Educated Persons Research Articles

Hybrid learning in higher education: Considerations for its implementation in course design

Hybrid learning, combining face-to-face and distance instruction through digital technologies, has gained greater prominence in higher education, noted for its flexibility and adaptability. This innovative model fosters student engagement by overcoming the limitations of the traditional classroom and promoting collaborative learning and autonomy. This article presents a methodological guide for planning hybrid environments with a student-centered approach supported by digital technologies. The method is based on a literature review, providing a conceptualization and characterization of hybrid learning. It also outlines a structured guide for a university course under the hybrid model, considering available physical and virtual conditions, the degree of possible hybridization, as well as the development of learning objectives, activity design, instructional material, assessment strategies, and chronogram. The results show the experience from a thematic unit of the course “pedagogical praxis in the university context” at the Universidad Nacional de Costa Rica, which exemplifies this planning by integrating face-to-face and virtual activities, and continuous and formative assessment. In conclusion, understanding the advantages and challenges of hybrid learning is key to developing effective methodologies focused on flexibility and personalization in higher education.

Short- and long-term health effects of job insecurity. Fixed effects panel analysis of German data.

Previous research has linked job insecurity to health deterioration. The risk accumulation model suggests that health effects of job insecurity may persist even after job security is restored, yet long-term empirical analyses are scarce. Our study evaluates the long-term effects of accumulated exposures to affective job insecurity on mental and physical health among the working-age population in Germany. Using data from the German Socioeconomic Panel (12 624 individuals; 84 219 observations), we applied panel regression models with individual fixed effects to assess short- and long-term health changes associated with affective job insecurity. Job insecurity was measured by respondents' worries about job security. Mental and physical health was recorded with the SF-12 scale. Job insecurity correlated with short-term worsening in mental and physical health. However, after job insecurity ceased, health recovery was incomplete resulting in a long-term health deterioration. The long-term effects were larger among respondents who accumulated more instances of job insecurity, and showed a similar pattern for mental and physical health. An additional analysis documented stronger health effects of job insecurity among lower educated persons. Our study is one of the first to empirically demonstrate the negative long-term health effects of job insecurity. Our findings for a well-protected labor market like Germany's, suggest that the health risks associated with job insecurity may be substantial and potentially underestimated by studies that focus solely on short-term effects.

USING ARTIFICIAL INTELLIGENCE BY TEACHERS IN PRIMARY SCHOOL

The article analyses scientific sources on teacher training in terms of the use of artificial intelligence in the educational process. The concept of 'artificial intelligence' has been clarified and presented as "computer science" which deals with formalized problems and tasks similar to the actions performed by a person. The advantages (quick access to information) and disadvantages (relevant and irrelevant answers; reliable and unreliable answers; insufficiently complete information; the context cannot always be understood) that accompany the use of artificial intelligence are identified. A number of authors describe useful digital artificial intelligence tools for primary school teachers and classify them according to the nature of their teaching activities. It is noted that personalization of education (the educational process can be adapted to each individual student), efficiency and productivity (spending more time interacting with students and supporting them), accessibility and distance learning (access to education in remote regions), interactivity and lifelong learning (combination of virtual reality and artificial intelligence) are of particular importance in the context of war, limited access to education and significant educational losses. As part of their professional activities, primary school teachers can use various artificial intelligence tools, including the following: brainstorming, technical support (introduction of digital tools, use of integrated learning - using language models (ChatGPT, Bing Ai, Perplexity, Bard, Claude), administrative tasks; communication with participants in the educational process, assessment (automatic assessment), creation of educational materials (preparing lessons, creating notes, presentations, tests, using tools for writing and checking texts (Grammarly)), research on specific topics, assistance in supporting teacher training (Futurepedia, virtual laboratories). It has been determined that the use of artificial intelligence in the professional activities of teachers opens up many new opportunities for improving the teaching and learning process based on individual needs of each student. It is concluded that artificial intelligence has become a tool of everyday use that has penetrated into our lives and the lives of our children, which makes education better and brings it to a new and higher level.

