KAIZEN IMPLEMENTATION METHOD OF “GREEN” PROJECT MANAGEMENT IN THE ORGANIZATION BASED ON LEARNING MODELS AND ARTIFICIAL INTELLIGENCE

The use of Artificial Intelligence or AI in learning as a form of integration between real and virtual world in the Society 5.0 Era presents challenges in the ethical aspect, such as forms of responsibility for use, data security issues, and plagiarism in the creation of works. The concept of ethical use of AI in learning is needed, included in the perception of Islamic Religious Education students as AI users in Islamic Education learning, will provide a broader perspective and could be associated with the context of Islamic Education. This research will explore the perception of Islamic Education (PAI) students about the ethics of using AI in learning based on phenomena at Islamic State University of Sultan Aji Muhammad Idris (UINSI) Samarinda. Descriptive qualitative research with phenomenological methods is used to answer the formulated objectives. Data collection uses interview techniques supported by surveys. Data analysis using the Miles, Huberman, and Saldaña models in the form of data condensation, data display, and conclusion drawing. The results of this study explain the perception of students in a neutral manner based on the phenomena experienced at UINSI Samarinda related to the concept of ethics in the use of AI in learning with the ethical limitations discussed as follows: 1) Absence of data privacy and security issues and their prevention; 2) Plagiarism avoidance that must be done because there is great potential that occurs at the location, and; 3) Responsibilities that are only carried out by some students.

The present and future of project management in pharmaceutical R&D

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.

The increasing prevalence of artificial intelligence (AI) in everyday life has intensified the emphasis on teaching AI literacy to children. However, there is no consensus on the specific knowledge and skills that constitute children’s AI literacy, resulting in varied AI learning materials for young people. We systematically searched for educational practices for children’s AI learning in both formal and informal settings and examined the AI learning content taught to children. Our findings led to the development of a holistic AI literacy framework for children, which contains three high-level dimensions and eight content areas of AI literacy: AI awareness (AI definition, AI application, and AI history), AI mechanics (AI input, learning procedure, and AI output), and AI impacts (AI implication and responsible practice). Theoretically, we contribute a research-based, comprehensive, and current framework for children’s AI literacy, advancing its conceptualization in early life stages. Practically, our framework can guide researchers and practitioners in promoting AI education for the next generation.

The title of this study is Hospitality Students' Perceptions of the Use of AI in English Language Learning at the Tourism Department of Kupang State Polytechnic. This study aims to determine the perceptions of hospitality students about the use of artificial intelligence (AI) in English language learning, as well as the impact of the use of AI. Student perceptions are divided into 2, namely positive and negative. Some of them think that the use of AI is very helpful for them in understanding and mastering 4 basic English skills, namely reading, listening, writing and speaking and can improve their learning experience through a more personal and adaptive approach, which allows them to learn according to their respective needs. There are also students who have negative opinions regarding the use of AI in the English language learning process. They feel that with the AI application, students will be more dependent on the application and no longer read existing modules or books related to their respective fields of science in this case related to the hospitality industry. They may also trust the application more than the lecturer/teacher who teaches. According to them, this needs to be addressed more carefully and thoroughly so as not to harm other parties, especially teaching staff or practitioners who are experts in the field of science, especially English. The impact of using AI is also divided into two, namely positive and negative. The positive impact is that AI can improve the quality of English learning, help them know which parts need to be improved and speed up the learning process. AI also allows learning that can be accessed anytime and anywhere or study everywhere, students are ready for the world of work because they believe that increasing English skills will better prepare them to face the challenges of the world of work, especially in the tourism industry in this case the increasingly global hospitality. The negative impact according to students is that this application can create dependence on technology so that they no longer learn independently and think logically/critically. Students will also interact less with fellow students and teachers because AI reduces the opportunity to practice language in real social interactions. Despite everything, AI applications actually bring new breakthroughs in the world of technology and various related fields, especially 1 https://mapindo.ejurnal.info/index.php/manajemen_pelayanan_hotel Vol. x, No.x; year education. This application can actually support learning. The use of AI applications appropriately can accelerate the education process.

