Search Results for
"Engineering education"

This chapter discusses the evolving relationship between artificial intelligence and education by arguing that AI should empower educators rather than replace them. While AI offers significant benefits such as personalized instruction, improved lesson planning, and enhanced assessment capabilities, education fundamentally requires human connection, empathy, and adaptive instruction that AI cannot replicate. We suggest a five-pronged model that redefines educators’ roles in light of AI advancements and in conjunction with the Technological Pedagogical Content Knowledge (TPACK) technology integration framework. Our model posits the role of educators with AI integration as Innovators, Instructional Designers, Facilitators of Critical Thinking, Ethical and Moral Guides, and Social-Emotional Mentors. The chapter explores practical applications of AI for content creation across curriculum planning, presentational instruction, interactions, and assessment, and offers guidance for educators to effectively integrate AI while maintaining pedagogical oversight and the human element of teaching. The role of educators worldwide is rapidly evolving in the face of ongoing technological advancements, especially with artificial intelligence (AI) recently emerging as a disruptive tool in many facets of the educational landscape. We now live in a world where educators have real concerns about the potential impacts of AI on education, including benefits and unintended consequences. Some have documented threats to teaching and learning, such as academic integrity concerns (Perkins, 2023), that prompt maintaining a cautious and balanced approach to AI integration. Yet, AI can also be a powerful tool to transform how teachers design, deliver, and adapt their curriculum (Adigüzel et al., 2023). As our classrooms grow increasingly more diverse, AI offers educators the ability to create tailored materials that cater to individual learning needs (Kolluru et al., 2018), streamline lesson planning (Bucchiarone, 2024), and enrich the overall learning experience (Barrios-Beltran, 2024). AI’s potential to enhance education is also linked to several positive learning outcomes, including enhanced engagement, motivation, and skill development (Xu, 2024). Furthermore, this growing body of research serves to highlight several important impacts on education. First, there is a need for educational stakeholders to better prepare students and teachers for the appropriate use of AI. AI competencies need to be integrated into content curricula to assist learners in developing such skills and appropriate uses, and we need to provide more AI professional development for educators at all levels. Second, the role educators play in terms of shaping the use of AI is still evolving. Notably, the presence of AI in education has significantly increased the imperative for educators to better understand AI tools, their uses and misuses, and to leverage them in their teaching and learning practices. As Velander et al. (2024, p. 4086) note, “This ʼnew normality’ requires teachers to be able to teach about different aspects of AI related to different subject topics as well as different age groups.” We argue here that the role of educators should be redefined to empower them to harness the potential of AI, rather than be replaced by it. By integrating AI-driven methods, teachers can not only save time (Fitria, 2023) but also unlock new creative possibilities in the classroom (Habib et al., 2024) that have the potential to empower them to meet the unique challenges of modern education. To further illustrate these opportunities, this chapter explores how educators must transform their roles as educators to successfully integrate AI into their instructional practices. Whether you are a veteran educator or new to teaching, this chapter will provide actionable insights and examples to help you integrate AI into your educational practices. © 2026 by IGI Global Scientific Publishing. All rights reserved.

This chapter focuses on the use of active learning strategies in entrepreneurial learning with an example of case-based learning to prepare university students for the future of work. The chapter aims to expand the understanding of the benefits of planning and applying a combination of active learning strategies with an example of case-based learning in the entrepreneurial learning programme of the faculty of business education. The author used a qualitative approach in terms of an exploratory systematic review of the literature concerning the role of active learning in entrepreneurial learning (EL). Based on the analysis of the findings, the author concludes that the solution to effective entrepreneurship education is the use of active learning activities to stimulate students’ mindset to develop entrepreneurial skills. The practical implications are that the use of active learning in EL can develop a spirit of creativity and empower students to become job creators instead of job seekers as HEIs prepare them for work environments in wage or self-employment after graduating. © 2026 by IGI Global Scientific Publishing. All rights reserved.