Personalized Learning: an Adaptive Approach Based on the VARK Model to Improve Distance Education

Objective: This research designs a virtual course that adapts to different learning styles according to the VARK model and evaluates students' academic performance and satisfaction. Additionally, it provides an educational environment that optimizes learning through personalization. Theoretical Framework: This study is based on the VARK model, which classifies learning styles, allowing a better understanding of students' information processing. The research also reviews theories on distance education and adaptive learning. Method: A mixed-method approach is employed, using surveys and interviews to collect data on students' learning experiences before and after the course implementation. Results are analyzed using statistical tools and content analysis. Results and Discussion: The results show that students who participated in the adaptive course exhibited a significant improvement in academic performance and satisfaction levels. Differences in the effectiveness of teaching strategies were observed based on the predominant learning style. These findings suggest that personalization in education can have a positive impact on learning. Research Implications: This research is relevant for curriculum design and teacher training, as it promotes the inclusion of adaptive approaches in higher education. Educational institutions are encouraged to consider diverse learning styles when implementing virtual courses to maximize student potential. Originality/Value: This study provides an innovative approach to using the VARK model in virtual education, highlighting the importance of tailoring learning to students' individual needs. Its originality lies in the practical implementation of this model, which can serve as a reference for future research and educational developments.

Adequate bowel preparation of patients undergoing screening colonoscopy : Does educational status matter? Aretrospective data analysis.

In order to achieve ahigh-quality screening colonoscopy, ahigh-quality bowel preparation is essential. To perform an adequate bowel cleansing patients need to understand and act on medical information, also known as health literacy. This study aimed to analyze the relationship between the patients' educational status as aproxy for health literacy and adequate bowel preparation. Aretrospective analysis of data collected within the Austrian national colorectal cancer screening program from 2012 to 2022 was conducted. The database contains information on the quality of bowel preparation as well as the academic degree of the patients. Variables were used to perform alogistic regression analysis, with bowel preparation quality (based on the Aronchick scale) as the dependent variable and completed tertiary education as independent variable, and sex and age as control variables. Atotal of 329,778 patients aged 30-99years were included in the analysis. Within the group of academics, 88.46% (n = 21,883) were adequately prepared whereas in the group of non-academics, 84.79% (n = 258,641) were found to have ahigh bowel preparation quality. The odds ratio (OR) for academics to have an adequate bowel preparation quality was 1.37 (95% confidence interval, CI 1.32-1.43; p < 0.001). Patients with atertiary education showed better bowel preparation quality than non-academics in Austria. Therefore, it is important to improve strategies on how to inform also less educated persons to facilitate abetter screening colonoscopy quality and to optimize the use of resources from aclinical as well as apublic health perspective.

Effectiveness of Virtual Simulations Versus Mannequins and Real Persons in Medical and Nursing Education: Meta-Analysis and Trial Sequential Analysis of Randomized Controlled Trials.

Virtual simulation (VS) is a developing education approach with the recreation of reality using digital technology. The teaching effectiveness of VSs compared to mannequins and real persons (RPs) has never been investigated in medical and nursing education. This study aims to compare VSs and mannequins or RPs in improving the following clinical competencies: knowledge, procedural skills, clinical reasoning, and communication skills. Following Cochrane methodology, a meta-analysis was conducted on the effectiveness of VSs in pre- and postregistration medical or nursing participants. The Cochrane Library, PubMed, Embase, and Educational Resource Information Centre databases were searched to identify English-written randomized controlled trials up to August 2024. Two authors independently selected studies, extracted data, and assessed the risk of bias. All pooled estimates were based on random-effects models and assessed by trial sequential analyses. Leave-one-out, subgroup, and univariate meta-regression analyses were performed to explore sources of heterogeneity. A total of 27 studies with 1480 participants were included. Overall, there were no significant differences between VSs and mannequins or RPs in improving knowledge (standard mean difference [SMD]=0.08; 95% CI -0.30 to 0.47; I2=67%; P=.002), procedural skills (SMD=-0.12; 95% CI -0.47 to 0.23; I2=75%; P<.001), clinical reasoning (SMD=0.29; 95% CI -0.26 to 0.85; I2=88%; P<.001), and communication skills (SMD=-0.02; 95% CI: -0.62 to 0.58; I2=86%; P<.001). Trial sequential analysis for clinical reasoning indicated an insufficient sample size for a definitive judgment. For procedural skills, subgroup analyses showed that VSs were less effective among nursing participants (SMD=-0.55; 95% CI -1.07 to -0.03; I2=69%; P=.04). Univariate meta-regression detected a positive effect of publication year (β=.09; P=.02) on communication skill scores. Given favorable cost-utility plus high flexibility regarding time and space, VSs are viable alternatives to traditional face-to-face learning modalities. The comparative effectiveness of VSs deserves to be followed up with the emergence of new technology. In addition, further investigation of VSs with different design features will provide novel insights to drive education reform. PROSPERO CRD42023466622; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=466622.

