Artificial intelligence in STEM education: the paradigmatic shifts in research, education, and technology

This chapter examines the importance of integrating artificial intelligence (AI) and STEM (Science, Technology, Engineering, and Mathematics) in early childhood education, highlighting how this integration contributes to foundational skill development in young children. AI's role in education is emphasized through its ability to provide interactive and personalized learning experiences, thereby fostering independent learning processes among students. Integrating AI tools in early learning increases children's interest in STEM subjects and supports teachers in various instructional tasks. The chapter presents three main paradigms of AI-based STEM education: AI-directed, AI-supported, and AI-empowered STEM education. These models serve distinct functions in the learning process, such as as automating access to information, providing real-time feedback, and creating student-centered learning environments. Specifically, natural language processing tools like ChatGPT support students' individualized learning paths by offering instant information on STEM topics and assisting teachers in preparing educational materials. A practical example, "Plant Exploration with a Smart Assistant," demonstrates a model for enhancing children's scientific observation skills and environmental awareness through AI. This activity enables young learners to explore nature with the support of AI, illustrating the potential of educational technologies in early childhood education. Finally, the chapter underscores AI's contribution to the development of critical thinking and creativity skills. AI's capabilities in providing instant feedback and accessible information encourage in-depth learning experiences in STEM education, equipping students with a more informed approach to data analysis and knowledge evaluation.

This study conducts a bibliometric analysis of artificial intelligence (AI) in STEM education research from 2020 to 2024. The study uses citation analysis to examine publication trends, country contributions, top authors, cited journals, and influential articles in this field. Data was collected from the Dimensions database using the keywords "artificial intelligence" AND "stem education." The analysis reveals a significant increase in publications and citations in 2024 compared to previous years. The United States emerges as the leading country in the number of documents (9) and citations (103). China follows with five documents but no citations. The most cited authors include Nesra Yannier, Kenneth R. Koedinger, and Scott E. Hudson, each with 55 citations. The International Journal of Artificial Intelligence in Education is the most cited journal, with 55 citations. The most influential article, "Active Learning is About More Than Hands-On: A Mixed-Reality AI System to Support STEM Education," received 55 citations. Carnegie Mellon University stands out as the most cited institution, with 55 citations. The findings highlight the growing importance of AI in STEM education research, focusing on personalized learning, advanced analytics, and instructional automation, inspiring us all with the potential of AI to transform the future of education. This bibliometric analysis provides valuable insights for researchers, educators, and policymakers interested in the intersection of AI and STEM.

The advancement of Technologies such as Artificial Intelligence (AI) tools are widely used and help in education such as in Technical and Vocational Education and Training (TVET), as well as Science, Technology, Engineering and Mathematics (STEM). This qualitative study is to identify the impact of AI on students in higher learning settings of TVET and STEM programs in Malaysia. From among science and engineering students, this study aimed to identify in what ways the tools and technologies impact on these learners’ experiences, impact their learning, their academic performance, and preparedness for their careers in the future. This study uses a thematic analysis research design. The sample will consist of 10 participants selected based on varying experiences and views. The research method chosen is an in-depth interview, meaning that it will go into details about the benefits and challenges of AI use in education as they are perceived by students. The initial findings will provide an insight into crucial themes enhancement in learning efficiency, development of critical thinking skills, and challenges resulting from the adoption of new technologies. In addition, this research also identifies possible application areas of AI for improving educational outcomes in TVET and STEM and possible barriers for effective implementation. Findings will provide an overview of the knowledge base at the present time about the adoption of AI in the higher learning sector, with associated meaning for educators, and institutions that are considering embedding AI into curricula. The outcome will further realize how AI could be applied to narrow the gap between traditional ways of educating people regarding the demands of an increasingly digitalized workforce, hence significantly improving the employability of TVET and STEM graduates in Malaysia.

