Search Results (164)

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

This study examines the complexities faced by teachers in rural Finland, focusing on tensions between professional roles and social lives within tight-knit communities. This research addresses the challenges of maintaining well-qualified teaching staff in rural schools. The purpose of the study is understanding the experiences of rural teachers and the challenges they face, including high visibility, community expectations, and blurred boundaries between personal and professional lives. The study uses a qualitative research design, employing in-depth interviews with 21 teachers across two rural locations in Northern Finland. The interviews focused on work experiences, roles, and reflections on the local educational context. The findings highlight that rural teachers experience a blurring of professional and social roles, leading to feelings of being constantly “on the job” and a lack of privacy. Community expectations often dictate their behavior outside of school. The study also reveals divisions within the teaching staff between local and commuting teachers. The study concludes that the cultural narrative of being a teacher in rural contexts includes traditional expectations of teachers, clashing with modern notions of teaching as a profession. The study emphasizes the importance of preparing teachers to be community-ready, acknowledging the challenges of rural teaching. Full article

Graduate Teaching Assistants (GTAs) play a pivotal role in supporting and advancing the educational mission of universities globally. They are fundamental to a university’s instructional workforce and their roles are critical to the undergraduate student experience. This study examines the experiences and perceptions of GTAs (n = 74) at an R2 institution in the Midwest, U.S. Survey results reveal that the majority of surveyed GTAs have been at the institution for at least one year, teach in face-to-face formats with classes typically ranging from 12 to 30, and allocate 11–20 h/week to their instructional duties, although 30% of respondents report >20 h/week. Survey respondents reported a need for more teaching-focused onboarding, discipline-specific training, and more opportunities for feedback on their teaching practices, while almost 50% reported never engaging with discipline-based education research (DBER) literature. Although departmental and institutional training programs were acknowledged, so too was the perception of their lack of accessibility or relevance. Potential strategies for supporting GTAs, particularly early in their careers, include shadowing opportunities, sustained formal classroom management, and pedagogical training that includes an introduction to (and discussion of) the DBER literature, and a reduced teaching load in the first semester. Universities should prioritize and design GTA professional development using a cognitive apprenticeship framework. This would invest in the undergraduate student experience and directly support an institution’s educational mission. It is also highly effective in preparing highly skilled graduates to enter an increasingly connected global workforce and could positively contribute to an engaged alumni base. Full article

In the face of escalating sustainability challenges globally, such as climate change, poverty, inequality, and injustices, the need for a systematic approach to tackle them through the infusion of Sustainable Development Goals (SDGs) in higher education has become increasingly critical. This article explores the crucial issue of professionalizing academic teaching, emphasizing the readiness of academic teachers to cope with sustainability and SDGs in higher education. Using the Stages of Concern Theory and the Concerns-Based Adoption Model (CBAM) to professionalize academic teaching to address SDGs in teaching, learning, and the curriculum, a sample of 1566 academic teachers in nine Latin American universities responded to the survey. This study aimed to answer two key questions, as follows: (1) How do the years of teaching experience affect academic staff’s stages of concern? (2) How do different academic teaching areas influence the academic staff’s stages of concern? The trend reveals that faculty members with fewer than four years of service scored higher than those with twenty or more years. Similarly, academic teaching staff from the Education Sciences have a significantly higher mean score and effect size than faculty members from the Humanities, Engineering, Social Sciences, Sciences, and Health Sciences across all stages of concern. However, despite these differences, professional development initiatives should be designed to match all teaching staff regardless of years of service and subject area by encouraging teamwork and increasing understanding of the critical importance of transformative teaching and learning. Full article

This study integrates the 2017 United Nations ESD framework and UNESCO’s ESD priorities with the Sustainable Development Goal (SDG) of “quality education” and the CDIO (Conceive, Design, Implement, Operate) framework to propose an innovative programming teaching model. A central component is an automatic architecture diagram generation system that visualizes program code structures in real-time, reducing cognitive load and enhancing comprehension of abstract programming concepts such as recursion and data structures. Students complete a project-based assignment—developing a Scheme interpreter—to simulate real-world software development. This model emphasizes system thinking, modular design, and problem solving, aligning with CDIO’s structured learning progression. The experimental results show that students using the system significantly outperformed the control group in their final project scores, demonstrating improved practical programming ability. While cognitive load remained stable, learning motivation decreased slightly, indicating the need for additional affective design support. The findings confirm that the integration of visual learning tools and project-based pedagogy under the CDIO framework supports the development of critical competencies for sustainable development. This approach offers a transformative step forward in programming education, cultivating learners who are capable, innovative, and ready to meaningfully contribute to global sustainability. Full article

