Integrating Learning Analytics, AI, and STEM Education:

Survey data on students' perceptions, knowledge, and use of learning analytics (LA) and generative artificial intelligence (GenAI) for the personalization of learning.

The acceptance of, perceived advantages to, and skepticism toward the integration of artificial intelligence (AI) in undergraduate education is investigated in this paper. In this study, a total of 675 students from six Romanian universities answered a self-administered online questionnaire evaluating three main aspects: AI acceptance, AI benefits, and AI skepticism. While AI skepticism has a modest but substantial negative influence (β = −0.113, p = 0.001), results show that AI benefits favorably predict AI acceptance (β = 0.541, p = 0.001). Whereas AI skepticism negatively correlates with AI acceptance (r = −0.124, p = 0.001), correlational analysis reveals a high positive association between AI acceptance and AI benefits (r = 0.544, p = 0.001). Despite concerns about its limitations, the regression model suggests that students’ willingness to embrace AI in education is mostly driven by its perceived advantages. This explains 30.8% of the variance in AI acceptance (R2 = 0.308, F(2, 641) = 142.909, p < 0.001). These results highlight the importance of techniques that improve perceived benefits while addressing uncertainty since they offer insightful analysis of student attitudes regarding artificial intelligence integration in higher education. By guiding policy decisions and educational activities meant to maximize AI-driven learning environments, this study adds to the current conversation on artificial intelligence adoption in education.

The integration of Artificial Intelligence (AI) in non-formal, informal and formal education is an emerging area with great potential to transform the world. However, the capacity of educators, youth workers and teachers to capitalise on this potential remains limited due to a lack of well-defined frameworks for AI integration in practices. The scope of this study is to explore the needs, challenges, and perspectives of youth work professionals regarding AI integration.The research methodology employed mixt research methods. The literature review was followed by four semi-structured focus groups involving 72 youth workers and professionals with diverse backgrounds, nationalities and experience levels. A common guideline was used to ensure consistency, including facilitator instructions, discussion prompts, and reporting templates. The data collected during the focus groups were analysed using a Computer-Assisted Qualitative Data Analysis Software (CAQDAS), ATLAS.ti software dedicated to qualitative research. The authors conducted qualitative analysis, through coding, identification of main categories, thematic map for visualization of results, and interpretation of findings to uncover patterns and deeper insights. The results revealed a generally positive attitude toward AI tools and an awareness of their potential benefits in youth work, particularly in content creation. However, participants reported significant challenges regarding the use of AI for administrative tasks efficiency, for personalised learning, ethical concerns, and difficulty in interpretation of data. Cross-country similarities emerged, suggesting a shared need for capacity building and continuous training, while differences highlighted the influence of national contexts and digital infrastructure.This study provides a foundational understanding of the competence needs of youth workers in relation to AI adoption. The findings are relevant for course designers wishing to offer targeted training for youth workers. Future research is also needed into policy-level support mechanisms that can enhance AI literacy and ethical integration across the sector.

The integration of Artificial Intelligence (AI) in education offers opportunities for personalized and accessible learning but raises critical ethical concerns. This study examines doctoral researchers' perspectives on these issues using the PAPA (Privacy, Accuracy, Property, and Accessibility) framework. Semi-structured interviews revealed 10 themes and 23 subthemes, highlighting concerns about data privacy, ownership, consent, and transparency. Participants stressed the need for robust ethical guidelines to address risks such as breaches, bias, and unequal access while ensuring AI supports rather than undermines academic integrity and autonomy. The findings emphasize ethical frameworks as essential for responsible and inclusive AI integration in education.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