Objective: This paper aims to critically analyze the role of Artificial Intelligence (AI) in Education 5.0, focusing on its opportunities and challenges. It explores technological advancements in AI, their applications in educational settings, and the paradigm shift towards personalization and adaptive learning, while examining ethical considerations inherent in integrating AI into education. Method: The research employs a qualitative analysis of existing literature spanning a period of five years. This involves reviewing case studies, industry reports, and empirical evidence on the implementation and impact of AI technologies in educational contexts. The study covers various aspects of AI applications, including AI algorithms in educational content curation, machine learning in student assessment, and natural language processing in language learning. Results: The findings reveal that AI significantly enhances educational experiences by enabling personalized and adaptive learning, improving student engagement, and providing tailored feedback. AI algorithms have transformed educational content curation, while machine learning has revolutionized student assessments by providing nuanced evaluations and predictive analytics. Natural language processing has advanced language learning by offering interactive and immersive experiences. However, the study also highlights challenges such as data privacy, algorithmic bias, and the digital divide. Ensuring robust data protection, addressing bias in AI systems, and improving digital infrastructure are essential to maximizing the benefits of AI in education. Conclusions: AI's integration into Education 5.0 presents both significant opportunities and substantial challenges. While AI has the potential to revolutionize education through enhanced personalization and efficiency, it also raises ethical and accessibility concerns. The study emphasizes the need for balanced approaches that leverage AI's strengths while mitigating its risks. Collaboration among educators, policymakers, and technologists is crucial to ensure that the benefits of AI in education are equitably distributed and ethically aligned with societal values.

Objective: This paper aims to critically analyze the role of Artificial Intelligence (AI) in Education 5.0, focusing on its opportunities and challenges. It explores technological advancements in AI, their applications in educational settings, and the paradigm shift towards personalization and adaptive learning, while examining ethical considerations inherent in integrating AI into education. Method: The research employs a qualitative analysis of existing literature spanning a period of five years. This involves reviewing case studies, industry reports, and empirical evidence on the implementation and impact of AI technologies in educational contexts. The study covers various aspects of AI applications, including AI algorithms in educational content curation, machine learning in student assessment, and natural language processing in language learning. Results: The findings reveal that AI significantly enhances educational experiences by enabling personalized and adaptive learning, improving student engagement, and providing tailored feedback. AI algorithms have transformed educational content curation, while machine learning has revolutionized student assessments by providing nuanced evaluations and predictive analytics. Natural language processing has advanced language learning by offering interactive and immersive experiences. However, the study also highlights challenges such as data privacy, algorithmic bias, and the digital divide. Ensuring robust data protection, addressing bias in AI systems, and improving digital infrastructure are essential to maximizing the benefits of AI in education. Conclusions: AI's integration into Education 5.0 presents both significant opportunities and substantial challenges. While AI has the potential to revolutionize education through enhanced personalization and efficiency, it also raises ethical and accessibility concerns. The study emphasizes the need for balanced approaches that leverage AI's strengths while mitigating its risks. Collaboration among educators, policymakers, and technologists is crucial to ensure that the benefits of AI in education are equitably distributed and ethically aligned with societal values.

The object of the study is the status, segment analysis, dynamics and prospects of the global markets for higher education and artificial intelligence in higher education. The analysis and systematization of literature data allowed to summarize the results of research in the field of marketing research of higher education markets and artificial intelligence in higher education. To conduct a marketing analysis, the method of literature search and the method of analysis were applied. The main trends, volumes, rates and factors of growth of the higher education and artificial intelligence markets in the field of higher education, as well as some limitations of their development are presented. The analysis of the higher education market is carried out by the following segments: geographical regions, mode of study, educational levels, sources of income, educational institutions, and the market of artificial intelligence in education – market components, deployment mode, technologies, applications, geographical regions. The potential demand and volume of higher education and artificial intelligence markets in the field of higher education in different countries of the world are determined, the dynamics and competition in the world markets are tracked. The state of the art and prospects for further research in the field of higher education and artificial intelligence in education are summarized. The following scientific methods were used: the method of searching for literature data on the topic under study; the method of analyzing literary sources; comparative analysis of different methodological approaches; content analysis of documents; the method of systematization and classification in conducting research on the achievements of modern science and technology in the field of higher education and the use of artificial intelligence in higher education. The systematization of literature data allowed to present the problems of higher education and the use of artificial intelligence in higher education in the form of tables and diagrams, which gives a certain advantage for understanding and using the material.