Background/Objectives: The COVID-19 pandemic has significantly restricted clinical training for midwifery students, highlighting the need for alternative teaching methods. With the disruption of traditional face-to-face education, online simulation-based training has emerged as an effective alternative for developing essential clinical skills. The acquisition of hands-on skills has a direct impact on students’ self-confidence and clinical performance. Interactive online simulations support the learning process by enhancing both theoretical knowledge and practical competencies. This study aims to evaluate the impact of online simple simulation-based episiotomy repair training on students whose clinical practice was limited due to the COVID-19 pandemic. Methods: A mixed-method approach was used, considering the outcomes from 61 midwifery students. Data were collected via observational questionnaires, which provide an online learning readiness scale and scales for student satisfaction and self-confidence. The analysis included descriptive statistics, McNemar’s, binary logistic regression, and the Mann–Whitney U test. Results: Students who trusted themselves in both opening and repairing an episiotomy after training had more readiness for online learning (t(43) = 2.73, p = 0.009; t(43) = 2.40, p = 0.02). Students with better training performance are more likely to obtain higher scores on the final exam of the Clinical Practice module (rho = 0.33, p = 0.01). Additionally, their performance was a positive and significant predictor of achieving a full mark (b = 0.11, s.e. = 0.05, p = 0.01). Conclusions: Interactive online simulation training improved midwifery students’ hand skills and self-confidence in clinical practice. Such methods should be promoted in circumstances like COVID-19. Full article

The COVID-19 pandemic disrupted medical education as the shift from face-to-face to remote teaching raised concerns about learning outcomes and well-being. However, while established schools’ adaptations have been widely studied, the challenges faced by new institutions in the pandemic’s aftermath remain underexplored. This research provides a valuable case study examining the motivations and concerns of faculty and students at a newly established medical school in the Philippines during its inaugural academic year. Employing a mixed-methods design, data were obtained via validated Likert-based questionnaires assessing motivation and survey questions eliciting concerns. Descriptive and inferential approaches were utilized to analyze data. Results show that faculty motivations were primarily intrinsic, whereas students were driven by a mix of intrinsic and extrinsic factors. Subgroup analyses revealed no difference in motivational profiles across demographic characteristics. Faculty were primarily concerned with their readiness for academic roles, use of modern teaching technologies, and ensuring effective student learning. Students’ concerns focused on training quality, institutional capacity, and the uncertainties of being the pioneer batch. Despite the pioneer cohort’s small size, this study highlighted the importance of understanding faculty and student motivations and concerns, already shaped by post-pandemic realities, to provide targeted support for new medical programs in the evolving post-pandemic landscape. Full article

Background/Objectives: Traditional nutrition education can increase children’s exposure to healthy foods, but preschool teachers face barriers such as limited time and competing priorities (e.g., kindergarten readiness). Integrating nutrition into other learning domains (e.g., science) has been identified as a potential solution. However, teachers need more professional development. We developed the More PEAS Please! program to support preschool teachers’ integration of food-based learning (FBL) and science, seeking to improve children’s science learning, language development, and dietary quality. Methods: In this pilot study, we used a mixed-methods process evaluation to assess the program in five Head Start centers (n = 23 classrooms) across three rural North Carolina counties. We collected teacher data via surveys and interviews. Results: A total of 24 teachers participated in the full intervention by attending a one-day workshop, completing at least one of four core learning modules, and implementing 16 food-based science learning activities in their classrooms. Teachers were Black/African American (81.1%) and 43.56 (11.89) years old. Teachers reported varying engagement levels and high satisfaction with the program, sharing increased confidence in FBL and science integration. However, barriers such as time, technology, and the coronavirus disease (COVID-19) limited full participation. Conclusions: Our findings suggest that the program is feasible and well received in Head Start settings and has promising impacts on classroom teaching practices. The findings will guide revisions to the PEAS program. Future research evaluating the revised program using a comparison group will be explored. Full article