In the wake of the fourth industrial revolution, artificial intelligence is gaining momentum and is widely applied in various aspects of life, particularly education. This study investigates the factors influencing students' use of artificial intelligence (AI) in learning, focusing on students at Ho Chi Minh City University of Industry. The research uses a combination of the technology acceptance model and the theory of planned behavior to examine the relationships between subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention. Combining these technological models brings new insights into the context of AI that can support or hinder user behavior through bias. The results were then analyzed based on the least squares linear structural model, with 390 students participating in the survey using the stratified sampling approach. The study found that perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning. Subjective norms also play an essential role in shaping students' image and intention to use AI. The research also highlights the importance of self-efficacy, perceived enjoyment, playfulness, output quality, result demonstrability, and job relevance in influencing students' perceptions and use of AI. The findings of this study underscore the need for educational institutions to create a supportive environment that encourages students to use AI in learning. In contrast, AI technology creators need to focus on simplifying the user experience to make AI tools more accessible and easy to use. These practical recommendations of the research can guide policy and design decisions in the field of educational technology. Finally, in place of a conclusion, the study also aims to open up further approaches for AI platforms in academia.

With the advent of the Fourth Industrial Revolution, artificial intelligence (AI) is heralding significant changes in industries and the economy. These changes transcend specific academic disciplines, positioning AI as essential knowledge for everyone. Consequently, the importance of formulating AI-related policies and education is increasingly emphasized worldwide. Additionally, as the significance of data-driven decision-making grows, the demand for skills such as data analysis, machine learning, and deep learning is rising.</br>Universities should teach these skills to students to equip them with practical abilities. However, most universities only distinguish between IT majors and non-majors, offering the same curriculum to both groups. This approach does not adequately consider students' diverse learning backgrounds and levels, leading to various problems and difficulties in course content and teaching methods.</br>To address these issues, it is crucial to consider students' diversity and provide them with the autonomy to choose courses that match their levels and interests. This selective, level-based learning approach is expected to result in high satisfaction with AI-related courses, regardless of prior AI learning experience.</br>In this study, we proposed a revised AI liberal arts curriculum comprising three detailed courses: AI and Data Foundations, AI and Machine Learning, and AI Development and Practice. This redesigned curriculum allows students to choose personalized learning pathways and implements an Engaged Learning (EL) model. The study analyzed satisfaction levels regarding course content, evaluation methods, and the EL model by dividing students into two groups based on their prior AI learning experience.</br>The findings confirmed that allowing students to select courses aligned with their levels and interests is more important than their prior AI knowledge. Additionally, implementing the Engaged Learning model proved effective for course delivery. This level-based AI liberal arts curriculum is expected to be extended and applied to other academic subjects as well.

PurposeTo support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.Design/methodology/approachJunior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.Findings(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.Originality/valueWithin SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

Artificial Intelligence (AI) has become an increasingly important field in the current era of digital transformation. Artificial Intelligence (AI), is a technology designed to make computer systems able to imitate human intellectual abilities. Artificial intelligence (AI) allows computers to learn from experience, identify patterns, make decisions, and complete complex tasks quickly and efficiently. Artificial Intelligence (AI) is able to connect every device, so that someone can automate all devices without having to be on site. More than that, currently there are many machines that can interpret certain conditions or events with the help of Artificial Intelligence (AI). The processes that occur in Artificial Intelligence (AI) include learning, reasoning, and self-correction. The implementation of AI in human workmanship is to obtain optimal performance results with fast processing times and maximum results. In today's modern era, artificial intelligence (AI) is very necessary to make activities easier, especially in rural areas. So that the village becomes a modern village and is more advanced with today's technology. Therefore, this Community Service was carried out in Cisadane Village, Kwandang District, North Gorontalo Regency, Gorontalo Province. In obtaining the data used were observation and literature study. This service activity is an effort to apply artificial intelligence (AI) in learning and teaching activities in Cisadane Village, Kwandang District, North Gorontalo Regency, Gorontalo Province. This effort was motivated by the lack of implementation and lack of human resources in artificial intelligence (AI) technology in Cisadane Gorontalo Village, which resulted in artificial intelligence (AI) technology in Cisadane Gorontalo Village not being optimal. As a form of the author's thinking, several efforts and breakthroughs are offered, namely; 1.) The role of Artificial Intelligence (AI) for the people of Cisadane Village in creating a modern and technology-literate Village, by making all activities in the village easier. 2.) Increasing Human Resources in utilizing Artificial Intelligence (AI) Technology, which previously did not know, became aware so that can advance the village. Keywords : Artificial Intelligence, AI, Cisadane Village, Community Service

