Artificial Intelligence Learning Research Articles

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy

There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.

Application and Evaluation of the Effectiveness of Artificial Intelligence in Personalized Learning

The application of artificial intelligence (AI) in personalized learning has demonstrated remarkable advantages, including intelligent assessment, resource recommendation, and teaching optimization, effectively enhancing learning outcomes and engagement. However, this process has also revealed several issues. Firstly, technical challenges and limitations hinder AI's ability to fully adapt to each student's individual needs, requiring further improvements in algorithm accuracy and reliability. Secondly, data privacy and security issues have become increasingly prominent, necessitating robust measures to protect student data from misuse and leakage. Lastly, challenges in the educational system and mindset must be addressed, focusing on how to integrate AI technology into traditional education models while maintaining teaching quality. To address these issues, continuous research and development, enhanced data protection, and the updating of educational systems and mindsets are required to fully harness the potential of AI in personalized learning.

Intelligent aeration amount prediction control for wastewater treatment process based on recurrent neural network

The use of machine learning in artificial intelligence to solve industrial problems has been a current trend. Predictions can be made by machine learning algorithms. This greatly improves the efficiency and also the accuracy. With the development of industrialization, the pollution of water resources is becoming more and more serious. How to treat wastewater more cost-effectively to meet the discharge standards has become one of the most urgent problems in the world. In the Anaerobic-Anoxic-Aerobic (AAO) process, the key to reach the standard of sewage treatment with low cost and high efficiency lies in the aeration link. In this study, multiple machine learning methods are used for modeling, and proposed a method employing the long short-term memory (LSTM) neural network model for regression prediction addresses the need for a more accurate prediction approach. By utilizing data from the preceding 100 days, this model replaces manual, experience-based prediction methods, thereby mitigating energy consumption. This model is optimized by training and testing with real wastewater treatment plant data and continuously adjusting the parameters through multiple sets of experiments. This can make the real recorded aeration amount and the actual aeration amount infinitely close. Through the experimental comparison, it is found that the performance of the LSTM neural network model is better, with about 14% higher accuracy than the benchmark model.

A year of generative AI in English language teaching and learning - A case study

Artificial intelligence (AI) tools are becoming increasingly prevalent in various fields, including foreign language education. Understanding foreign language students’ and teachers’ perceptions, experiences, ideas, and suggestions is crucial for effectively integrating these tools into language learning. To this end, this case study examines the perceptions of 14 English as a Foreign Language instructors and 13 students regarding the integration of ChatGPT in a foreign language school over a year. Data were collected through focus groups and one-to-one interviews and analyzed using reflexive thematic analysis. The findings suggest that generative AI chatbots can enhance language skills, offer diverse learning materials, and foster inclusiveness and equity in foreign language learning. However, concerns were raised about plagiarism and potential negative impacts on students’ cognitive skills.

Evaluation of Problem Based Gamification Learning (PBGL) Model on Critical Thinking Ability with Artificial Intelligence Approach Integrated with ChatGPT API: An Experimental Study

The purpose of this research is to evaluate the effectiveness of Problem Based Gamification Learning (PBGL) model integrated with Artificial Intelligence (AI) through ChatGPT API in improving participants' critical thinking skills. The study used an experimental design with two groups, namely control and experimental, each consisting of 120 students (240 in total) from two countries (Timor Leste and Indonesia) studying basic Python programming. The main issues were complex system integration with ChatGPT API, technical challenges in AI implementation for automated feedback, and maintaining test consistency and fairness. Results showed the experimental group had a significant improvement in critical thinking skills, with an average score improvement of 23.57 points compared to 9.04 points in the control group, showing a mean difference of 14.53 points. Content Validity Ratio (CVR) testing showed good content validity with CVR values > 0.75 for all items. Construct Validity (CV) testing confirmed the instrument measured the desired construct with significant correlation coefficients. Test-retest Reliability testing showed excellent reliability with a Pearson correlation coefficient of 0.960. The findings confirm that the integration of AI in gamified learning can provide automatic feedback and additional relevant information, improving critical thinking and motivation of participants. The contribution of the research is the development and testing of an AI-supported PBGL model, which can serve as a reference in digital learning curriculum design and improve critical thinking skills. Practical implications show that the integration of AI in learning enriches the material and provides flexibility to participants and teachers. Overcoming the technical challenges and adaptability of AI models will be key to increasing the effectiveness of PBGL in various educational contexts in the future. This research provides new insights in the development of more effective and adaptive learning methods in the digital era.