Objective: The study aimed to identify the attitudes of music skills teachers in Oman towards the use of artificial intelligence (AI) in teaching and to understand the challenges they face in integrating AI into their teaching methods. Theoretical Framework: The proliferation of modern learning resources and technological advancements, especially the emergence of artificial intelligence, have significantly impacted educational institutions. Oman is striving to align its education system with the digital transformation outlined in Oman Vision 2040, which emphasizes high-quality education, the integration of technology, innovation, entrepreneurship, and skill-building. Method: This study adopted a descriptive analytical method, analyzing a questionnaire completed by music skills teachers in Oman regarding their opinions on AI in teaching. The survey focused on three main axes: knowledge of AI, its use in teaching music skills, and the challenges faced by teachers in integrating AI into their practice. Results and Discussion: The results indicated that music skills teachers in Oman have a positive attitude towards the use of artificial intelligence in teaching. Regarding knowledge of AI, the statement "I strive to develop my knowledge about artificial intelligence and its applications in various fields" received the highest mean score of 4.14 with a standard deviation of 0.808, showing a strong commitment to learning about AI. In terms of using AI in teaching music skills, the statement "I intend to use artificial intelligence technologies in the future" ranked highest with a mean score of 4.24 and a standard deviation of 0.761, reflecting a positive intention to integrate AI into their future teaching practices. However, when it came to challenges, the statement "Lack of specialized training in artificial intelligence for music skills teachers" emerged as the most significant barrier, with the highest mean of 4.26 and a standard deviation of 0.782, pointing to the lack of adequate training as the primary obstacle in AI adoption for teaching music skills. Conclusion: The study concludes that music skills teachers in Oman generally have positive attitudes toward the use of AI in teaching. However, the main challenge identified was the lack of specialized training for teachers, which hinders the effective implementation of AI in music education. Research Implications: This research highlights the need for better training and resources for teachers to effectively incorporate AI in their teaching practices. It also emphasizes the importance of curriculum updates to align with AI advancements. Originality/Value: This study provides valuable insights into the perspectives of music skills teachers in Oman on the integration of AI in teaching, contributing to the growing body of research on AI in education in the Middle East. Contributions: The study contributes to the understanding of the role of AI in music education, specifically in Oman, and identifies key barriers that must be addressed for effective integration.

Artificial intelligence (AI) integration into education presents challenging ethical and practical issues as well as transforming possibilities for individualized learning, operational efficiency, and improved outcomes. This report investigates trust, transparency, algorithmic bias, and responsibility in AI-driven educational systems from the points of view of three hundred teachers, educators, and researchers around India. The results highlight a notable knowledge gap: Just 28.33% of respondents are highly familiar with artificial intelligence in education, while 32.67% are completely unfamiliar. Though 35% of respondents are unsure about data privacy policies, 41.67% of respondents believe AI can greatly improve educational outcomes, so they are cautiously optimistic. Only 27.67% of respondents said they were completely aware of how artificial intelligence systems use data. Thus, algorithmic bias became a major issue for 34% of respondents, along with general calls for transparency. The results highlight the need for human supervision since 35.67% of respondents support artificial intelligence use just under such circumstances. This paper comes to the conclusion that tackling these issues calls for strong ethical rules, professional growth to raise AI literacy, and inclusive models for transparency and responsibility. These steps are essential to guarantee fairness and confidence in AI integration, so opening the path for responsible application in learning environments.