Rebooting Master’s Programs in an Updated Higher Education System: Management Track

The relevance of the research topic is the importance of higher education programs in the implementation of one of the key functions of a modern university to form a graduate’s picture of the world of an educated person and acquire skills for successful professional activity. The article substantiates proposals that contribute to updating the Russian model of master’s training in the management track in the direction of increasing its efficiency, competitiveness and relevance.Research objectives: to consider the role of the master’s degree institute in the context of value creation by universities; justify one of the possible approaches to the design of a master’s program, which is based on the implementation of new principles for the creation and implementation of an educational product (using the example of direction 38.04.01 — Economics); consider the “double degree” format as part of a package proposal for the implementation of an MBA program and a master’s program (in the direction of 04/38/02 — Management); justify the need to include disciplines on venture financing and risk management of technological projects in the structure of the master’s training curriculum. Research methods: systematic approach, logical analysis, synthesis, content analysis of open sources, including official documents of a regulatory nature.Results. Ensuring high quality and sustainability of master’s programs is possible based on the implementation of the principle of the “four Us” (uniqueness, recognition, sustainability, satisfaction). To successfully solve the problems of ensuring the demand and competitiveness of the educational product in order to increase the value created by universities, taking into account new requirements for updating the domestic higher education system, it is necessary to update the ongoing master’s programs based on reengineering. It is recommended that master’s programs be updated in accordance with the developed principles of reengineering: taking into account transformational mega-trends when developing program content; strengthening the management component in the curriculum structure; increasing the level of originality of content through the development of “professorial” courses and reflecting this in the KPI system; the possibility of reformatting the curriculum into a block-modular format; participation of employers in the implementation of the master’s training curriculum in order to solve pressing problems of the real sector of the economy; formation of scientific groups of students to carry out research on current research topics with the possibility of using the results obtained in preparing reports on design and analytical work and writing a final qualifying thesis. The following are proposed as criteria for the success of a master’s program in Economics as elements of a university’s KPI system: the presence in the curriculum of disciplines that reflect transformational mega-trends, original “professorial” courses and disciplines of a management nature.Main conclusions (discussion). The project of launching an MBA program in a “double degree” format with an additional option providing the possibility of obtaining a “Master of Management” degree seems quite promising. The MBA program requires teaching the disciplines “Venture Business and Innovation Financing” and “Risk Management of Technological Projects.”

Home Dialysis for Latinx Individuals Living with Kidney Failure: A Qualitative Study of Interdisciplinary Dialysis Clinicians.

Latinx individuals experience 2 times the incidence of kidney failure compared to non-Latinx individuals and are less likely to utilize home dialysis therapies. In this qualitative study, interdisciplinary home dialysis clinicians were interviewed to understand the key factors and strategies used by clinicians to improve home dialysis uptake among the Latinx community. One-to-one, semi-structured interviews were conducted between November 2021 and March 2023 with 25 home dialysis interdisciplinary clinicians in Denver, Colorado and Houston, Texas. Interviews were audio-recorded, transcribed, and analyzed using thematic analysis. We identified three themes that focus on different levels of clinician advocacy in home dialysis uptake for the Latinx community: (1) Individual patient-level advocacy (helping patients overcome social challenges to home dialysis, cultivating personalized relationships, educating patients with in-person versus phone language interpretation, understanding cultural differences in communication), (2) Understanding patient influences on modality decision-making (acknowledging the importance of cultural concordance with clinician educator and patient peers, incorporating the patient lived experience, connecting with a patient's social support network, highlighting greater flexibility for employment, underscoring flexibility with culturally concordant foods) and (3) Changes to education at the dialysis facility level (standardizing routine and repeated modality education, promoting early and patient-centered education). Clinicians outlined efforts to improve access to home dialysis for Latinx groups on the patient and system level; in particular, individual and system level advocacy was grounded in trusting relationships and personalized education. A future intervention that improves the quality and personalization of dialysis modality education incorporating Latinx cultural values may improve access to home dialysis for Latinx people with kidney disease.

Homeschooling as an extension of school choice in the Czech Republic after the COVID-19 pandemic?

ABSTRACT The aim of the study was to examine the homeschooling and its recent development in Czechia as part of the school choice debate. The study analyses the different attitudes towards homeschooling among different groups within the Czech population and the underlying demographic, socio-economic, and ideological factors that may be behind these differences, as well as analysing the impact of the COVID-19 pandemic on this form of education in Czech society. The analysis of the nationally representative data shows that public support for homeschooling increased significantly after the COVID-19 pandemic, reaching its highest level in 2023. We found that the typical supporter of homeschooling tends to be a younger, highly educated person who lives in the large city or in its suburbs, is committed to democratic and liberal principles, is critical of totalitarian systems, and sees the legal possibility of homeschooling as an extension of the existing school choice.

The Influence of Artificial Intelligence on the Education System

The rapid development of artificial intelligence has had a huge and far-reaching impact on the field of education. The deep integration of artificial intelligence and education has promoted the emergence and development of artificial intelligence educational applications. Today's AI is not only a tool to enhance the learning experience, but also an important promoter of changing traditional teaching models and educational management methods. This article explores the acceptance and views of AI in different cultural backgrounds, and deeply analyzes the ethical issues that may arise in the application of AI in education, such as academic misconduct, algorithmic bias, and data privacy protection. Studies have shown that AI can help improve the personalization and efficiency of education, but its widespread use may cause students to rely on technology and weaken their critical thinking ability. In addition, if the bias in the algorithm is not effectively controlled, it may exacerbate inequality in education. The significance of the paper lies in providing theoretical support for the combination of AI and education, and putting forward constructive suggestions for balancing technological progress and human education elements in the future.

Lifestyle and BMI Changes after the Release of COVID-19 Restrictions: Do Humans Go 'Back to Normal'?

Humans adapt their life history strategy and lifestyle behavior to the environment. The social restrictions during the COVID-19 pandemic in the years 2020-2021 led to changes in diet and physical activity habits in large parts of populations worldwide. Additionally, many people reported an increase in body weight during the pandemic. We investigated how diet quality, physical activity, and BMI changed since the end of the COVID-19 restrictions in Switzerland (2019-2023). We assessed diet and physical activity using a standardized questionnaire. We measured anthropometry and BMI using a stadiometer and a balance of a medical bioimpedance analyzer. Our results show a stable BMI in men and women during the pandemic. In both sexes, diet quality and physical activity levels did not change significantly during this time. Among women, physical activity and age were associated with BMI, while in men, apart from physical activity, high diet quality and living in the agglomeration were associated with BMI. Overall, we did not find evidence for a transient "unhealthier" lifestyle immediately after the COVID-19 pandemic. However, we found constituent sex differences in BMI determinants. Our results could indicate a higher resilience in highly educated persons and might contribute to more personalized approaches to prevent obesity.