Artificial Intelligence (AI) is emerging as a useful tool in helping to improve the quality of education. However, studies on student perceptions of the use of AI in learning English as a Second Language are scarce (ESL). The purpose of this study is to investigate the attitudes of Vinschool Ocean Park Grade 11 students on the employment of AI in ESL in terms of orientation, use, and impact. A qualitative-descriptive method was adopted to gain in-depth insights into the perceptions of Grade 11 students. The data was gathered through semi-structured interviews, and the transcribed data were subjected to content analysis. Findings suggested that selected Grade 11 learners have a moderate level of familiarity with AI in education. The learners want to know more and have some reservations regarding the use and implications of AI. They have used only a few AI applications in their learning of ESL, with varying expectations and satisfaction levels of the outcomes. In light of the results, this research recommends a broader integration of AI in the classroom, accompanied by adequate training to improve teacher and student understanding and usage. Future studies on AI in learning ESL should focus more on student perspectives to evaluate how AI can be used to enhance educational experiences.

BackgroundTo analyze medical students’ perceptions, trust, and attitudes toward artificial intelligence (AI) in medical education, and explore their willingness to integrate AI in learning and teaching practices.MethodsThis cross-sectional study was performed with undergraduate and postgraduate medical students from two medical universities in Beijing. Data were collected between October and early November 2024 via a self-designed questionnaire that covered seven main domains: Awareness of AI, Expectations and concerns about AI, Importance of AI in education, Potential challenges and risks of AI in education and learning, The role and potential of AI in education, Perceptions of generative AI, and Behavioral intentions and plans for AI use in medical education.ResultsA total of 586 students participated in the survey, 553 valid responses were collected, giving an effective response rate of 94.4%. The majority of participants reported familiarity with AI concepts, whereas only 43.5% had an understanding of AI applications specific to medical education. Postgraduate students exhibited significantly higher levels of awareness of AI tools in medical contexts compared with undergraduate students (p < 0.001). Gender differences were also observed, with male students showing more enthusiasm and higher engagement with AI technologies than female students (p < 0.001). Female students expressed greater concerns regarding privacy, data security, and potential ethical issues related to AI in medical education than male students (p < 0.05). Male students or postgraduate students showed stronger behavioral intentions to integrate AI tools in their future learning and teaching practices.ConclusionsMedical students exhibit optimistic yet cautious attitudes toward the application of AI in medical education. They acknowledge the potential of AI to enhance educational efficiency, but remain mindful of the associated privacy and ethical risks. Strengthening AI education and training and balancing technological advancements with ethical considerations will be crucial in facilitating the deep integration of AI in medical education.Trial registrationNot clinical trial.

The use of artificial intelligence (AI) is growing and requires greater attention in the field of education, particularly in developing nations. In these situations, the teachers' contributions to the classroom—their advanced and in-depth understanding of AI—are crucial. AI learning and teaching will lead to the development of more advanced educational systems that keep up with the times. Finding the factors that ultimately influence attitudes toward AI and, eventually, adoption of AI through the teaching and learning process is the goal of this study. While attitude toward AI acts as a mediator, AI readiness as a moderator, and actual learning and teaching of AI as an endogenous construct, exogenous constructs include self-transcendent goals, subjective norms, personal relevance, and confidence in AI. Based on the random sample procedure, 272 respondents from Bangladesh participated in the quantitative study. SMART PLS-4 software is used to apply Partial Least Square Equation Modeling (PLS-SEM) for data analysis and hypothesis verification. According to the findings, personal relevance, subjective norms and self- transcendent goals have a significant impact on attitudes towards AI, and attitudes toward AI have a significant and positive impact on actual teaching and learning of AI. The endogenous constructs are unaffected by AI readiness as a moderator. Finally, the results offer valuable insights for improving AI-based educational institutions.

Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula.

Using artificial intelligence (AI) in education, especially in English language learning and teaching has become an important role to keep up with the development of technology in education. In Vietnam, students face difficulties in learning English as a foreign language due to the lack of foreign language practice and communication. Therefore, the use of AI to facilitate language learning has brought a transformative change for both students and teachers, especially at tertiary level. The study aims to investigate both teachers’ and students’ perceptions toward the impact of using AI in English language learning (ELL). It also considered whether there were differences between non-English majors’ and English majors’ attitudes toward using AI in learning English language. To collect data, online close-ended questionanaires were posed to 10 teachers and 221 students (both English majors and non-English majors) from Lac Hong University (LHU). The findings showed that both teachers and students had positive perceptions toward the efficacy of AI in English language learning. Also, there were small differences between English majors and non-English majors about the effectiveness of AI, but both of them highly valued the irreplaceable role of teachers in their English language learning process. Finally, the study suggested teachers use AI in teaching practices and instruct their students to optimise the utilisation of AI for English language acquisition.

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment