AI Applications in Smart Mineral Processing: Ore Characterization, Sorting, and Efficiency

The recent mainstreaming of Artificial Intelligence (AI) applications and tools has significantly enhanced EFL educators’ research writing process. Nevertheless, few studies exist regarding how EFL educators understand AI applications and their integration into research writing processes and techniques. The current study aims to investigate the attitudes and perceptions of (n= 40) EFL teachers at Majmaah University concerning integrating AI tools in the Research writing process, by collecting data from mixed-methods source questionnaires and interviews. Thematic analysis was employed for qualitative data, while descriptive and inferential statistics through SPSS were used for quantitative data analysis. The questionnaire results demonstrated that most respondents have positive perceptions toward integrating AI tools, believing they can improve efficiency and quality in the research writing process. However, more familiarity with existing AI applications is needed to ensure this integration process. Moreover, the study affirms the significant impact of training and support on the effective integration of AI tools. Respondents strongly agree about the benefits of AI applications in streamlining literature reviews, aiding in data analysis, reducing errors, and enhancing overall language quality. Interview responses further emphasize the possible benefits of AI tools in the research writing process, highlighting efficiency gains, assistance in various writing tasks, and improved quality of EFL teachers’ research articles. All the participants stress the importance of ethical utilization, maintaining quality, and vigilance when integrating these tools into the research writing process.

Will ChatGPT undermine ethical values in nursing education, research, and practice?

In the era of artificial intelligence (AI), physical education (PE) teachers face unprecedented challenges in integrating advanced technologies into their teaching practices. This study aims to identify the specific challenges PE teachers encounter in the AI era and propose practical strategies to enhance the quality and relevance of school-based physical education. This study employed a multi-method approach, including literature review, mathematical statistics, questionnaire surveys, and expert interviews. The participants were 120 PE teachers from urban and rural schools across different regions. The study began with a literature review to establish a theoretical foundation. Questionnaires were used to gather teachers' perceptions of challenges related to AI integration, student engagement, and professional development. In-depth interviews with 25 PE experts provided deeper insights into their experiences and coping strategies. Data were analyzed using SPSS software. The results showed that 80% of PE teachers reported difficulties in integrating AI tools into their lessons, while 70% struggled with engaging students in technology-enhanced activities. Analysis of the questionnaire and interview data revealed three main challenges: (1) limited access to AI resources, (2) lack of training in AI applications, and (3) concerns about the impact of AI on traditional teaching methods. Teachers emphasized the need for continuous professional development and support to effectively utilize AI in teaching. The study concludes that PE teachers face significant challenges in adapting to the AI-driven educational environment, particularly in integrating AI tools and maintaining student engagement. These findings align with previous research on educational technology integration but highlight the unique concerns of PE teachers regarding the impact of AI on traditional teaching. This study proposes the following strategies: (1) increasing investment in AI tools and establishing resource-sharing platforms to improve access to AI resources; (2) providing continuous professional training in AI applications and integrating AI education into teacher development programs; (3) offering technical support for AI integration and encouraging teachers to use AI tools to optimize traditional teaching methods.

Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity profiles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artificial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artificial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artificial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

This chapter explores the practical applications of artificial intelligence (AI) tools in enhancing business efficiency, demonstrating how AI-driven solutions can streamline operations, optimize decision-making, and foster innovation across various industries. It provides a structured guide to understanding AI-powered business applications, including machine learning algorithms, predictive analytics, automation tools, and AI-driven customer engagement systems. Through real-world case studies, the chapter highlights how businesses of all sizes—from small startups to large corporations—can leverage AI to reduce costs, improve productivity, and gain competitive advantages. The discussion includes scalable AI solutions, debunking the myth that AI is only accessible to large enterprises. The chapter also examines sector-specific AI applications, showcasing AI’s impact in finance, healthcare, marketing, retail, manufacturing, and logistics. Additionally, this chapter addresses the ethical and societal implications of AI adoption, including privacy concerns, algorithmic bias, workforce transformation, and regulatory considerations. It provides a practical roadmap for AI implementation, guiding business owners in identifying the right AI tools, overcoming challenges, and ensuring responsible AI integration. By the end of this chapter, readers will have a clear understanding of the AI landscape, including how to strategically implement AI tools to maximize business efficiency while maintaining ethical integrity.