Artificial intelligence (AI) is changing the landscape of technical skills training worldwide. This narrative review sums up recent scholarly research to examine how AI is used into vocational and industrial training contexts, the opportunities it affords for personalized and adaptive learning, and the challenges it poses in practice. Our work outlines the state-of-the-art applications of AI aligned with 'Industry 4.0' needs, and narrates thematic areas including personalization, curriculum innovation, practical skills training, teacher competency development, administrative uses, and global perspectives. Findings reveal that AI technologies are enabling more flexible, inclusive, and industry-responsive vocational learning experiences, while also raising important issues around data privacy, bias, teacher readiness, and equity. The discussion provides a critical analysis of these developments and offers insights into policy implications and future research directions. We conclude that AI holds significant promise for enhancing vocational and industrial education, provided that stakeholders address implementation barriers and ethical concerns to harness AI's potential for improving workforce development and lifelong learning. © 2025 IEEE.

Outcome-Based education for engineering Educators presents a student-centered approach that aims to produce graduates with a well-rounded skill set, ready to contribute effectively to the engineering profession and adapt to the dynamic nature of technology and industry. It places an emphasis on preparing students for real-world applications and ensuring they acquire the necessary skills and knowledge to excel in their future careers. Instead of focusing on fulfilling accreditation requirements, the book discusses the constructive alignment between learning outcomes, authentic assessments, and teaching-learning activities. It demonstrates how defined learning outcomes should be aligned with the broader goals and objectives of the engineering program that reflect industry needs, technological advancements, and professional standards. The book shows how curriculum may integrate theoretical knowledge with practical applications, hands-on projects, and real-world problem-solving exercises. The book is intended for engineering educators studying and incorporating evidence-based teaching practices. It will also interest graduate students taking courses in engineering management and higher education pedagogy. © 2026 Faris Tarlochan.

In an era defined by rapid technological advancement and shifting workforce demands, business education undergoes a transformative evolution. The integration of artificial intelligence (AI), a focus on skill development, and the rise of hybrid and blended learning models reshapes how future business leaders are trained. These innovations enhance the accessibility and personalization of education while aligning curricula with the needs of the global economy. As institutions adapt, the intersection of technology, pedagogy, and real-world application helps prepare future professionals. Innovating Business education: AI, Skill Development, and Hybrid and Blended Models explores how emerging technologies like AI, along with evolving teaching methods such as hybrid and blended learning, transform the landscape of business education. It examines the shift toward practical, skills-based learning and how these innovations better prepare students for the demands of a changing global workforce. This book covers topics such as educational technology, professional development, and behavioral analytics, and is a useful resource for business owners, educators, computer engineers, academicians, researchers, and scientists. © 2026 by IGI Global Scientific Publishing. All rights reserved.

The integration of Large Language Models (LLMs) into Creative Project-Based Learning (CPBL) enhances student engagement and creativity. This chapter proposes a framework embedding LLMs within CPBL using the Double Diamond model, guiding students through Discover, Define, Develop, and Deliver. LLMs serve as cognitive tools for idea generation, refining problem statements, and articulating outputs. The framework highlights prompt engineering, ethical considerations, and educator guidance to maintain student agency and ensure responsible AI use. The integration is evaluated using quantitative methods (Ennis’ taxonomy, modified Torrance Test of Creative Thinking) and qualitative methods (interviews, prompt analysis). Findings suggest LLMs improve student autonomy, divergent thinking, and collaboration, offering valuable insights for educators and instructional designers. The chapter also explores future research into AI’s pedagogical and ethical impacts in education. © 2026 by IGI Global Scientific Publishing. All rights reserved.

