RLT Journal

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.