AI Learning Research Articles

Integrating unplugged and plugged activities for holistic AI education: An embodied constructionist pedagogical approach

AbstractThere is a growing consensus that AI literacy requires a holistic lens, including not only technical knowledge and skills but also social and ethical considerations. Yet, providing holistic AI education for upper-primary students remains challenging due to the abstract and complex nature of AI and a lack of pedagogical experiences in schools. Against this backdrop, this study employs a design-based research (DBR) methodology to develop pedagogical solutions and theoretical knowledge. Drawing upon theories about AI literacy and learning sciences, this study proposes a novel pedagogical approach, namely, the embodied constructionist approach. This approach, by integrating embodied analogies and constructionist making, forges a cyclical understand–make–reflect process. Through this process, students gradually comprehend the conceptual, technical, and ethical dimensions of AI, towards holistic development in AI literacy. This approach was implemented and fine-tuned in authentic classrooms with 107 sixth graders over two years. During the implementation, a variety of qualitative data were collected from classroom observation, video-recording, student interviews and artifacts, and debriefings with the teacher. Using a case studies method, this study uncovered the overall learning progress demonstrated by the class in AI knowledge, skills, and ethical and critical reflections, as well as the reflections and struggles experienced by individual students. This study offers an age-appropriate pedagogy to promote inclusive, holistic AI education in the crucial, yet under-examined upper primary context. It highlights the potential of integrating AI literacy and computational thinking (CT) in K-12 education, while contributing to theoretical knowledge about embodied cognition and constructionism in the context of AI education.

Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges

Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges

The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications

The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed "Architectural Design Process-Cognitive Pilot Map" from an architectural perspective to aid AI programmers in developing architecture design software applications.

Redesigning an AI bill of rights with/for young people: Principles for exploring AI ethics with middle and high school students

“AI ethics” has become a popular term for signaling concerns with the development and application of artificial intelligence, and a hot-button issue in both academic and public discourse. The current work tends to center adult-oriented, adult-generated, and issues-based conceptions of ethics. This paper reports on an alternative model for “doing ethics” via co-design with young people, highlighting the need for students to engage in AI ethics work, especially as AI is increasingly used in their education and social lives. In particular, we focus on developing a rights-based approach, using the White House Blueprint for an AI Bill of Rights as a starting point for redesigning core AI ethics principles and their conveyance to be accessible and relevant to young people, in collaboration with young people. In this manuscript, we describe our empirical work in a series of three studies on AI ethics teaching and learning with middle and high school students, which culminated in redesigning pieces of the AI Bill of Rights. We present lessons learned, implications for responsible AI in education from student perspectives, and conclude with our suggested principles for integrating student insight and perspectives into the ethics frameworks that guide AI applications.

A facial and vocal emotion recognition system based on deep learning

The traditional teaching mode, mainly characterized by "cramming" instruction, often overlooks the absorption of knowledge by students during the teaching process. Even in recent years, the emerging "flipped classroom" model heavily relies on teachers' experience and expertise, making it challenging to quantify students' classroom learning states. Among young teachers lacking teaching experience, the replication of excellent teaching models faces difficulties. With the rapid development of artificial intelligence technology, deep learning and other AI techniques are increasingly applied to people's lives. Additionally, emotion computing technology is also becoming more sophisticated. In the field of education, the use of AI technology holds the potential to help teachers systematically and comprehensively assess teaching quality, promoting the sharing of high-quality educational resources nationally and globally. Existing emotion detection technologies often focus on single techniques such as image recognition or natural language processing, resulting in low accuracy in discerning human emotions and making it difficult to accurately identify emotional changes in complex scenarios such as classrooms. Furthermore, there is a lack of comprehensive emotional statistical analysis methods for audio and visual data. In light of these, we have designed and implemented a multi-modal emotion recognition and analysis system. Through testing in classroom settings, this system has the potential to assist classroom teaching and enhance teaching quality.

Scholarly apps: enhancing research mobility and accessibility

Purpose This paper aims to explore how scholarly apps are changing the landscape of academic research by increasing mobility, accessibility and user engagement. It assesses the role of mobile-based applications in supporting research activities, enabling access to scholarly content and facilitating seamless communication between researchers and libraries. Design/methodology/approach A brief content analysis of prominent scholarly apps was performed to evaluate their prime features and usability. Findings Scholarly apps improve research mobility by offering on-the-go access to academic resources, databases, communication tools, personalized content recommendations and integrated automated citation tools. The integration of AI and machine learning into these apps holds potential for further personalizing and streamlining research processes. Research limitations/implications Although certain discussed mobile apps are well known and frequently used by users, their value in the field of mobile technology remains significant. Despite their vast popularity, these apps continue to play a significant part in the mobile app ecosystem due to their proven efficacy, widespread acceptance and continuous relevance in meeting user demands. Originality/value To the best of the authors’ knowledge, this is one of the first studies to comprehensively evaluate the scholarly apps and contributes to the literature on mobile-based library services.