The cultivation of high-quality engineering talent in newly established undergraduate programs presents significant challenges, particularly in integrating theoretical knowledge with practical and innovative capabilities. This study proposes and implements a “triadic integration” teaching model that synergistically combines project-based learning, interactive teaching, and practice-oriented instruction to enhance students’ engineering competencies in a materials innovation course. Project-based learning forms the backbone of the model, guiding students through progressively complex tasks—from foundational to comprehensive and advanced projects—while integrating engineering-material innovation competitions to create a closed-loop “teach–learn–compete” pathway. Interactive teaching strategies, including heuristic, inquiry-based, and participatory methods, foster a student-centered learning ecosystem that enhances engagement, collaboration, and critical thinking. Practice-oriented instruction translates theoretical knowledge into practical application through laboratory experiments, simulation, and full-cycle research projects, cultivating problem-solving and innovation skills in authentic engineering contexts. The model’s effectiveness was evaluated through student performance, competition achievements, and iterative seminar-based reflection among instructors. Results indicate that the triadic approach not only improves students’ technical competence and innovative capacity but also provides a scalable, replicable framework for curriculum innovation in newly established undergraduate programs. This study offers valuable insights for the design and reform of engineering education curricula worldwide. © © 2025 Sun, Wu, Qian, Xu and Wang.

On-the-job training plays a key role in developing the professional skills and practical experience of students in engineering education programs. In this regard, the aim of this study was to create a toolkit for analyzing the factors that influence the development of these skills and the integration of students into the professional environment. The methodology included surveying three target groups—students, teachers, and employees of companies involved in the implementation of practice-oriented training at universities—as well as testing three updated training modules integrated with practical training. Based on the collected data, regression models were constructed reflecting the influence of four blocks of factors (organization of practice, digital and production resources, educational support, project activity) on the development of skills in the application of engineering equipment and technologies. The most significant predictor was the block of digital and production resources (standardized regression coefficient β = 1.666, p < 0.001, R2 = 0.684 for the experimental group of students), which emphasizes the importance of the technological content of the learning environment. As a result of training using the updated modules, an increase in the level of proficiency in the application of engineering tools and technologies was recorded. The average score for this criterion was 4.52 in the control group and 4.74 in the experimental group. Progress was also noted in key skills such as basic economic and legal knowledge (F6), self-analysis (F9), and planning (F7). This confirms the effectiveness of a structured approach to combining theoretical and practical training. The proposed structured toolkit for analyzing influencing factors, tested through a multilateral survey and pilot implementation, is a key innovation of the study and can be used by universities and companies to modernize workplace training programs. © © 2025 Karstina, Tussupbekova and Mussenova.

This chapter provides a thematic and bibliometric review of the use of artificial intelligence (AI) in education to promote green learning environments and sustainability. We used VOSviewer to map research trends and collaborative clusters in accordance with SDG 4.7 under the Four-in-Balance model: Vision, Infrastructure, Digital Content, and Competencies. The literature showed rapid expansion in learning analytics and e-learning; sustainability and the SDGs; higher education's institutional reform and digital adoption; and emerging technologies, including big data, generative AI, and Industry 4.0. The results show uneven progress: Vision and Competencies lag, leading to fragmented, reactive adoption, while Infrastructure and Digital Content have improved, including intelligent tutoring, IoT-enabled classrooms, and adaptable resources. Few institutions incorporate environmental goals into strategy, and persistent gaps in AI literacy and sustainability skills limit responsible use. © 2026 by IGI Global Scientific Publishing. All rights reserved.

Background: In recent years, digital competencies have become progressively more valuable in the labour market, and digital skills are increasingly becoming a prerequisite for employment. Objective: Our research focuses on the digital literacy of human capital in the 27 EU Member States. Methods: Using mathematical-statistical methods, correlation graph matrix and panel regression, we searched for significant correlations that can impact the digital readiness of human resources. The Hausman test suggests that the random effects panel regression model should be used along with the two main types of multivariate panel regression. We used Eurostat databases and the first dimension (human resources) of the DESI index for 2017–2022 for our analyses. Results: Our analysis revealed several significant factors influencing digital readiness: a 10% increase in STEM student enrolment raises the DESI index by 6 percentage points; €10,000 higher real GDP per capita increases digital readiness by 4.9%; a €10 billion increase in public education spending improves the HC index by 1%. Conversely, each additional 1000 students in mobility programs decreases the HC index by 0.028 percentage points, while each decade of delayed EU accession correlates with a 2.3% lower HC index. Conclusions: Complementing descriptive statistics with our methods helps link macroeconomics and practical solutions for digital readiness. © The Author(s) 2025