Harnessing machine learning technique for improved detection and classification of heart failure

Artificial Intelligence (AI) performs exercises recently performed by people utilizing AI and profound learning, Right now simulated intelligence is changing cardiovascular medication identifying problems, therapeutics, risk appraisals, clinical consideration, and medication advancement. The death rates in medical clinics for patients with cardiovascular breakdown display a scope of 10.6% at 30 days, 23.0% at 1 year, and 43.3% at 5 years. Cardiovascular breakdown (HF) patients need customized restorative and careful treatment, in this way early finding is pivotal. The 85% precise Brain Organization (NN) archetypal made this conceivable. By applying our calculation, simulated intelligence can assist with examining crude cardiovascular imaging information from echocardiography, processed tomography, and heart attractive reverberation imaging and EKG accounts. Unpleasant Sets (RS) and strategic relapse (LR) choice trees to analyze congestive cardiovascular breakdown and computerized reasoning to identify future impermanence and destabilization incidents have further developed cardiac illness results. This examination inspects how computer- based intelligence has changed pretty much every area of HF determination, avoidance, and the executives

The Impact of Using AI in Learning on Understanding of Material by Young Students

This study aims to examine the impact of the use of artificial intelligence (AI) technology in learning on the understanding of material by young students. In today's digital era, AI is increasingly being adopted in educational environments, with the potential to personalize learning, increase student engagement, and accelerate the process of understanding material. This research method uses a quantitative approach with a quasi-experimental design. A total of 120 students from various study programs at a university in Indonesia became research respondents, who were divided into two groups: a group that used an AI-based learning platform and a group that used conventional learning methods. Data collection was carried out through pre-tests and post-tests to measure the level of understanding of the material, as well as surveys to evaluate the learning experience. The results of the study showed that the group of students who used AI technology in learning had a more significant increase in understanding the material compared to the control group. In addition, students who used AI reported a more interactive and motivating learning experience. However, this study also found challenges, such as excessive dependence on technology and disparities in access to adequate technological devices. Based on these findings, the study recommends the integration of AI into the education curriculum in a balanced manner, considering technical and pedagogical factors to maximize the potential of AI-based learning.

Forme Fruste Keratoconus Detection with OCT Corneal Topography Using Artificial Intelligence Algorithms.

Distinguishing early Keratoconus (KC) from normal corneas is challenging owing to their striking similarities. The aim of our study was to identify discriminating parameters to differentiate a normal cornea from a Form Fruste Keratoconus (FFKC) with the Swept-Source (SS) OCT-topography CASIA 2 (Tomey,Japan) using machine learning artificial intelligence algorithms. The study was monocentric, carried out in Bordeaux. This was a retrospective study, case control. Three groups were included: KC group (108 eyes), FFKC (88 eyes) and normal corneas (162 eyes).The data were analyzed and processed using the Dataiku data science platform. Machine learning models (Random Forest, Logistic Regression) were used to develop a multiclass classifier for automated early KC detection. The models were trained using a training database and tested using a test database. Then algorithms were compared to the Ectasia Screening Index (ESI), which is an OCT-topography inherent screening score for ectasia. The Logistic Regression (LR), and Random Forest (RF) detected FFKC with an AUC of 0,99, and 0,98 respectively. The sensitivities of LR (100%), RF (84%) were better than the ESI (28%) for the diagnosis of FFKC. However, ESI has a maximum specificity (100%) compared to the RL (100%) and 90% for RF. This study identified discriminating topographic parameters to be considered in refractive surgery screening on SS-OCT CASIA 2. We developed an algorithm capable of classifying normal eyes versus FFKC cases, with improved performance compared to the ESI score.

