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Bilim, Teknoloji, Mühendislik ve Matematik disiplinlerinin entegrasyonuna dayanan (Science, Technology, Engeenering, Mathematics - STEM) STEM eğitimi, son zamanlarda erken çocukluk araştırmacıları ve eğitimcilerinin ilgisini çekmektedir. Bu ilginin nedenleri arasında, STEM eğitimi ile ilgili çalışmaların çocukların bilimsel süreç, okula hazır oluş, iletişim, problem çözme ve yaratıcılık gibi becerilerini desteklediği yönündeki bulguları düşünülebilir. STEM eğitiminin sunduğu bakış açısı erken çocukluk eğitimini çeşitli şekillerde etkilemiştir. Erken çocukluk eğitiminin temel özelliği olan öğrenme merkezlerinin yanında STEM öğrenme merkezleri oluşturulmaya ve resimli çocuk kitapları STEM ile ilişkilendirilerek kullanılmaya başlamıştır. STEM eğitiminin amacına ulaşabilmesi için etkili bir şekilde planlanması ve uygulanması gerekmektedir. Çocukların gelişim seviyelerine uygun deneyimleri içermesi ve STEM’i oluşturan disiplinlerin yetişkinlerde olduğu gibi değil çocukların gelişim seviyelerine uygun bir şekilde ele alınması önem taşımaktadır. Ancak, özellikle erken çocukluk döneminde STEM eğitimi konusunda yeterli Türkçe kaynağın olmaması ve STEM uygulamalarının çocuk merkezlilikten uzak olması STEM eğitiminin etkili bir şekilde uygulamasını engelleyebilmektedir. Bu çalışmada, eğitimcilere ve araştırmacılara erken çocukluk döneminde STEM eğitimine yönelik kavramsal bir çerçeve ve etkili uygulamalar geliştirmelerine rehberlik edebilecek öneriler sunulması amaçlanmıştır.

This study investigates the relationship between undergraduates’ technological readiness, self-efficacy, attitude, and usage of artificial intelligence (AI) tools. The study leverages the technology acceptance model (TAM) to explore the relationships among the study’s variables. The study’s participants are 176 undergraduate students from a public university in southwestern Nigeria. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to analyze the responses from the participants. The questionnaire has six constructs measured on a 5-point Likert scale. The results show that undergraduates’ technological self-efficacy determines their usage of AI tools and perception of AI tools’ ease of use, but this does not determine their perception of the AI tools’ usefulness and attitude towards AI tools usage. Also, technological readiness was found to determine the perception of the AI tools’ usefulness, perception of AI tools’ ease of use, and technological self-efficacy among undergraduates but does not determine their usage of AI tools and attitude towards AI tools usage. In addition, undergraduates’ attitude towards AI tools was considered the primary determinant of the usage of AI tools. It was concluded that some factors determine the adoption of AI tools, which are interrelated. Educators can play a pivotal role in empowering students to harness the power of AI tools by encouraging their usage under well-coordinated guidance rather than imposing outright restrictions. By fostering AI literacy and equipping students with the knowledge and skills to navigate these innovative technologies, educators can instil the confidence and competency needed to integrate AI tools into various academic activities seamlessly.

The purpose of this longitudinal investigation was to examine the effectiveness of a comprehensive, integrated curricular and co-curricular program designed to build community, provide academic and social support, and promote engagement in academically purposeful activities resulting in more equitable environments for historically underrepresented, low-income science, technology, engineering, and mathematics (STEM) information technology (IT) students. The study also focused on the role that the sense of belonging and academic hope play in enhancing persistence to degree completion. Program participants had significantly higher persistence rates compared to a matched comparison group. Additionally, STEM-specific belonging and academic hope significantly predicted students’ intentions to persist to degree completion in IT. A major finding was that STEM domain–specific belonging was a stronger predictor of persistence than general belonging. Our investigation has implications for the role that cohort-based programs, industry engagement, peer mentoring, proactive advising, undergraduate research opportunities, career preparation, and leveraging need-based financial aid play in ensuring equity in STEM.

Artificial intelligence (AI) is becoming increasingly relevant, and students need to understand the concept. To design an effective AI program for schools, we need to find ways to expose students to AI knowledge, provide AI learning opportunities, and create engaging AI experiences. However, there is a lack of trained teachers who can facilitate students’ AI learning, so we need to focus on developing the capacity of pre-service teachers to teach AI. Since engagement is known to enhance learning, it is necessary to explore how pre-service teachers engage in learning AI. This study aimed to investigate pre-service teachers’ engagement with learning AI after a 4-week AI program at a university. Thirty-five participants took part in the study and reported their perception of engagement with learning AI on a 7-factor scale. The factors assessed in the survey included engagement (cognitive—critical thinking and creativity, behavioral, and social), attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI. We used a structural equation modeling approach to test the relationships in our hypothesized model using SmartPLS 4.0. The results of our study supported all our hypotheses, with attitude, anxiety, readiness, self-transcendent goals, and confidence being found to influence engagement. We discuss our findings and consider their implications for practice and policy.

Artificial intelligence (AI), as an emerging technology, has been widely used in STEM education to promote the educational assessment. Although AI-driven educational assessment has the potential to assess students’ learning automatically and reduce the workload of instructors, there is still a lack of review works to holistically examine the field of AI-driven educational assessment, especially in the STEM education context. To gain an overview of the application of AI-driven educational assessment in STEM education, this research conducted a systematic review based on 17 empirical research published from 2011 January to 2023 April. Specifically, this review examined the functions, algorithms, and effects of AI applications in STEM educational assessment. The results clarified three main functions of AI-driven educational assessment, namely academic performance assessment, learning status assessment, and instructional quality assessment. Moreover, the systematic review found that both traditional algorithms (e.g., natural language processing, machine learning) and advanced algorithms (e.g., deep learning, neural fuzzy systems) were applied in STEM educational assessment. Furthermore, the educational and technological effects of applying AI-driven educational assessment in STEM education were revealed. Based on the results, this research proposed educational and technological implications to guide the future practice and research of AI-driven educational assessment in STEM education.