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

Machine Learning Algorithm for Enhanced Cybersecurity: Identification and Mitigation of Emerging Threats

Machine learning (ML) methodologies have significantly transformed cybersecurity by offering sophisticated instruments that not only identify but also avert and alleviate cyber threats. This research study seeks to examine the convergence of machine learning and cybersecurity, focusing on diverse methodologies and their use in enhancing cybersecurity measures. The study examines several machines learning methods, including Graph Neural Networks, Adversarial Learning, Federated Learning, Explainable AI, and Reinforcement Learning. Every algorithm is essential for enhancing the identification and mitigation of cyber assaults. Graph Neural Networks facilitate the modelling of intricate linkages within cybersecurity data. It aids not just in forecasting future events but also in identifying anomalies and analyzing network traffic. Adversarial Learning assists in training machine learning models to address the difficulty of producing misleading input data that can deceive any model, hence enhancing their efficacy. Federated Learning is examined as a method for training machine learning models across decentralized networks while preserving data privacy and enhancing model accuracy. Explainable AI methodologies primarily offer transparency and interpretability in machine learning-driven cybersecurity decisions, which are crucial for comprehension and confidence in automated security systems. Reinforcement Learning is focused on a trial-and-error methodology, wherein the model acquires new tasks through a system of rewards and penalties. These sophisticated algorithms jointly improve the effectiveness, precision, and clarity of cybersecurity protocols, offering strong protection against emerging cyber threats.

Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning

AbstractThis study aimed to investigate how AI impacts students’ intrinsic motivation and learning experiences in various cultural and academic settings. An analysis of the effects of AI-powered learning tools on intrinsic motivation and learning enhancement was conducted in early 2024. The study recruited 455 students (192 males and 263 females) from Egypt, Saudi Arabia, Spain, and Poland who were studying diverse majors in educational faculties. Using a 5-point Likert scale, the validated instrument included three dimensions with a total of 10 items: intrinsic motivation and AI, AI and enhancing learning, and overall perception of AI in education. Significant differences were found by nationality and major, with Polish students and education technology majors showing the highest motivation levels. As a result of enhancing learning experiences, autonomy, and critical thinking skills, AI tools were found to positively influence motivation. Motivation, however, was not significantly influenced by academic level. Despite ethical concerns, students held a moderately positive view of artificial intelligence in education. Further research on optimal AI implementation and ethical considerations in education is necessary based on these findings.

Perceptions of Women’s Safety in Transient Environments and the Potential Role of AI in Enhancing Safety: An Inclusive Mobility Study in India

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning.

Enhancing Quality Assurance in Annuities: A Risk Management Approach with AI and Machine Learning

Enhancing Quality Assurance in Annuities: A Risk Management Approach with AI and Machine Learning

Jurisprudential Rulings Relevant to the Use of Artificial Intelligence in the Medical Field

*This paper of research is sponsored and funded by Kuwait University, Research Project No. (HC03/23) Idea of the Research is to address the topic of using artificial intelligence in the medical field from the Islamic point of view. Its importance lies in focusing on the way Islamic Law keeps pace with latest issues that comply with the provisions and objectives of Islamic Law. Hence, the problematic deals with jurisprudential rulings relevant to the use of AI in the medical field and providing patients’ confidential data to AI software. This research aims to explain some jurisprudential rulings on using AI. Accordingly, extrapolatory, analytical, and comparative methods have been used. The main findings of the research are the following: learning AI is a collective duty provided that it agrees with the Objectives of Islamic Law that firmly stipulates non-disclosure of patients’ medical data except in a few cases to bring benefit or prevent harm. On this basis, practitioners can provide AI software with confidential patients’ information under certain conditions such as achieving public interest, obtaining permission and a written consent from the patient, revealing true information and preventing any kind of harm. In this regard, the researcher urges to keep pace with the latest AI developments and encourages more jurisprudential studies to promote research on emerging medical issues.

Securing the IoT Edge Devices Using Advanced Digital Technologies

As the IoT ecosystem continues to grow, edge computing is becoming essential for handling and analyzing the vast amount of data generated by connected devices. Unlike traditional centralized data models, where information is sent to remote centers for processing, edge computing processes data closer to where it is generated. This decentralized approach helps reduce latency, optimizes bandwidth usage, and improves both privacy and security. However, the rise in IoT devices and the spread of edge computing also increase the potential for cyberattacks, demanding more robust security measures. With AI and machine learning being utilized to analyze IoT data, edge computing facilitates this analysis directly at the data source, pointing to a future where AI and ML applications are more prevalent on edge devices.