Research on the Precision Model of Ideological and Political Education under the Background of Big Data

With the rapid development of information technology, big data has become a core driving force for transformation across various industries, and education is particularly prominent. In ideological and political education, traditional models can no longer meet the increasingly diversified educational needs. Especially with the promotion of informatization, personalization, and precision education, utilizing big data technology to improve the relevance and effectiveness of ideological and political education has become an urgent issue. This paper analyzes the transformation needs of ideological and political education in the context of big data, proposes a framework for constructing a precision model of ideological and political education driven by big data, and explores its application in areas such as monitoring student ideological trends, customizing educational content, and feedback assessment. The research indicates that big data technology can effectively enhance the personalization, effectiveness, and interactivity of ideological and political education, drive innovation in teaching methods, and improve educational quality and outcomes. Finally, the paper looks ahead to the continuous development of the precision model of ideological and political education and suggests that the deep application of big data will promote more precise, efficient, and personalized service for students in ideological and political education.

The Current Status and Development Trends of Information Technology-Aided Mathematics Teaching

With the rapid development of information technology, mathematics teaching is gradually transitioning from traditional teaching models to more information- and technology-driven methods. Information technology has brought significant changes in the presentation of teaching content, student learning experiences, and interactive communication, and has provided new opportunities for the personalization and diversification of mathematics education. This article aims to analyze the current application of information technology in mathematics teaching, explore the challenges and potential it brings, and focus on the applications of digital learning platforms, Computer-Aided Mathematics Education (CAME), Virtual Reality (VR), and Augmented Reality (AR) in mathematics education. By analyzing the current use of teaching tools, platforms, and technologies, this article further looks ahead to the trend of deep integration between information technology and mathematics education, particularly in the fields of intelligent learning systems and big data-driven assessment mechanisms. The article concludes by proposing future development directions for information technology-assisted mathematics teaching and discussing how to promote the further application and practice of information technology in education.

Increasing Student Interest in Learning through the Implementation of the K-Nearest Neighbor Algorithm in Classifying Learning Preferences at SMAN 1 Kraksaan

This research examines the effectiveness of implementing the K-Nearest Neighbor (KNN) algorithm in classifying student learning preferences and its impact on increasing interest in learning at SMAN 1 Kraksaan. The main aim of the research is to optimize learning methods through personalization based on individual student preferences. The study involved 560 students of SMAN 1 Kraksaan, with data including variables of age, gender, academic grades, daily study time, attendance and participation in class. The KNN algorithm is used to classify students' learning preferences into visual, auditory, kinesthetic, and reading/writing categories. The learning method is then adjusted based on the results of this classification. The results show that the KNN algorithm is able to classify student learning preferences with an accuracy of 80.36%. After implementing personalized learning methods, there was a significant increase in students' interest in learning, with an average increase of 1.76 points on a 10-point scale. Paired t-test analysis showed a statistically significant difference between interest in learning before and after intervention (p &lt; 0.0001). This research concludes that the implementation of the KNN algorithm in classifying learning preferences can help increase students' interest in learning effectively. These findings emphasize the importance of personalization in education and demonstrate the potential of integrating machine learning in the pedagogical process to improve learning outcomes.

Managing Personalization and Collaboration in Education: A Systematic Review of Educational Practices

This systematic literature review analyzes 20 carefully selected documents from the Scopus database. This study explores ways that align with Islamic educational management's principles to balance personalized learning outcomes and community empowerment in academic settings. The selection of documents is based on relevance to the focus and recent developments in Islamic education. The qualitative analysis method uses thematic coding and VOS Viewer. The analysis results indicate that Islamic approaches like collaboration, reflection, and feedback can achieve balanced learning outcomes. Collaborative filtering helps select learning materials according to student's needs, while reflection and feedback strengthen positive interactions and ukhuwah within the learning community. In addition, computer-based learning and e-learning allow students to learn at their own pace. A flexible curriculum allows for adjustments to students' interests by the principle of taysir (making it easy). These findings recommend that educational institutions integrate a multifaceted approach to create an inclusive, adaptive, and Islamic-oriented learning environment.