Improving The Effectiveness of Mathematics Learning Through Artificial Intelligence: Literature Review

Industrial Revolution 4.0 has fundamentally transformed the educational landscape. Rapid advances in technology, especially artificial intelligence (AI), demand significant curriculum adaptations. Mathematics, as the foundation of science, is the main focus in efforts to integrate AI into the learning process. This research aims to examine in depth the effect of applying AI in mathematics learning on students' learning capacity. Through a comprehensive literature study referring to various scientific journals and Google Scholar articles, this research analyzes various aspects related to the application of AI in mathematics learning. Analysis includes mapping trends in AI implementation, evaluating the effectiveness of various AI approaches, identifying factors that influence successful implementation, and exploring the positive and negative impacts of AI on the learning process. The findings of this research show that the application of AI in mathematics learning has enormous potential to increase student learning motivation, personalize learning, and deepen understanding of concepts. However, research also identifies a number of challenges, such as gaps in technology access, the need for appropriate pedagogical development, and the potential for AI to replace teachers' roles. Based on these findings, this research concludes that integrating AI in mathematics learning requires careful planning and strong collaboration between educators, technology developers, and policymakers. The implications of this research include the importance of flexible curriculum development, continuous teacher training, and the provision of adequate technological infrastructure.

Bridging academia and industry in the era of Industry 4.0 by means of the triple helix: The PLANET4 initiative

Practical Learning of Artificial Intelligence on the Edge of Industry 4.0 (PLANET4) is a cross-disciplinary initiative funded by the European Commission under the Erasmus+ program that embodies the triple helix model of collaboration between academia, industry, and administration. It aims to bridge the gap between academic teaching and practical applications in the context of Industry 4.0. PLANET4 focuses on developing hard skills in artificial intelligence, industrial Internet of things, and cloud and edge computing, along with the soft competencies required to manage changes in the industrial ecosystem. It involves three phases: (1) training needs analysis, taxonomy development, and workshop design; (2) collection of best practices and training material design; and (3) implementation of the PLANET4 learning course. This paper presents the materialization of these phases into a blended learning course, highlighting the integration of the triple helix model and the European Commission’s support for academia-industry collaboration with the aim of improving education quality and promoting Industry 4.0 innovation in Europe.

An international study presenting a federated learning AI platform for pediatric brain tumors

While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.

Using AI-generative tools in tertiary education: Reflections on their effectiveness in improving tertiary students’ English writing abilities

Artificial intelligence (AI) tools have become a popular topic in the education field. Most of the schools in Hong Kong focus on how to properly use AI software to help students’ learning experience. As this is still a relatively new device, the stance for most of the schools in Hong Kong is skeptical. This study aims to find out whether AL-Generative tools, such as Chatgpt, can help to improve students’ English writing skills in university. Interviews were used to find out students and teachers’ opinions towards using AI in writing. The results indicate that students find AI tools convenient for learning writing skills, but teachers are concerned that the feedback and examples provided by AI are too general and ambiguous. This study provides some interesting opinions from students and teachers about their experience in using AI in learning and writing, and this helps us to understand more about how to use AI effectively in the education sector.

The Role of Instructional Designers in the Integration of Generative Artificial Intelligence in Online and Blended Learning in Higher Education

The purpose of this exploratory research study was to examine the roles instructional designers (IDs) in higher education play in the integration of generative Artificial Intelligence (GenAI) into their organizations, and how they use GenAI technologies in their own professional practices. Data were collected from 15 participants in the southeastern United States (U.S.) in an ID role or with similar job titles (e.g., educational technologist). Using a general qualitative approach, semi-structured interviews were conducted in Zoom about IDs’ use and integration of GenAI. Our findings resulted in three primary themes related to IDs’ integration of GenAI in online and blended education: (a) use of GenAI for instructional design; (b) collaborative guidance for faculty integration of GenAI; (c) training, resources, and guidelines on the integration of GenAI. A common thread through all the themes and interviews was IDs’ conscientious and cautious approach and ethical concerns about GenAI integration. We unpack these themes while discussing the implications of IDs in higher education integrating GenAI to meet organizational, faculty, and student needs. We provide limitations and a rich discussion about the advancement of GenAI by IDs working in institutions of higher education.