This study investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) in STEM education, emphasizing the transformative potential and inherent challenges of these technologies. The purpose of this research is to provide a thorough understanding of how AI and ML can enhance educational outcomes, personalize learning experiences and address critical issues within the STEM fields. Utilizing a comprehensive review of current literature, this study examines the state of AI and ML integration in STEM education, identifies key ethical considerations and explores future trends and research directions. Key findings reveal that AI and ML significantly contribute to personalized learning, adaptive teaching strategies, and increased student engagement. However, challenges such as data privacy concerns, ethical dilemmas and the necessity for extensive educator training and infrastructure investment are prominent. The study underscores the importance of developing ethical frameworks and guidelines to ensure responsible use, mitigate biases and promote transparency. The conclusions drawn from this research highlight the critical need for collaboration among educators, technology developers, policymakers and researchers to fully leverage the potential of AI and ML. Recommendations include investing in professional development for educators, ensuring equitable access to AI tools and fostering international cooperation to share best practices and innovative solutions. Further, ongoing research into the ethical and practical implications of these technologies is essential for their successful integration into STEM education. This study elucidates the profound opportunities AI and ML present in transforming STEM education and calls for a strategic, ethical, and collaborative approach to overcome existing challenges and enhance educational practices. Keywords: Artificial Intelligence, Machine Learning, STEM Education, Personalized Learning, Ethical AI, Educational Technology.

This study explores the adoption of Artificial Intelligence (AI) in STEM education by proposing a new conceptual model that integrates UTAUT 2 and GETAMEL frameworks. Data collected from 582 science teachers in Turkey were analyzed using Structural Equation Modeling. The results demonstrated that the proposed model outperformed the original GETAMEL and UTAUT 2 models in predicting teachers' intentions to adopt AI in STEM education. Key factors influencing adoption included subjective norm, experience, perceived enjoyment, anxiety, and self-efficacy, which significantly impacted perceived usefulness and perceived ease of use. These factors, in turn, positively influenced attitudes and intentions toward using AI-powered tools. Additionally, price value, facilitating conditions, and habit were identified as significant predictors of intention. The mediating roles of perceived ease of use, perceived usefulness, and attitude were confirmed in explaining adoption intentions. This model offers valuable insights for promoting effective AI integration in STEM education, aiding policymakers, educators, and researchers in understanding the factors driving technology adoption. It highlights actionable strategies for enhancing the acceptance and utilization of AI-powered tools in educational settings.

BackgroundThe application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework.ResultsThe results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education.ConclusionsThe application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.

The evolution of artificial intelligence (AI) and robotics in education has transitioned from automation toward emotionally responsive learning systems through artificial emotional intelligence (AEI). While AI-driven robotics has enhanced instructional automation, AEI introduces an affective dimension by recognizing and responding to human emotions. This study examines the role of AEI-powered robotics in fostering student engagement, cognitive development, and social-emotional learning (SEL) across early childhood, K-12, and higher education. Constructivist and experiential learning theories provide a foundation for integrating emotionally intelligent robotics into interdisciplinary and transdisciplinary STEAM education. Findings indicate that AEI enhances motivation, problem-solving, and collaboration by creating adaptive learning environments that respond to student affective states. However, challenges such as data privacy, inaccuracies in emotion recognition, and access to robotics must be addressed to ensure ethical implementation. The study advocates for further interdisciplinary research, professional growth, and infrastructure investment to optimize AEI-powered robotics in education. The study also emphasizes prioritizing emotionally intelligent interactions for AEI-driven robotics that represents a shift toward human-centered, AI applications for supporting personalized learning and holistic student development. Future directions include refining affective computing models and fostering ethical AI and AEI frameworks to ensure responsible and effective implementation in early childhood through higher educational settings.

This study aims to evaluate the current integration of Artificial Intelligence (AI) in STEM (Science, Technology, Engineering, and Mathematics) curricula at European universities, focusing on its impact on student outcomes such as problem-solving, analytical skills, and job readiness. A mixed-methods approach was employed, combining a content analysis of 25 STEM curricula with quantitative data from faculty surveys (n = 120) and qualitative insights from student focus groups (n = 50). The study also leveraged recent developments in STEM pedagogy, AI education frameworks, and institutional reporting. The results reveal that although 92% of faculty recognize the importance of AI in STEM education, only 40% feel prepared to teach AI-related content, and just 30% have access to adequate resources. Additionally, only 40% of the analyzed STEM curricula include dedicated AI coursework. Students highlighted the critical role of AI for their future careers but expressed concerns over the limited availability of practical, real-world learning opportunities. The study concludes that despite a broad acknowledgment of AI's significance in STEM, there exists a pronounced gap in faculty preparedness, resource availability, and curriculum integration. These shortcomings may impede the development of the essential skills needed to meet contemporary industry demands. To address these issues, the paper recommends enhancing faculty training programs, making targeted investments in AI infrastructure and technology, and undertaking a comprehensive overhaul of STEM curricula to embed AI-focused courses. Such initiatives are vital to overcoming institutional constraints and unlocking the full transformative potential of AI in STEM education.