Background/Objectives: Technological advancements are revolutionizing nursing education by improving precision, patient outcomes, and learning experiences. There is an urgent need for systematic frameworks to help nurse educators effectively integrate advanced technologies into their teaching methods. This manuscript introduces the Artificial Intelligence-Driven Technology Adoption in Nursing Education (AID-TANE) framework and operationalizes its use through a pilot study with undergraduate nursing students. Methods: The framework was tested through a convergent mixed-methods pre/post-test study design involving 160 senior-level community health nursing students who participated in an AI-driven educational intervention. Quantitative data were collected using the Facts on Aging quiz, while qualitative data were gathered from a reflective survey. Statistical analyses included paired-sample t-tests and a qualitative content analysis. Results: The study revealed a statistically significant increase in learners’ knowledge about older adults, with mean scores improving from 33.29 (SD = 5.33) to 36.04 (SD = 6.76) post-intervention (t = 5.05, p < 0.001). The qualitative analysis identified four key themes: communication and understanding, patience and empathy, respect for independence, and challenging stereotypes. Conclusions: This study found that AI-driven educational tools significantly improved nursing students’ knowledge about older adults and positively influenced their learning experiences. The findings highlight the need for targeted frameworks like AID-TANE to effectively integrate AI into nursing education, ensuring that students are ready for a technologically advanced practice setting. Full article

Bringing artificial intelligence (AI) and living intelligence into higher education has the potential to completely reshape teaching, learning, and administrative processes. Living intelligence is not just about using AI—it is about creating a dynamic partnership between human thinking and AI capabilities. This collaboration allows for continuous adaptation, co-evolution, and real-time learning, making education more responsive to individual student needs and evolving academic environments. AI-driven tools are already enhancing the way students learn by personalizing content, streamlining processes, and introducing innovative teaching methods. Adaptive platforms adjust material based on individual progress, while emotionally intelligent AI systems help support students’ mental well-being by detecting and responding to emotional cues. These advancements also make education more inclusive, helping to bridge accessibility gaps for underserved communities. However, while AI has the potential to improve education significantly, it also introduces challenges, such as ethical concerns, data privacy risks, and algorithmic bias. The real challenge is not just about embracing AI’s benefits but ensuring it is used responsibly, fairly, and in a way that aligns with educational values. From a sustainability perspective, living intelligence supports efficiency, equity, and resilience within educational institutions. AI-driven solutions can help optimize energy use, predict maintenance needs, and reduce waste, all contributing to a smaller environmental footprint. At the same time, adaptive learning systems help minimize resource waste by tailoring education to individual progress, while AI-powered curriculum updates keep programs relevant in a fast-changing world. This paper explores the disconnect between AI’s promise and the real-world difficulties of implementing it responsibly in higher education. While AI and living intelligence have the potential to revolutionize the learning experience, their adoption is often slowed by ethical concerns, regulatory challenges, and the need for institutions to adapt. Addressing these issues requires clear policies, faculty training, and interdisciplinary collaboration. By examining both the benefits and challenges of AI in education, this paper focuses on how institutions can integrate AI in a responsible and sustainable way. The goal is to encourage collaboration between technologists, educators, and policymakers to fully harness AI’s potential while ensuring that it enhances learning experiences, upholds ethical standards, and creates an inclusive, future-ready educational environment. Full article

This paper aims to provide a framework for educators to effectively navigate the complexities of artificial intelligence (AI) integration while maintaining the core principles of effective teaching and learning, specifically through the lenses of Dewey’s experiential learning, situated cognition, and distributed cognition. By examining these principles, we explore the essential role of the teacher in this context and the implications of ignoring AI. The integration of AI can enhance personalized learning experiences, facilitate adaptive learning systems, and support educators in fostering critical thinking and problem-solving skills among students. Additionally, we highlight the challenges and ethical concerns associated with AI use in education. We argue that AI should be viewed as a tool that enhances, rather than replaces, the role of educators, emphasizing the importance of maintaining the educator’s role in guiding and supporting student learning. This framework serves as a valuable resource for educators seeking to embrace AI’s potential while ensuring that teaching remains centered on student engagement and successfully achieving learning outcomes as we prepare the next generation to be an AI-ready workforce. Full article