Integrating Generative AI in University Teaching and Learning: A Model for Balanced Guidelines

The study proposes a balanced approach and flexible guidelines for incorporating generative artificial intelligence (AI) into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy. Building on Chan’s (2023) AI Ecological Education Policy Framework, the guidelines offer a suggestive frame of reference for faculty and students to integrate generative AI into their coursework. Furthermore, feedback from 118 students and 14 academics at a teacher education institution in the Philippines underscores the guidelines' potential benefits, concerns, usefulness, and necessity in their academic undertakings. While the policy may not cover every detail exhaustively, it seeks to provide practical and context-sensitive recommendations for ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Consequently, other higher education institutions in general, and academics in particular, may adopt and/or modify the guidelines to suit their positions, goals, needs, and directions.

A Study on the Role of Digital Transformation in Enhancing Bank Performance

This study investigates the role of digital transformation in enhancing the performance of banks, focusing on how technological advancements have reshaped the banking sector. The research explores various dimensions of digital transformation, including the adoption of digital banking platforms, artificial intelligence (AI), blockchain technology, and big data analytics. By analyzing secondary data from financial reports, industry studies, and case studies of banks that have undergone digital transformation, the study aims to assess the impact of these technologies on operational efficiency, customer experience, and financial performance. Key performance indicators such as cost- to-income ratios, transaction processing times, customer satisfaction levels, and risk management effectiveness are evaluated to understand the overall benefits of digital transformation. The findings reveal that digital transformation significantly enhances bank performance by improving operational efficiency, increasing customer engagement, and driving revenue growth. Specifically, the adoption of digital platforms has streamlined processes, reduced costs, and improved the accuracy of transactions. AI and machine learning have enabled more personalized customer service and effective risk management, while blockchain technology has enhanced transaction security and transparency. The research concludes with recommendations for banks to optimize their digital strategies and overcome these challenges, and suggests areas for further research to explore emerging trends and technologies in the banking sector. Keywords: Digital Transformation, Bank Performance, Financial Technology (Fintech) Artificial Intelligence (AI), Financial Performance Metrics, Technology Adoption

Recent advances and applications of artificial intelligence in 3D bioprinting.

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review.

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.

Advanced classification of drill core rock type and weathering grade using detection transformer-based artificial intelligence techniques

Rock classification plays a crucial role in various fields such as geology, engineering, and environmental studies. Employing deep learning AI (artificial intelligence) methods has a high potential to significantly improve the accuracy and efficiency of this task. The paper delves into the exploration of two cutting-edge AI techniques, namely Mask DINO and Mask R-CNN (convolutional neural network), as means to identify rock weathering grades and rock types. The results demonstrate that Mask DINO, which is a Detection Transformer (DETR), outperforms Mask R-CNN for the aforementioned purposes. Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection, as opposed to the Mask R-CNN’s f-1 scores of 84% and 75%, respectively. These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states. Although the study examines only two AI models, the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field. By incorporating these advanced AI techniques, logging personnel can obtain valuable references to aid their work, ultimately contributing to the advancement of geological and related fields.

The Application of Artificial Intelligence in Education – The Current State and Trends

The aim of this paper is to discuss the application of contemporary AI tools and technologies in education. The paper will mainly focus on a close examination of relevant academic literature as well as the current situation and methods of AI implementation in different sectors, finally, in education. Particularly, it was crucial that there were identified the major directions of applying AI in learning with an emphasis on learners and educators being the basic aspects of AI use within modern educational approaches. Secondly, some benefits and drawbacks concerning AI implementations in education supported by experiences from industry leaders and specific satisfaction measures among existing users, are analyzed. The most significant part covers integrating findings that show facts about artificial intelligence being used in education for 2024 like top online learning statistics for 2024, top AI statistics for 2024, and best AI plagiarism checkers for teachers in 2024 respectively. This includes evaluating teacher or instructor preparation skills to employ artificial intelligence (AI) tools and technology as well as ways students can learn how to use them without misuse towards quality education improvement.