of artificial intelligence in pain management

AbstractBackground and ObjectiveWhile the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.Databases and Data TreatmentThis review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.ResultsFrom the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image‐guidance for procedural interventions and self‐management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.ConclusionThere is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.SignificanceThis review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Applications of integrating artificial intelligence and big data: A comprehensive analysis

Abstract The integration of artificial intelligence (AI) and big data technologies has the potential to revolutionize various industries, yet there are complexities and challenges associated with their implementation. This comprehensive study aims to investigate the combined impact of AI and big data on operational efficiency, precision, and security across multiple sectors. By utilizing a methodological analysis of 105 peer-reviewed articles sourced from reputable databases, we systematically explore the diverse applications, key innovations, and transformative potential of these technologies. Our findings uncover significant advancements in healthcare diagnostics, drug discovery, personalized education, and smart farming, highlighting how AI enhances big data analytics to drive notable improvements. Specifically, the study reveals the accuracy of AI in healthcare diagnostics, the efficiency of big data in drug discovery, the personalization of learning experiences through AI in education, and the sustainability advancements in agriculture through smart farming. These results underscore a substantial shift toward more sophisticated data-driven decision-making and operational processes facilitated by the integration of AI and big data. This shift addresses the initial research problem and makes a significant contribution to both academic and practical understanding of the role these technologies play in shaping the future of industry operations. The study concludes that while AI and big data integration offers substantial benefits, addressing associated challenges is crucial for maximizing their impact.

Despite Contradictions, Artificial Intelligence and Block Chain Technology are Composing the Future Together

Now times, Block Chain technology and enhancements in Artificial Intelligence paradigm are facing more attention in the field of research with new advancements in many security techniques in all the sectors of IT and business. One cannot deny from the fact that the adoption of Artificial Intelligence paradigms and block-chain technology is shaping the new face of market. Beyond their contradictions both are proceeding at lightning speed. Both of them are aimed at providing something new to the world but the degree of complexity of each is quite different. With a secure, decentralized, and trustworthy system, block-chain technology did automate the bit-coin payments and provide the users an access to a shared ledger of records, transactions and data. With the useof smart contracts, a block-chain can also regulate the user interactions without the need for any central authority. In the contradiction, AI provides agents with the ability of reasoning, decision making and human-level intellect. At bottom one can say, block chain is concerned with keeping correct records, authentication and implementation while AI deals in conducting assessments, examining, and coming to a decision analyzing certain patterns and datasets, eventually giving rise to a self- directed interaction. Artificial Intelligence along with block chain allocates several descriptions which will make a seamless communication prospect certainly. AI and Block-Chain need sharing of data. The decentralized approach of database focuses on the significance of data sharing among various clients on a meticulous network. In the same way, AI depends very much on Big-Data, exclusively, for data distribution. The first section in the proposed article offers the Introduction part. The second section focuses on the majorly emphasized topics and domains about block chain technology indulging Artificial Intelligence into it, presents a detailed literature-review of many of the contributions by eminent research analysts. The third section describes the challenges in the collaborating AI with block-chain. The last section expresses a brief conclusion of the research work. Keywords- Block Chain Technology, Artificial Intelligence, AI, Machine Learning

Latent Information in the Evolving Energy Structure

Recent studies indicate that Artificial Intelligence (AI) technology, characterized by its integration of information and communication technology attributes, exerts a multifaceted influence on the energy system. The authors employ Difference-in-Differences (DID) and Triple-Difference (DDD) models to investigate the effects of AI. The research initially demonstrates that AI may exhibit dual attributes of constraining energy structure. Specifically, the rapid development of AI tends to increase the proportion of fossil fuel-based electricity generation while optimizing the consumption of renewable energy. Furthermore, the degradation of energy structure stems from the surge in electricity consumption by AI, which the renewable energy consumption capacity is unable to satisfy. Lastly, industrial agglomeration effects and the construction of the digital economy have positive impacts on development of renewable energy; technological innovation aids in mitigating the negative shocks to the energy structure. This study provides a new perspective on the role of AI technology in energy transition.

Towards smart and sustainable transportation: the role of artificial intelligence and new technologies in mitigating passenger car CO2 emissions in European countries

Towards smart and sustainable transportation: the role of artificial intelligence and new technologies in mitigating passenger car CO2 emissions in European countries

Using LLMs to support teacher reflections on using questions to deepen learning and promote student engagement

While much attention has been focused on improving student outcomes, there is growing interest in supporting teachers' reflection on their teaching practice using Generative Artificial Intelligence (GenAI) technologies. This paper examines the application of Large Language Models (LLMs) such as GPT-4 to automate the analysis of secondary school teachers' teaching practice in Singapore, specifically within the context of one of the teaching areas identified by the Singapore Teaching Practice model: using questions to deepen learning. We aimed to demonstrate the effectiveness of LLMs in analyzing classroom lessons in this teaching area. The methodologies employed in this study included the collection of classroom data and their analysis, both manually and using LLMs. Specifically, this involved transcribing the classroom lessons and analyzing each question using LLMs, with the results compared to a ground-truth dataset created through manual analysis. The findings suggest that LLMs are effective in providing, forming the basis for future teacher reflection and the potential for automated self-reflection tools in Singapore schools.