Artificial Intelligence in Education (AIEd) is an established interdisciplinary field of research with focus on deploying artificial intelligence (AI) algorithms to transform education. There has been a growing interest in harnessing the power of AIEd to solve educational challenges in science, technology, engineering, and mathematics (STEM). This opening chapter focuses broadly on state-of-the-art knowledge and cutting-edge innovations in the area of AIEd. We discuss the transformation of AI in STEM through the prism of three AIEd paradigm frameworks. These frameworks are known as AI-directed STEM education, AI-supported STEM education, and AI-empowered STEM education. We clarify the design and application of AI in STEM education using these paradigms. Advantages, disadvantages, and future trends of AI applications in STEM education are highlighted.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The integration of artificial intelligence (AI) has become increasingly essential in implementing innovative instructional approaches at Higher Secondary School (HSS). The demand for advanced education that integrates intelligent technologies to improve the overall educational experience is growing. Individuals that actively study artificial intelligence are more equipped to face future difficulties related to technology, society, and the environment. The Knowledge and Attitudes Survey (KAS) lacks empirical evidence regarding the motivation levels of students in their involvement with the field of AI. The main purpose of this study was to examine how students' self-confidence, peer opinions, and personal beliefs affect their willingness to participate in AI education. A comprehensive longitudinal survey covered a total of 707 students who entered in higher secondary school. The survey examined the subjective norms, intentions, and knowledge of the participants, as well as their acquaintance with AI, their attitude towards acquiring knowledge of AI, their inclination towards AI-based learning, and their self-assurance in studying AI. The participants displayed a primarily optimistic perspective on AI education, with 78.1% expressing positivity. Moreover, an overwhelming majority of 73.1 percent of participants exhibited assurance in their capacity to gain knowledge in the realm of artificial intelligence. The data indicate that a substantial majority of respondents, precisely 82%, recognized the influence of different elements, such as peer pressure, instructors, and families, on their choice to participate in AI courses. The results indicate that students' perception of social norms significantly impacts their motivation to study artificial intelligence. Students must demonstrate a heightened openness to AI learning, especially if they perceive support and approval from their teachers and peers. The desire of pupils to participate in AI learning was discovered to be impacted by their viewpoint, self-assurance, and perceived proficiency in the subject matter. Various tactics can be employed to boost students' motivation to engage in the study of AI at a HSS, as indicated by the research results. These tactics encompass the dissemination of knowledge regarding the importance of AI education, fostering favorable views towards the subject, and nurturing students' self-perception of their abilities and control. This study offers significant insights for government authorities, educators, and other stakeholders in KAS and similar educational settings.

Today, when digital transformation affects the field of social work, the integration of artificial intelligence technologies into professional practices gains importance. This study examines social work students' views on artificial intelligence with a phenomenological approach. The aim of the study is to determine students' perceptions of artificial intelligence, its potential role in professional practices and their concerns about ethics. The study included 14 third and fourth year social work students living in Bandırma. A semi-structured interview form was used as a data collection tool, and in-depth interviews lasted between 30-45 minutes. The analyses were carried out by thematic analysis method using MAXQDA software. In the findings, 8 sub-themes and 27 codes were identified under 3 main themes: 'Perceptions of Artificial Intelligence', 'Intersection of Social Work and Artificial Intelligence' and 'Artificial Intelligence Education and Access'. While students emphasise the benefits of artificial intelligence such as making life easier (f=10) and fast access (f=8), they have concerns about lack of empathy (f=8), privacy and data security (f=8). In addition, the integration of artificial intelligence into the curriculum (f=9) emerged as an important need. The results show that artificial intelligence in social work education should be positioned in line with professional values.

This study explores the integration of Artificial Intelligence (AI) in language learning and literary education, highlighting its potential to enhance personalized learning, adaptive feedback, and accessibility. AI can facilitate text analysis, foster creativity, and support interactive learning experiences. However, challenges such as AI's lack of emotional intelligence, ethical concerns about data privacy, and the risk of overreliance on technology must be addressed. While AI can optimize educational efficiency, human involvement remains essential for emotional support and critical discussions. The study emphasizes the need for balanced AI integration, ensuring that technology complements rather than replaces human teaching. Future research should focus on developing emotionally intelligent AI, ethical data frameworks, and equitable access to AI-powered education. By addressing these aspects, educators and policymakers can maximize AI's benefits while maintaining a holistic and inclusive learning environment.

