Application of Artificial Intelligence in Employee Training and Development

We work in one of the few offices in the country dedicated to helping students (health professional, life sciences, population and social sciences) and postdocs navigate the job market. Our team designs career & professional development programs and resources, and offers 1:1 counseling support to graduate-level biomedical trainees. Feel free to ask us anything about how biomedical scientists can prepare and position themselves for the job market, how institutions can provide career development support to their PhD-level trainees, or strategies to improve the career prospects of the nation’s STEM PhD’s. More about our work: • The UCSF Office of Career & Professional Development • Motivating INformed Decisions Program • Graduate Student Internships for Career Exploration Answering your questions here today are: Laurence, Clement, PhD. Program Director, Academic Career Development Anna Correa, MS. Program Director, Clinical Careers Bill Lindstaedt, MS. Executive Director, Career Advancement, International and Postdoctoral Services Thi Nguyen, PhD. Program Director, Non-Academic Career Development Naledi Saul, MPM. Director, Office of Career and Professional Development Liz Silva, PhD. Program Director, Motivating INformed Decisions (MIND) Program Alexandra Schnoes, PhD. Program Manager, Graduate Student Internships for Career Exploration(GSICE) Program Claire Will, PhD. Program Director, Professional Skills Development We will be back at 1 pm ET (10 am PT, 5 pm UTC) to answer your questions, ask us anything! EDIT: Hello everyone! We’re all here and are reading your great questions. We’ll begin answering now! 2nd EDIT: Thanks everyone - these were amazing questions, and we had a lot of fun. Thanks for participating!

The advancement of artificial intelligence, which has remarkable applications in numerous industries, is a key area of worldwide science and technology development. Among them, the application of artificial intelligence in new drug research and development and auxiliary disease diagnosis has made such outstanding achievements. Therefore, this paper analyzes the development and application of AI in biomedicine and looks forward to its future development. The detailed review included examples of how artificial intelligence has been used in biological medicine, the research and development of new pharmaceuticals, the development of new drugs, and the diagnosis of auxiliary diseases. Finally, this paper summarizes and prospects the future development of biomedicine.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

With the continuous development of science and technology, exploration in the field of life sciences has become increasingly profound, encompassing the understanding of human beings themselves and the exploration of disease etiology and treatment methods.Artificial intelligence is defined as the intelligence exhibited by artificially created systems. The application of artificial intelligence is quite extensive and can replace humans in various aspects of work, significantly enhancing efficiency in daily life and work. Artificial intelligence has a dual nature, as it could provide convenience to human society but could also bring about various adverse effects.This paper summarizes the current applications of artificial intelligence in the field of life sciences. By analyzing the applications of artificial intelligence in areas such as gene editing, drug development, and bioinformatics, it demonstrates the potential of artificial intelligence in the field of life sciences. At the same time, this paper also points out the ethical, technical, social, and various challenges that the application of artificial intelligence technology in the field of life sciences faces, with the aim of providing reference for research in related fields.