Revolutionizing Sports Education using AI & ML

In the realm of sports education and performance analysis, there exists a pressing need to overcome barriers such as geographical limitations, cost constraints, and time constraints inherent in traditional teaching methodologies. In response to these challenges, this paper presents a novel sports education system leveraging artificial intelligence (AI) technologies. This allinclusive system allows for easy access to sports academies and training facilities based on geographic proximity by introducing an innovative teaching platform driven by AI and GPS integration. Moreover, it offers an extensive library of sports laws, sophisticated strategies, and professional advice, encouraging ongoing skill development. Setting goals and closely observing performance are made easier by the system's integration of AI-driven forecasts, such as victory probabilities. The central aim of this initiative is to redefine the landscape of sports education by offering a convenient, cost-effective, and highly efficient solution accessible to individuals at all proficiency levels. In addition to its educational advantages, the system is poised to make significant contributions to sports analytics, supplying extensive data and insights for both researchers and practitioners. This proposal advocates a progressive approach that moves beyond traditional methods, heralding a new era in sports education and performance analytics

Implementation of Artificial Intelligence Technology-Based Learning Media at SD Negeri Kotagede 1 Yogyakarta

In the digital era, the field of education is witnessing the transformation that occurs through the integration of artificial intelligence (AI). With artificial intelligence's (AI) potential to revolutionize various sectors, it presents itself as a material or tool that can enhance the teaching and learning experience. Its features align with our thinking, enabling us to progress according to our desired content. Artificial intelligence (AI) is today's most important tool in education. Artificial intelligence (AI) plays a crucial role in enabling the use of AI-based learning media, thereby facilitating the learning process in education. Artificial intelligence (AI)--based learning media in education will aid teachers and students in learning. This study aims to provide insight to all of us regarding the implementation of learning media based on artificial intelligence (AI) technology at SD Negeri Kotagede 1 Yogyakarta. This study employs a descriptive-qualitative approach. In the current educational landscape, particularly at SD Negeri Kotagede 1 Yogyakarta, artificial intelligence (AI) plays a crucial role. Artificial intelligence (AI) can facilitate the learning process flow by using learning media based on artificial intelligence technology. Teachers can utilize artificial intelligence to design programs and learning processes, while students can enhance their comprehension of the material and cultivate their critical thinking skills. Implementing learning media based on artificial intelligence (AI) technology can benefit educational institutions in a more interesting, innovative, effective, and creative learning process.

Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance

Abstract. The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..

S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness and its relationship with other safety-critical factors, such as carbon emissions, S-RAF aids developers and stakeholders in building safe and responsible driving agents, and streamlining safety certification processes. Furthermore, S-RAF offers significant advantages, such as reduced testing costs, and the ability to explore edge cases that may be unsafe to test in the real world.

MeDi‐TODER: Medical Domain‐Incremental Task‐Oriented Dialogue Generator Using Experience Replay

ABSTRACTArtificial intelligence (AI) technology has brought groundbreaking changes to the healthcare domain. Specifically, the integration of a medical dialogue system (MDS) has facilitated interactions with patients, identifying meaningful information such as symptoms and medications from their dialogue history to generate appropriate responses. However, shortcomings arise when MDS lacks access to the patient's cumulative history or prior domain knowledge, resulting in the generation of inaccurate responses. To address this challenge, we propose a medical domain‐incremental task‐oriented dialogue generator using experience replay (MeDi‐TODER) that applies the continual learning technique to the medical task‐oriented dialogue generator. By strategically sampling and storing exemplars from previous domains and rehearsing it as it learns, the model effectively retains knowledge and can respond to the novel domains. Extensive experiments demonstrated that MeDi‐TODER significantly outperforms other models that lack continual learning in both natural language generation and natural language understanding. Notably, our proposed method achieves the highest scores, surpassing the upper‐class benchmarks.

The Frontier Exploration and Application Prospect of Data Science

Abstract. In the era of information explosion, data floods in like a relentless tide, shaping peoples world every minute and second. Data science, the most prominent discipline of our time, wields unprecedented power to propel the development of industries across the board, serving as a bridge that connects the past with the future, granting insights into the present, and predicting trends. This article delves into the allure of data science, unveiling how it has become the key to unlocking the future world. Furthermore, this review explores in detail the core concepts, key technologies, and broad applications of data science. Through in-depth research on data acquisition, processing, analysis, and visualization, it reveals the innovative applications and significant impacts of data science in multiple fields such as business intelligence, healthcare, and financial technology. At the same time, the future trends and challenges of data science are prospectively analyzed, which provides a valuable reference for further promoting the research and practice in this field.