In many educational institutions, the adoption of mobile learning continues to be a growing topic. As has been considered recently, wireless technologies are currently employed by mobile technology to spread and exchange data via thinking, communicating, exchanging, and understanding. As a consequence, merging mobile technologies into teaching and learning can enhance the ambiance in higher education. Thus, the purpose of this investigation is to implement mobile learning to examine students’ applications in the framework of educational technology. The use of mobile technologies in STEM education is always efficient and engaging for the students. According to its potential to redefine traditional classroom learning paradigms, the inclusion of cellular phones into STEM (science, technology, engineering, and mathematics) education has drawn significant interest. Three artificial intelligence education (AIEd) paradigm structures are utilized to narrow our exploration of how AI is influencing the STEM sectors. An established cross-disciplinary topic of research dealing with leveraging artificial intelligence (AI) approaches to improve training is defined as AIEd. There seems to be an increasing desire to harness AIEd’s promise to tackle academic barriers in STEM fields. The implications of mobile phones on the educational outcomes of students in STEM education settings are explored in this study. By performing a deep review of existing scholarship and empirical investigation, we look for the impact of mobile devices, functions, and platforms on pupil engagement, understanding, and performance in multifaceted STEM fields. A learning approach entitled STEM Project-Based Learning merges project-based curriculum design with the STEM approach to education. As a whole, pupils’ science and technology literacy were improved by the STEM mobile learning package on the ecosystem. Certain learning packages deserve to be studied isolated, while others might be given outright during offline or personal conversations.

IntroductionArtificial intelligence (AI) has reshaped STEM education by influencing instructional design, learner agency, and ethical frameworks. However, the integration of AI into educational ecosystems raises critical questions regarding pedagogical coherence, assessment reform, and algorithmic ethics.MethodsThis study conducted a systematic review of 41 peer-reviewed publications to examine how AI has been integrated into STEM educational ecosystems. The review focused on peer-reviewed studies published between 2020 and 2025 that addressed AI applications in STEM education, transdisciplinary approaches to AI integration, and the ethical challenges inherent in AI-driven learning environments. A transdisciplinary communication (TDC) framework guided the synthesis of findings. The review followed PRISMA protocols for transparency and utilized Nvivo, Excel and VOSviewer to support thematic coding and bibliometric mapping.ResultsThe analysis identified three emergent themes: (1) the evolving role of student agency in AI-enhanced learning, (2) shifts in assessment paradigms toward adaptive, AI mediated models, and (3) ethical tensions surrounding algorithmic transparency, equity, and automation in pedagogical design. Divergent disciplinary perspectives were noted, with some emphasizing efficiency and other prioritizing inclusive access and epistemic reflexivity.DiscussionDrawing on the Universal Design for Learning (UDL) framework and trustworthy AI principles, this review offers a critical lens on inclusivity and design ethics in AI-mediated learning environments. The results offer a conceptual foundation and a set of actionable strategies for institutions, educators, and policymakers seeking to implement AI technologies in ways that are ethically sound, inclusive, and informed by epistemic plurality in STEM education.

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.

Artificial intelligence technology plays a decisive role in various spheres of life, including education. Modern preschools are faced with the need to adapt to the requirements of the modern world, where technology is a mandatory component. The article defines the regulatory and legal principles of using artificial intelligence technologies in the field of education. Based on the study and systematization of the results of scientific research, directions and methods of using artificial intelligence in the educational field have been identified. Scientific-pedagogical guidelines for the use of generative models of artificial intelligence in a preschool education institution are justified from the author's point of view. Modern possibilities of using artificial intelligence technology in the educational process of a preschool education institution are presented, in particular, the features of using artificial intelligence by parents of preschool children. Also, practical aspects of the use of artificial intelligence by specialists in a preschool education institution are proposed, in particular, ChatGPT, Quillbot, Google Gemini, Vocal Remover. The importance of the right approach to the implementation of artificial intelligence in the educational process to ensure efficiency and safety for children is emphasized. The results of the surveys of parents of preschoolers and specialists of preschool education institutions regarding the potential of using artificial intelligence in preschool education for the development of children are summarized, which requires a careful approach and cooperation between teachers, parents, and the administration of preschool education institutions. The use of artificial intelligence in preschool education is a promising direction that can provide support for preschool education specialists, make the learning process more interesting for children, and contribute to their intellectual development. Therefore, research and development of practices for the use of artificial intelligence in preschool education is an important component of the effective implementation of these technologies.