With built environment (BE) programs emphasizing industry-based learning through cadetships and work-integrated experiences, students face significant stress in balancing studies and work. The research aims to investigate the relationship between specific stressors encountered by students in various BE disciplines and their subsequent impact on mental health and academic outcomes. An online survey of BE students across Australian universities examined academic and work stressors in architecture, engineering, construction management, property, and planning disciplines. ANOVA analysis compared their intensity, health impacts, and academic outcomes across the disciplines. The study found that academic stressors—self-expectations, test anxiety, and heavy workload—were consistent across BE disciplines. Work-related stressors, including time pressure and work–study balance, were also widespread, with architecture students particularly concerned about career relevance and workplace support. Mental health outcomes showed moderate anxiety and depression across all disciplines, but architecture students reported higher stress levels and greater academic impact. This research sheds light on systemic factors impacting BE students’ well-being and academic performance, emphasizing the need for targeted support. It advocates flexible teaching, enhanced work-integrated learning, and tailored mental health resources. Further study is needed to develop a causal model linking stressors to outcomes and to rethink BE education for better student support and career readiness. Full article

As the development of technology and business improvement is rapidly advancing these days, higher education (HE) should continually provide and develop up-to-date knowledge and skills for students. This is crucial for training competitive specialists, addressing digital transformation and enhancing digital readiness of HE institutions, as well as increasing students’ employment opportunities. Therefore, this paper explores the development and implementation of the new courses for teaching Rapid Application Development (RAD) on the Oracle Application Express platform at five European universities. Consequently, a new and flexible methodology for the integration of developed courses into existing study programs with different integration strategies is proposed and implemented. The effectiveness of the courses’ integration, implementation and students’ satisfaction were evaluated using Kirkpatrick’s model. The results reveal that students’ knowledge of RAD increased after completing the courses, which can improve students’ employment opportunities and promote digital transformation in HE institutions and studies. In addition, a majority of the students expressed positive feedback for both modules, finding the courses relevant, well delivered and motivating for future study. This study and its results are expected to inspire researchers, teachers and practitioners for further work towards the digital transformation of HE and offer valuable insights for future HE digitalization and research. Full article

This study explored how bilingual Latine teacher candidates (TCs)—undergraduate students in a teacher preparation program working toward obtaining a teaching license and identifying as individuals from Latin America or of Latin American descent, using the gender-neutral term in Spanish, “Latine”, to encompass all genders—identified and addressed cultural assumptions in mathematics questions on the STAAR (State of Texas Assessments of Academic Readiness) test. Twenty Latine TCs who were enrolled at a major southern research university (pseudonym: Southland University) program reviewed fifth-grade STAAR mathematics questions to assess cultural assumptions and suggest revisions for cultural relevancy. The findings reveal that the TCs identified cultural assumptions in questions about probable unfamiliar currency, non-standard measurement units, and culturally specific terms that could impede students’ understanding. In their revisions, the TCs proposed simplifying language and provided contextual examples to enhance clarity, aligning with the first tenet of culturally relevant pedagogy (CRP). However, few revisions addressed CRP’s second and third tenets, which involve fostering cultural competence and critical consciousness. This study underscores the importance of integrating comprehensive CRP training in teacher preparation programs to better equip TCs to create culturally responsive teaching practices. The findings contribute to ongoing discussions about improving the cultural relevancy in standardized tests and supporting diverse student populations in achieving academic success. Full article

We present an automatic approach for generating learning problems for teaching introductory programming in different programming languages. The current implementation allows input and output in the three most popular programming languages for teaching introductory programming courses: C++, Java, and Python. The generator stores learning problems using the “meaning tree”, a language-independent representation of a syntax tree. During this study, we generated a bank of 1,428,899 learning problems focused on the order of expression evaluation. They were generated in about 16 h. The learning problems were classified for further use with the used concepts, possible domain-rule violations, and required skills; they covered a wide range of difficulties and topics. The problems were validated by automatically solving them in an intelligent tutoring system that recorded the actual skills used and violations made. The generated problems were favorably assessed by 10 experts: teachers and teaching assistants in introductory programming courses. They noted that the problems are ready for use without further manual improvement and that the classification system is flexible enough to receive problems with desirable properties. The proposed approach combines the advantages of different state-of-the-art methods. It combines the diversity of learning problems generated by restricted randomization and large language models with full correctness and a natural look of template-based problems, which makes it a good fit for large-scale learning problem generation. Full article