Artificial Intelligence (AI) has rapidly emerged as a transformative force across diverse sectors worldwide, with policy initiatives signalling its rising prominence. In the realm of education, AI holds promise for revolutionising learning and teaching. However, the incorporation of AI in basic education faces significant challenges for many schools, particularly when explicit curriculum guidelines are absent, and teachers have limited exposure to AI technologies. This study delves into the importance of integrating Artificial Intelligence in Education (AIED), particularly in primary and secondary school settings, with a specific emphasis on the obstacles and facilitators. The research examines the impact of digital leadership and its role in mitigating teachers' barriers to AIED incorporations. These external variables are systematically outlined within the conceptual framework proposed by this study. Furthermore, it conceptualises and considers three AIED approaches—Learning from AI, Learning about AI, and Learning with AI—as internal variables central to basic-education AIED incorporations, as highlighted in previous research. The study gathers quantitative data through a self-constructed questionnaire survey from 204 school principals and management-level teachers across 60 primary and secondary schools in Hong Kong, capturing their perceptions of AIED incorporation. A structural equation model (SEM) is adopted to analyse the relationships between digital leadership, internal and external teacher barriers, and the three AIED approaches. The findings underscore that digital leadership is a significant factor in facilitating AI incorporations in schools, and that teachers' internal and external barriers considerably affect Learning about AI. The paper concludes by proposing strategies to enhance the capabilities of school leaders and teachers in promoting AIED in basic education.

The integration of Artificial Intelligence (AI) into education transcends its role as a mere efficiency tool, becoming an active, “unseen classroom partner” that shapes cognitive, social, and ethical dimensions of learning. This chapter employs an interdisciplinary conceptual analysis to argue that understanding AI's profound influence requires synthesizing perspectives from computer science, psychology, sociology, and philosophy. Moving beyond a technocentric view, we critically examine AI's dual potential to enhance metacognition and personalization while simultaneously risking algorithmic dependency, eroded autonomy, and the amplification of societal biases. The analysis extends to AI's role in shaping trust, motivation, and classroom dynamics, with particular attention to its integration within immersive and inclusive learning environments. We confront key ethical dilemmas surrounding data privacy, surveillance, and algorithmic fairness.

The integration of artificial intelligence (AI) in rural healthcare systems holds great promise for improving access to quality medical care and addressing healthcare disparities. However, numerous barriers must be overcome to successfully implement AI technologies in these settings. This paper explores the key challenges and potential solutions for implementing AI in rural healthcare systems, focusing on infrastructure, data privacy, internet connectivity, and workforce readiness. One of the primary barriers is the lack of infrastructure in rural areas. Many rural healthcare facilities are under-resourced, lacking the necessary hardware, software, and technical support to adopt AI solutions. Addressing this issue requires significant investment in healthcare infrastructure, including upgrading existing facilities and ensuring they are equipped with modern technology. Data privacy and security present another significant challenge. AI systems rely on vast amounts of patient data to function effectively, raising concerns about data confidentiality and protection. Implementing robust cybersecurity measures and developing clear data governance policies are essential to safeguard patient information and build trust in AI technologies. Internet connectivity is a crucial enabler for AI-driven healthcare, yet rural areas often suffer from unreliable or limited internet access. Solutions to this challenge include investing in broadband infrastructure, exploring alternative connectivity options such as satellite internet, and leveraging offline AI capabilities that can operate with minimal connectivity. Workforce readiness and acceptance of AI technology are also critical for successful implementation. Many rural healthcare providers may lack the training and expertise needed to utilize AI tools effectively. Comprehensive training programs, ongoing education, and support from technology providers can help bridge this knowledge gap and encourage the adoption of AI. In conclusion, while the integration of AI in rural healthcare systems faces significant barriers, targeted solutions can address these challenges. Investment in infrastructure, robust data privacy measures, improved internet connectivity, and comprehensive training programs are essential steps to enable the successful implementation of AI in rural healthcare. By overcoming these barriers, AI has the potential to transform rural healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Collaborative efforts among government entities, healthcare providers, and technology companies will be crucial in realizing this potential and ensuring equitable access to advanced healthcare technologies in rural areas.