This research paper will present how advances in robotics and artificial intelligence affect economic and social transformation. Based on the review of the latest literature and research conducted in practice, the relationship between artificial intelligence (AI) and robots will be evolved due to rapid technological development and the increasing application of methods, techniques and tools of artificial intelligence in various spheres of life and work. Starting with a clear definition and tracing the evolution of artificial intelligence in robotics, the paper will highlight its role, importance and application in various fields. Studies show that artificial intelligence and robotics offer great opportunities for improving life and work, improving efficiency and precision, reducing costs, errors, etc. In this context, this research study presents a critical assessment of the application of the phenomenon of artificial intelligence and robots, based on successive studies of relevant literature, evaluating artificial intelligence and robots, their development, characteristic forms and application in everyday life. Due to their capabilities, robots with elements of artificial intelligence have successfully replaced humans in performing many difficult and physically demanding and dangerous jobs, as well as in mentally simple and repetitive activities, which certainly justifies their humane application. Practice indicates that the process of implementing artificial intelligence and robots requires an adequate ICT infrastructure, followed by specialized training of key users, as well as compliance with all ethical rules of use. Research results indicate that robotics and artificial intelligence have become key players in the transformation of the world, opening the door to new ways of working and optimizing existing business processes. In this sense, the application of artificial intelligence and robots opens the door to new technological innovations and potential opportunities for the improvement of the product and service sectors. Considering the topicality of the topic, there are still insufficient studies dealing with the application of artificial intelligence (AI) in robotics by researching the integration, progress and implications of intelligent robotic systems based on artificial intelligence. Practice shows that in addition to all the benefits, one should be extremely careful and not ignore the criticism of experts in this field about the exponential development of robotics and artificial intelligence and the present fear that robots will one day become a threat to human society.

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

BackgroundThe therapeutic relationship is a professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. The integration of artificial intelligence (AI) into mental health care has the potential to support accessibility and personalized care; however, little is known about how AI might affect the dynamics of the therapeutic relationship.ObjectiveThis study aimed to ascertain how physicians anticipate AI tools will impact the therapeutic relationship in mental health care.MethodsWe conducted 42 in-depth interviews with psychiatrists and family medicine practitioners to investigate physician perceptions regarding the impact of AI on mental health care.ResultsPhysicians identified several disruptions from AI use, noting that these tools could impact the dyad of the patient-physician relationship in ways that are both positive and negative. The main themes that emerged included potential disruptions to the therapeutic relationship, shifts in shared decision-making dynamics, and the importance of transparent AI use. Participants suggested that AI tools could create efficiencies that allow for relationship building as well as help avoid issues with miscommunication during psychotherapeutic interactions. However, they also expressed concerns that AI tools might not adequately capture aspects of the therapeutic relationship, such as empathy, that are vital to mental health care. Physicians also raised issues related to the impact that AI tools will have on maintaining relationships with patients.ConclusionsAs AI applications become increasingly integrated into mental health care, it is crucial to assess how this integration may support or disrupt the therapeutic relationship. Physician acceptance of emerging AI tools may be highly dependent on how well the human elements of mental health care are preserved.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

This study examined the effect of artificial intelligence (AI) applications on the efficiency of second-year students enrolled in the Bachelor of Science in Information and Communications Technology (BSICT) program. As AI tools were becoming increasingly integrated into educational settings, their impact on student productivity, learning retention, and engagement warranted investigation. The research employed a quantitative descriptive-correlational design, using a structured survey to gather data from 92 students. The study focused on three key areas: code completion, code debugging, and task automation. Results showed that students perceived AI tools as significantly helpful in enhancing coding speed, reducing manual workload, and improving overall academic performance. However, findings also indicated a potential risk of over-reliance on these tools, which could affect the development of critical thinking and problem-solving skills. The study concluded that AI applications can serve as valuable educational aids when integrated thoughtfully, supporting both learning efficiency and skill development. Recommendations included guided usage strategies for students and educators to ensure balanced learning

The widespread use and adoption of Artificial Intelligence (AI) applications among university students has drastically transformed the educational landscape. Recognizing the importance of this transformation, this study aims to investigate the factors affecting the adoption and use of AI applications among Pakistani research scholars. This study used an extended version of the unified theory of acceptance and use of the technology model and innovative resistance theory. The data were collected from 235 research scholars through a questionnaire. Descriptive statistics and a multiple linear regression test were used to analyze the collected data. The study found that Pakistani research scholars used various AI applications for research purposes such as ChatGPT, Grammarly, ChatPDF, and SciSpace. This study found that personal innovativeness, performance expectancy, social influence, and trust significantly influence research scholars’ behavioral intention to use AI applications. In contrast, the impact of effort expectancy, facilitating conditions, and resistance to innovation on students’ behavioral intention to use AI tools was statistically insignificant. The findings offer actionable insights for educators, policymakers, and technology developers aiming to enhance AI adoption in higher education.

The paper presents the results of the research study conducted on students at the Faculty of Humanities and Social Sciences in Zagreb (FHSS), Croatia, at the end of 2024, focusing on their experiences with artificial intelligence (AI) tools, and its use in academic activities, students' trust in AI, and the future use of AI by students. While over 81 % of respondents reported using AI tools for study purposes, more than 89% did not receive any education about AI and expressed a need for such education. Generative AI tools for text creation are the most frequently used type of AI tools, but students still require additional knowledge to use them effectively. AI tools are also utilized for seeking advice, finding information, being available 24/7, and assisting in writing student papers, though not yet on a massive scale. Teachers remain the most trusted source for information and for recommending AI tools. Despite known shortcomings of AI tools such as content errors, potential costs and ethical or privacy concerns, students expressed their intention to continue using AI tools in the future.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.