The Role of AI and Machine Learning in Optimizing Taxation Policies for Rural Development and Infrastructural Upgrades

The Role of AI and Machine Learning in Optimizing Taxation Policies for Rural Development and Infrastructural Upgrades

Current Understanding, Motivations, and Barriers Towards Implementing Sustainable Initiatives in the Hospitality Industry in the Age of Automation and Artificial Intelligence

Background:: Sustainability concerns are rapidly being acknowledged as a key concern for hospitality sectors worldwide. Sustainable initiatives immediately contribute to improved organizational performance in terms of utility consumption, waste management, and regulatory compliance, resulting in cost-effectiveness and competitive advantage through distinctiveness. Objective:: The purpose of the study is to analyze and summarize the motivations, indicators, and barriers towards applications of sustainable initiatives and modern technologies in the hospitality industry using the existing literature to develop a current understanding of the subject and know the way the current industry is thinking about it. Method:: This study is a combination of systematic and bibliometric review, where the systematic review was based on selected articles from reputed journal databases, and the bibliometric review was conducted using VOS viewer and web of science database for a period of 20 years (2002- 2022) Seven research questions were framed and answered for the systematic review. Result:: By describing the motivations, barriers, and impacts of implementing sustainability initiatives and cutting-edge technologies like AI and machine learning in the hospitality sector, the study helps practitioners and academics understand its present state for robust research. The current condition of such implantations in the hospitality sector is also discussed. Conclusion:: This study adds value by shedding light on the perspective of sustainability in the hospitality industry by considering the recommendations and practical advice for hotel management suggested in the existing literature about the application of current sustainability innovations and effective sustainability initiatives in hotel management.

Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies

In response to increasing environmental demands, modeling emissions from older vehicles presents a significant challenge. This paper introduces an innovative methodology that takes advantage of advanced AI and machine learning techniques to develop precise emission models for older vehicles. This study analyzed data from road tests and the OBDII diagnostic interface, focusing on CO2, CO, THC, and NOx emissions under both cold and warm engine conditions. The key results showed that random forest regression provided the best predictions for THC in a cold engine (R2: 0.76), while polynomial regression excelled for CO2 (R2: 0.93). For warm engines, polynomial regression performed best for CO2 (R2: 0.95), and gradient boosting delivered results for THC (R2: 0.66). Although prediction accuracy varied by emission compound and engine state, the models consistently demonstrated high precision, offering a robust tool for managing emissions from aging vehicle fleets. These models offer valuable information for transportation policy and pollution reduction strategies, particularly in urban areas.

In the Pipeline: Recursion’s Approach to AI and Machine Learning

In the Pipeline: Recursion’s Approach to AI and Machine Learning

The Role of AI and Machine Learning in Fraud Detection: Enhancing Risk Management in Corporate Finance

The Role of AI and Machine Learning in Fraud Detection: Enhancing Risk Management in Corporate Finance

Recognizing Large‐Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network

ABSTRACTThe proliferation of Artificial Intelligence Generated Content (AIGC) poses significant challenges to user experience and information accuracy, especially on search engine websites(Guo et al., 2023). The current solution is to identify AIGC by machine learning algorithms or publicly available AI detection tools, whereas, machine learning(Wang & Wang, 2022) algorithms degrade in accuracy as more data is available and tools such as GPTZero perform poorly in the task of AIGC detection on social media. In this paper, we propose an EPCNN model to identify AIGC on search engine websites, which maintains good performance in large‐scale samples. The ERNIE model integrates cross‐domain knowledge and improves language understanding and generalization. We use ERNIE to extract text features, then use a feature pyramid network to capture semantic information at different levels, and finally use an end‐to‐end structure to connect ERNIE and the feature pyramid network to construct the EPCNN. Experimental results show that our proposed algorithm has high accuracy and the ability to handle large‐scale data compared with machine learning algorithms and AI detection tools.

Artificial General Intelligence (AGI): A Comprehensive Review

Artificial General Intelligence (AGI) represents a significant leap in the field of artificial intelligence, defined by its ability to perform any intellectual task that a human can. Unlike narrow AI, which is task-specific, AGI is characterized by versatility, adaptability, autonomy, and reasoning capabilities. This comprehensive review explores the defining features of AGI, including its cognitive flexibility and capacity for autonomous decision-making and problem-solving. The current state of AGI research is examined, highlighting key areas such as cognitive architectures, neuroscience-inspired AI, and machine learning and deep learning advancements. The review also addresses the technical challenges and ethical considerations associated with AGI development, including potential impacts on employment, privacy, and security, as well as the necessity for robust safety and control measures. Looking ahead, the potential benefits of AGI in various domains, such as healthcare, climate change, education, and economic growth, are discussed. Finally, the importance of establishing ethical frameworks and governance structures to guide AGI development and usage is emphasized. By addressing these challenges and fostering collaboration among scientists, policymakers, and society, AGI can be developed and deployed to benefit humanity as a whole.