RLT Journal

Traditional teaching methods in Aircraft Maintenance Technology (AMT) often struggle to maintain student engagement, motivation, and knowledge retention. While gamification has been recognised as a potential solution, its application in AMT education remains underexplored, particularly in bridging theoretical knowledge with practical skill acquisition. This study investigates the effectiveness of Kahoot-based gamification in enhancing student engagement, motivation, knowledge retention, and practical skills development. A mixed-methods approach was employed, integrating quantitative surveys, pre- and post-assessments, and qualitative interviews with students and instructors. The study assessed 120 participants, revealing statistically significant improvements: engagement increased by 43.8%, motivation by 55.2%, knowledge retention by 41.2%, and practical skills by 51.6%. Qualitative findings supported these results, with students reporting higher participation, improved concept recall, and greater enthusiasm. Instructors observed enhanced confidence in applying theoretical knowledge. Despite these benefits, challenges such as internet connectivity issues, time constraints, and question format limitations were identified. The study recommends a blended learning approach integrating gamification with hands-on training, scenario-based exercises, and emerging technologies such as Augmented Reality and Virtual Reality. Beyond AMT, these findings highlight gamification’s potential in technical and vocational education, emphasising the need for future research on adaptive gamification models and AI-driven learning platforms.

For this study, 350 university students in Germany were surveyed to understand how they perceive ChatGPT’s educational advantages and challenges. Using a combination of quantitative and qualitative methods, it found out that students tend to see ChatGPT as helpful for academic performance (53.14%), writing (47.14%), and exam preparation (50.00%). Nonetheless, a large majority of people expressed doubt regarding its ability to understand queries (61.72%), reliability (52.29%), privacy (52.57%), bias (47.43%), security (55.14%) and displaced jobs (56.29%). These concerns were reinforced by open ended responses, which showed that attitudes towards AI can be based on factors such as a person’s digital literacy and their experience with AI. In the study, the researchers propose a need to incorporate AI education into curricula in order to teach students to critically assess AI- generated content and to identify biases. Moreover, it suggests setting ethical standards that AI systems need to meet such as accuracy, security, and transparency. Perspectives between cultures vary, and require the teacher to conduct more global research, and approach teaching context specifically. The overarching goal is to promote the thoughtful, ethical, and context-aware integration of AI into educational practices worldwide.

The integration of generative artificial intelligence (AI) tools, such as ChatGPT and DALL-E, presents transformative opportunities and challenges for K-12 education. This mixed-methods study investigates educators’ perceptions, familiarity, and preparedness for AI adoption, as well as institutional strategies and barriers. Quantitative findings indicate strong relationships between AI familiarity, perceived readiness, and institutional planning stages. Qualitative analysis highlights challenges such as insufficient professional development, ethical concerns, and infrastructural inequities, alongside opportunities for enhancing personalised learning and operational efficiency. The findings underscore the need for targeted training, equitable resource access, and clear institutional policies to ensure effective and ethical AI integration. This research offers actionable insights for educators, policymakers, and leaders seeking to navigate AI’s potential in K-12 education.

In the dynamic landscape of higher education, the integration of artificial intelligence (AI) into learning has emerged as a transformative force, ushering in tailored, adaptive, and immersive educational experiences for undergraduate university students. This study employed a thematic analysis to scrutinize focus group discussions with 25 undergraduate participants majoring in English language at a university in Jordan to examine how these learners engage with AI-supported self-regulated learning. The findings revealed five prominent themes: accessibility and inclusivity, adaptive feedback mechanisms, impact on learning habits, technological proficiency and preparedness, and social dynamics in AI-infused learning. Within these themes, diverse student views were categorized according to Ab Rashid and Yunus’ (2016) framework of perception evaluation: the Avid Category (very positive perception), the Analytic Category (enthusiast but critical), the Anxious Category (enthusiast but with worries and fear), and the Agnostic Category (negative view). These varied views collectively reveal the profound implications of AI integration in reshaping the educational landscape. This study contributes to the discourse on AI in education by highlighting the importance of integrating AI tools with pedagogical approaches that foster independent learning and critical engagement. Recommendations include combining AI feedback with peer reviews and instructor guidance, enhancing digital literacy programs, and ensuring robust support measures. By addressing these areas, educational institutions can create more inclusive and effective AI-supported learning environments that cater to diverse student needs and promote a balanced approach to technology in education.

Many universities implemented blended and hybrid delivery for the first time during the COVID-19 pandemic, and as such, the use of terms that relate to various manifestations and implementations of blended learning has increased significantly by all higher education stakeholders. However, the meaning ascribed to these terms is often inconsistent and can lead to confusion, making it difficult to set expectations clearly for both staff and students. This study aimed to investigate how higher education staff understand and use these terms and to identify sources of confusion and barriers to adopting standardised definitions. We surveyed 152 higher education staff and asked them to provide definitions of each term as well as completing a categorisation task. An applied thematic analysis identified two factors that were present across definitions: choice (no choice, student choice and choice not specified) and modality (mixed but separate, dual delivery and mixed not otherwise specified). Our findings reveal significant discrepancies in understanding, particularly regarding hybrid learning, which was often conflated with other modalities and involved definitions where neither choice nor modality was clearly specified. Blended learning was most consistently defined and identified as involving separate online and in-person components with no student choice as to the modality in which they could engage with each component. Hyflex learning, despite being less familiar to many participants, was accurately associated with dual delivery and the maximum student choice. Our results underscore the need for clearer terminology and for all stakeholders to provide maximally descriptive definitions. The use of any broad category term should be accompanied by a specific definition that at minimum describes choice and modality, but where best practice would be to encompass additional information based on existing frameworks.

This study aims to investigate the factors influencing teachers’ intentions to integrate Virtual Reality (VR) technology into their educational practices, utilising the Unified Theory of Acceptance and Use of Technology (UTAUT-2) framework. The research involved adapting and validating the ‘Acceptance of Mobile Immersive Virtual Reality in Secondary Education Teachers’ scale to the Turkish context, ensuring cultural relevance and psychometric reliability. Data were collected from 213 in-service teachers with prior experience in using VR in education. The results of the Confirmatory Factor Analysis (CFA) confirmed the validity of the adapted scale. The findings indicate that effort expectancy, social influence, personal innovativeness and hedonic motivation significantly predict teachers’ behavioural intentions to adopt VR technology. However, contrary to expectations, performance expectancy and facilitating conditions did not show a significant impact. These results underscore the importance of focusing on the ease of use and social support mechanisms, as well as fostering a culture of innovation amongst educators, to successfully integrate VR into educational settings.