Введение.Определена актуальность внедрения технологий искусственного интеллекта в Российской Федерации (ИИ-технологий). Установлено, что в нашей стране строительство значительно уступает другим отраслям экономики по использованию ИИ-технологий. Цель исследования – определение приоритетных сфер применения технологий искусственного интеллекта в строительном комплексе РФ. Методы. В ходе исследования использовались генеративные технологии, методы машинного обучения, статистического анализа, экспертных оценок. Результаты. Изучен отечественный и зарубежный опыт внедрения ИИ-технологий в строительстве; выявлены основные причины недостаточного развития этих технологий в строительстве в нашей стране; определены основные направления использования искусственного интеллекта в строительном комплексе РФ; оценены техническая возможность и экономическая целесообразность реализации ИИ-технологий в практической сфере строительства. В качестве основных причин слабого использования искусственного интеллекта в строительном комплексе Российской Федерации следует назвать ограниченное использование данных, отсутствие квалифицированных специалистов, высокую стоимость внедрения, сложности с интеграцией; регулирование и стандарты. Определены наиболее значимые факторы эффективного применения технологий искусственного интеллекта в строительной отрасли: качество данных; интеграция с существующими системами; обучение и поддержка; выбор правильного инструмента; регулярное обновление и улучшение. На основе проведенного анализа выявлены приоритетные направления внедрения ИИ-технологий в строительном комплексе РФ: искусственный интеллект в автоматизированном проектировании, управлении строительством, контроле качества строительных работ, прогнозировании рисков в строительстве, анализе данных, оптимизации ресурсов, обеспечении безопасности и экологической устойчивости. Вывод. Для ускорения внедрения технологий искусственного интеллекта в строительную отрасль России необходимо развивать инфраструктуру данных, обучать специалистов, создавать благоприятные условия для инвестиций и разрабатывать соответствующие регулятивные нормы и стандарты. Introduction. Artificial intelligence has recently found extensive application in the Russian Federation. However, it has been determined that the application of AI technology in the construction industry in our country lags much behind other economic sectors. The aim of research is to determine priority areas for the application of artificial intelligence in the construction industry of the Russian Federation. Materials and methods. The research made use of generative technologies, statistical analysis, machine learning, and expert evaluation methods. Research outcomes. As a result of research, the authors examined the experiences of both national and international construction companies utilising artificial intelligence. They also determined the primary applications of AI in the Russian Federation's construction industry and evaluated the viability of both technological and financial implementation of AI in construction. The following should be listed as the primary causes of insufficient application of artificial intelligence in the Russian Federation's construction industry: restricted data use, a shortage of qualified personnel, high implementation costs, challenges related to integration, regulations and standards. The following are the key elements for effective application of AI in the construction sector: high-quality data; integration with current systems; assistance and training; selection of the appropriate tools; and regular updates and enhancements. Based on the analysis, priority areas for the implementation of AI technologies in the Russian construction industry were identified: artificial intelligence in automated design, construction management, quality control of construction work, risk forecasting in construction, data analysis, resource optimization, ensuring safety and environmental sustainability. Conclusions. The development of data infrastructure, the training of experts, the creation of investment-friendly environments, and the development of suitable regulatory norms and standards are all essential to accelerate the adoption of artificial intelligence technologies in the Russian construction sector.

Veterinary hospitals play a vital role in ensuring animal health, but their success depends on skilled and motivated veterinarians. This research investigates how career development, training, and human resource planning influence veterinarian performance, with employee motivation as a mediating factor, in a veterinary hospital in DKI Jakarta. The objective is to understand these relationships and provide practical recommendations for improving service quality. A quantitative approach was used, collecting data from 125 veterinarians through questionnaires and interviews. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to analyze the data. The findings show that career development, training, and human resource planning positively and significantly enhance performance, both directly and through motivation, which acts as a key mediator. All hypotheses were supported, confirming the importance of these factors in boosting veterinarians’ effectiveness. In conclusion, veterinary hospitals should prioritize structured career paths, regular training, and strategic planning to enhance motivation and performance, improving animal care quality. However, the study’s reliance on self-reported data and focus on DKI Jakarta may limit its applicability to other contexts.

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

The rapid development of technology has increased the use of Artificial Intelligence (hereafter, AI) applications to improve students’ English skills, especially in speaking skill. This study aimed to investigate the effectiveness of AI applications to improve the introvert students’ speaking ability. Pre-experimental one group pre-test and post-test design was utilized in this study. This study conducted with 85 introvert students from two universities in Riau, Indonesia chosen by using MBTI test and purposive sampling technique. The data were collected through the questionnaire and speaking test. The data were analyzed by using SPSS 25th version to find out the descriptive statistic, normality test, and paired sample t-test. This finding shed light AI applications were effective in improving the introvert students’ speaking ability. It was proved that the t-test was higher than the t-table value (12.8231.663) with the level of significance p0.05. In short, there was significant difference on the students’ speaking ability before and after using AI applications for English speaking practiced. In conclusion, AI applications can be implemented to improve university students’ speaking ability.