Occupational therapy in the space of artificial intelligence: Ethical considerations and human-centered efforts

Background Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs. Objective To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT’s ethical values and beliefs. Results Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data. Conclusion and Significance There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.

Impacts of interacting with an AI chatbot on preservice teachers' responsive teaching skills in math education

AbstractBackgroundArtificial Intelligence (AI) technologies offer unique capabilities for preservice teachers (PSTs) to engage in authentic and real‐time interactions using natural language. However, the impact of AI technology on PSTs' responsive teaching skills remains uncertain.ObjectivesThe primary objective of this study is to examine whether interaction with a responsive AI‐based chatbot that acts as a virtual student improves pre‐service teachers' noticing abilities. The second objective is to compare how the presence or absence of chatbot responses affects changes in PSTs' questioning practices. Finally, the third objective is to investigate how the experience of interacting with the responsive virtual student affects PSTs' perceptions of the effectiveness of their questioning, their satisfaction with the interactions, and their confidence about interacting with a real student compared to the non‐responsive chatbot.MethodsA randomised controlled pre‐ and post‐test design was used with 50 PSTs. PSTs' noticing, interaction with the chatbot, and post‐survey data were collected, and a t‐test was conducted to examine significant differences by group.Results and ConclusionIn the experimental group, the virtual student responded to PSTs' questions, while in the control group, she did not. Notable differences were observed in their questioning practices.TakeawaysOverall, AI‐based chatbots hold promise for enhancing PSTs' responsive teaching skills. Future research is needed to examine the long‐term impact of responsive chatbot use on PSTs' noticing skills.

A Review of Data-Driven Intelligent Monitoring for Geological Drilling Processes

The exploration and development of resources and energy are fundamental to human survival and development, and geological drilling is a key method for deep resource and energy exploration. Intelligent monitoring technology can achieve anomaly detection, fault diagnosis, and fault prediction in the drilling process, which is crucial for ensuring production safety and improving drilling efficiency. The drilling process is characterized by complex geological conditions, variable working conditions, and low information value density, which pose a series of difficulties and challenges for intelligent monitoring. This paper reviews the research progress of the data-driven intelligent monitoring of geological drilling processes, focusing on the above difficulties and challenges. It mainly includes multivariate statistics, machine learning, and multi-model fusion. Multivariate statistical methods can effectively handle and analyze complex geological drilling data, while machine learning methods can efficiently extract key patterns and trends from a large amount of geological drilling data. Multi-model fusion methods, by combining the advantages of the first two methods, enhance the ability to handle complex multivariable and nonlinear problems. This review shows that existing research still faces problems such as limited data processing capabilities and insufficient model generalization capabilities. Improving the efficiency of data processing and the generalization capability of models may be the main research directions in the future.

Teledentistry Integration: Revolutionizing Dental Insurance Through Technological Innovation

This article examines the transformative impact of teledentistry on the dental insurance industry, exploring how telecommunications technology is revolutionizing dental care delivery, access, and insurance. It discusses the expansion of dental care accessibility, streamlining of insurance processes, enhancement of preventive care, and the generation of data-driven insights through teledentistry. The article also addresses privacy and security considerations, presents case studies of successful implementations, and explores future prospects and innovations in the field. By analyzing the integration of teledentistry with artificial intelligence and wearable technology, the article highlights the potential for more proactive, personalized, and accessible dental care, while emphasizing the need for continued research and development to fully realize these benefits.

Implementation of Committee Partnerships on AI-Based School Policies at Public Junior High School

In the increasingly sophisticated digital era, artificial intelligence (AI) technology has significantly changed various fields, including education. In Indonesia, secondary schools have begun implementing AI technology to improve the quality of teaching and school management. Implementing this technology includes using AI-based systems for personalized learning, student data analysis, and more efficient school administration management. Implementing AI-based policies requires a strong partnership between the school committee and the school. This includes monitoring technology implementation, providing teacher training, and involving parents in the technology-based learning process. This study aims to identify and analyze the role of partnerships between school committees and schools in implementing AI-based policies at SMP Negeri 3 Tanjungsari. To ensure effective policy implementation, various stakeholders, including school committees, must support advancing AI technology in education. This study employs a qualitative approach, incorporating a case study method. It obtained the collected data through in-depth interviews, participant observation, and document analysis. The study results indicate that strong partnerships between school committees and schools play a significant role in supporting the implementation of AI policies. However, some challenges are still faced, such as the committee's lack of understanding of AI technology. This study also offers strategies to improve the effectiveness of these partnerships, including training and enhancing communication between the committee and the school. These findings significantly contribute to developing educational policies that are more adaptive to technological advances.