Research Article
Izida I. Ishmuradova, Sergei P. Zhdanov, Sergey V. Kondrashev, Natalya S. Erokhova, Elena E. Grishnova, Nonna Yu. Volosova
CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep579
ABSTRACT
The development of generative artificial intelligence (AI) has started a conversation on its possible uses and inherent difficulties in the field of education. It becomes essential to understand the perceptions of pre-service teachers about the integration of this technology into teaching practices as AI models including ChatGPT, Claude, and Gemini acquire popularity. This investigation sought to create a valid and trustworthy instrument for evaluating pre-service science teachers’ opinions on the implementation of generative AI in educational settings related to science. This work was undertaken within the faculty of education at Kazan Federal University. The total number of participants is 401 undergraduate students. The process of scale development encompassed expert evaluation for content validity, exploratory factor analysis, confirmatory factor analysis, and assessments of reliability. The resultant scale consisted of four dimensions: optimism and utility of AI in science education, readiness and openness to AI integration, AI’s role in inclusivity and engagement, and concerns and skepticism about AI in science education. The scale demonstrated robust psychometric properties, evidenced by elevated reliability coefficients. Cluster analysis unveiled distinct profiles of pre-service teachers based on their responses, encompassing a spectrum from enthusiastic participants to skeptical disengaged individuals. This study provides a comprehensive instrument for evaluating pre-service teachers’ perceptions, thereby informing teacher education programs and professional development initiatives regarding the responsible integration of AI. Recommendations entail the validation of the scale across varied contexts, the exploration of longitudinal changes, and the investigation of subject-specific applications of generative AI in science education.
Keywords: generative artificial intelligence, science education, scale development, pre-service teacher’s perceptions
Research Article
Rana Saeed Al-Maroof, Ragad M. Tawafak, Waleed Mugahed Al-Rahmi, Khadijah Amru Alhashmi, Ibrahim Yaussef Alyoussef
CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep580
ABSTRACT
Despite the spread of artificial intelligence (AI) tools and applications, the Apple Vision Pro (AVP) stands out for its innovative features compared to other types of wearable technology. Moreover, traditional glasses have been deficient in incorporating many AI innovations that could enhance user experiences and pose new challenges. In response to these innovative aspects, this study aims to develop a theoretical model by integrating constructs from the expectation confirmation model (ECM) (expectation confirmation and satisfaction [SAT]) and aspects from the Uses and Gratifications (U&G) theory. The perceived human likeness of AI mediates the model. This study focuses on the educational domain, aiming to assess how this technology enhances the academic environment and improves learning outcomes. The method used was a survey distributed among 134 participants from Al Buraimi University College, Oman, for two departments: English, linguistics, and information technology. The study consists of seven hypotheses to emphasize the conceptual model. The findings significantly impact predicting the actual use (AU) of AI features of AVP, indicating that users’ expectations and SAT play a pivotal role in technology adoption and are closely linked to the variable human likeness. Similarly, factors such as entertainment value, informativeness, and the lack of web irritations significantly influence technology adoption and are associated with the human likeness variable. However, Informativeness gratification failed to pass the proposal and showed a negative indicator for predicting the AU of AI. The implications drawn from these results suggest that educational institutions should tailor their courses and curricula to promote the effective use of AI.
Keywords: Apple Vision Pro, vision, ECM, U&G theory, human likeness
Review Article
Rita Wong Mee Mee, Fatin Syamilah Che Yob, Lim Seong Pek, Muhammad Fairuz Abd Rauf, Yang Mingmei, Ali Derahvasht
CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep581
ABSTRACT
Computational thinking (CT) has emerged as a foundational skill for young learners, preparing them to navigate and contribute to an increasingly digital world. This bibliometric analysis utilizes 374 articles from the Web of Science database to explore the research landscape surrounding CT in children’s learning, focusing on its applications in language acquisition and cognitive development. Using co-citation and keyword co-occurrence analyses, the study identifies key thematic clusters, including CT’s integration into curricula, its role in enhancing critical thinking, and its social-emotional benefits. Findings suggest that CT holds significant potential in advancing equitable and inclusive education, aligning with Sustainable Development Goal (SDG) 4 by promoting accessible, high-quality learning experiences. Furthermore, CT’s interactive and problem-solving methodologies, such as coding exercises and robotics, actively engage children and encourage collaborative learning, directly supporting SDG 10 by reducing educational inequalities across diverse learning environments. This analysis not only highlights CT’s transformative impact on traditional educational practices but also reveals critical research gaps, particularly in the areas of inclusivity and accessibility. Future research is encouraged to investigate these areas further, advancing sustainable educational strategies that equip children with essential skills for a rapidly evolving technological landscape, thus fostering resilience, adaptability, and creativity among young learners.
Keywords: computational thinking, children’s education, digital literacy, cognitive development, creativity