This study investigates the implementation of Gated Recurrent Units (GRU) and Generative Adversarial Networks (GAN) to enhance effectiveness in Human Resource Management (HRM) and organizational efficiency. Across 10 experimental trials, the GRU model achieved an average accuracy of 85.7%, with precision, recall, and F1 score values averaging 0.86, 0.82, and 0.84, respectively. Meanwhile, the GAN model demonstrated an average accuracy of 93.2%, with precision, recall, and F1 score values averaging 0.93, 0.90, and 0.91, respectively. These results highlight the potential of neural network technologies to optimize HRM processes, including recruitment, performance evaluation, and workforce planning. By providing more accurate predictions and insights, GRU and GAN offer valuable decision support tools for organizations aiming to improve HRM practices and enhance organizational performance. This study contributes to the growing body of literature on the application of artificial intelligence in HRM. It underscores the importance of leveraging advanced technologies to drive innovation and efficiency in modern workplaces.

In response to the shortage of flight control talents in the context of the rapid development of the UAV industry, this study constructed a systematic UAV flight control talent training system and career development plan. The system adopts a teaching method that combines theory with practice, aiming to comprehensively improve students' professional skills and career development capabilities. The study investigated the effectiveness of the existing drone training system and found that students were less satisfied with drone courses, with the lowest average score being only 2.18 points. At the same time, when investigating the required abilities of UAV flight control personnel, it was found that the pre-flight inspection ability load was the highest, at 0.822. It proves that it is necessary to train new UAV flight control talents at this stage, and we should also focus on the training of pre-flight inspection capabilities in UAV talent training.

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Drug discovery has traditionally been a time consuming and expensive endeavor. Additionally, drugs weren’t as effectively designed as those that are being predicted and developed through AI and ML today. Machine learning is a form of artificial intelligence that develops and evolves based on experience (similarly to the human mind), and is more recently being utilized in drug discovery and design. The integration of AI and ML into the drug discovery and development process has allowed for higher target precision, lower toxicity, and better dosage formulations. AI more generally has been introduced to and has been leveraged at, each step of drug development, including target identification and validation, hit identification, as well as hit to lead optimization, and has been key in shortening the previously lengthy drug screening process. AI and ML has also been applied downstream in drug formulation where it has maximized resource utilization and is allowing for web-based 3D printing of drugs. Application of AI in the drug development process has also been extended to the modeling of novel drug-like compounds to predict their ADMET properties. This review will address the stages of drug discovery and development in which the application of AI and ML modeling has altered the traditional development of drugs. Keywords: Drug discovery, machine learning, artificial intelligence, computational drug development.

Purpose This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R 2 = 0.098) but strongly predicted sustainable development (R 2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.

This study investigated through qualitative design Dr. Dorothy Skeel’s professional career and scholarly development within the sociocultural context and life history that helped shape her ideas and actions to identify patterns in her life which reflected the social structure in which it was imbedded. The research questions guiding this study were: (1)How were significant aspects of Dr. Dorothy Skeel’s life history and professional knowledge represented in her practice? (2)What patterns and changes occurred in Dr Dorothy Skeel’s career development (3) What meanings and possible interpretations can be understood from the patterns and themes in Dr. Dorothy Skeel’s personal and professional life? (4) How might Dr. Dorothy Skeel’s life and work be used in teacher development? The research design employed grounded theory, an inductive method of comparative analysis. After data had been gathered it was coded and stored using Ethnograph (1998), a qualitative software package. Findings related to Dr. Dorothy Skeel’s professional career and scholarship emerged through 1) interview transcripts and 2) analysis of primary and secondary sources related to her writings and professional activities in social studies education and teacher preparation. The findings of the study revealed that Dr. Dorothy Skeel’s career path reflected the challenges women born in the 1930s had to face during the post- WWII period until the early 1970s. Additionally, her scholarship was shaped by important 20th century historical, social, and institutional tensions that impacted curriculum changes in teacher preparation and social studies education. Themes identified in Dr. Dorothy Skeel’s professional development were : Out of the Kitchen: A House Without a Wife- A Degree without the ‘M.R.S.,’ Mother-Lust Satisfaction, Walking The Fine Line In Academe: Balancing Acts, and Beyond Time and Space: The Transcendence of Care. Other themes explored were barriers she faced related to philosophical differences and the value placed on research vs. the scholar of the practice. The benefits and limitations of using biography were discussed along with recommendations for future study.