The concepts of ‘educational process’ and ‘educated person’ from the philosophical perspective of Richard Stanley Peters: Critical review

This article aimed to examine the two concepts of the educational process and educated person from the philosophical perspective of Richard S. Peters, a prominent philosopher of education. In light of this, one of his influential works, “What is an Educational Process?”, was carefully reviewed. Some comments on the overall relevance of Peters’ positions on the two concepts are also included. The review provides valuable insights into these concepts, which are profoundly important for the education field. Finally, concluding remarks and implications of the author's perspectives on contemporary educational systems are highlighted.

Hearing Loss and Annual Earnings Over a 20-Year Period: The HUNT Cohort Study.

The association between hearing loss and income has only been examined in cross-sectional studies. We aim to study annual increase in earnings over 20 years, comparing people with and without hearing loss. We used data from a population-based hearing study in Norway (The Trøndelag Health Study, 1996-1998), including 14,825 persons (46.2% men, mean age at baseline 30.6 years, age range 20 to 40 years). Hearing loss was defined as the pure-tone average threshold of 0.5 to 4 kHz in the better hearing ear ≥20 dB HL (n = 230). Annual earnings were assessed from 1997 to 2017. Longitudinal analyses were performed with linear mixed models adjusted for age, sex, and education. People without hearing loss at baseline (before age 40) had a greater annual increase in earnings over a 20-year follow-up period compared with people with hearing loss. For people with normal hearing, annual earnings over 20 years increased by 453 Euro (EUR) (95% confidence interval [CI] = 384 to 522) or 13.2% more per year than for people with hearing loss, adjusted for age and sex. The difference in annual earnings over 20 year was greater among women (462 EUR, 95% CI = 376 to 547) than men (424 EUR, 95% CI = 315 to 533), greater among younger than older adults, and greater among lower than higher educated persons. When including adjustment for education in the model, in addition to age and sex, the difference in annual earnings over 20 years between persons with and without hearing loss was reduced (337 EUR, 95% CI = 269 to 405). The results from this large population-based study indicates that people with hearing loss experience lower long-term earnings growth compared with people with normal hearing. The findings highlight the need for increased interventions in the workplace for people with hearing loss.

Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis. A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis. Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.

Learning Behavior Analysis and Prediction Based on Convolutional Neural Networks

With the development of information technology and the change of educational needs, personalized learning and intelligent education have become an important direction of current educational research. Traditional educational assessment and teaching methods are limited by subjectivity and limitations, and it is difficult to fully analyze and understand learners individual needs and behavior patterns. The analysis and prediction method of learning behavior based on convolutional neural networks provides new tools and perspectives for educational decision makers and teachers through automated and data-driven methods, which is expected to deepen the understanding of the laws behind learners behaviors and achieve the goal of education personalization and effect optimization. This paper will discuss the application status and future development direction of CNN in learning behavior analysis, and contribute new theoretical and practical experience to the innovation and progress of educational technology.

Generative artificial intelligence and the personalization of health professional education: A narrative review.

This narrative review examined the intersection of generative artificial intelligence (GAI) and the personalization of health professional education (PHE). This review aims to the elucidate the current condition of GAI technologies and their particular uses in the field of PHE. Data were extracted and analyzed from studies focusing on the demographics and professional development preferences of healthcare workers, the competencies required for personalized precision medicine, and the current and potential applications of artificial intelligence (AI) in PHE. The review also addressed the ethical implications of AI implementation in this context. Findings indicated a gender-balanced healthcare workforce with a predisposition toward continuous professional development and digital tool utilization. A need for a comprehensive educational framework was identified to include a spectrum of skills crucial for precision medicine, emphasizing the importance of patient involvement and bioethics. AI was found to enhance educational experiences and research in PHE, with an increasing trend in AI applications, particularly in surgical education since 2018. Ethical challenges associated with AI integration in PHE were highlighted, with an emphasis on the need for ethical design and diverse development teams. Core concepts in AI research were established, with a spotlight on emerging areas such as data science and learning analytics. The application of AI in PHE was recognized for its current benefits and potential for future advancements, with a call for ethical vigilance. GAI holds significant promise for personalizing PHE, with an identified need for ethical frameworks and diverse developer teams to address bias and equity in educational AI applications.