The expansion of remote work practices has transformed work-integrated learning in higher education, including university internships. Although remote internships gained visibility after the COVID-19 pandemic, empirical research on their pedagogical design, stakeholder perceptions, and institutional implementation remains limited. This study addresses this gap by analysing stakeholders’ perceptions of remote internships and examining the PREVIEW Remote Internship Blueprint (RIB) as a pedagogical infrastructure that can support the transition from conventional remote internships to intelligent remote internships. A qualitative phenomenological design was adopted. Data were collected through focus groups with students, company mentors, and academic supervisors from five European higher education institutions. Content analysis, semantic analysis, and a SWOT framework were applied using CAQDAS software. Findings indicate that remote internships are perceived as viable and pedagogically meaningful when intentionally structured and adequately supported, particularly in relation to flexibility, accessibility, internationalisation, and the development of digital and transversal competences. At the same time, participants reported challenges related to reduced social interaction, risks of isolation, uneven mentoring capacity, and difficulties in providing timely formative feedback and assessing transversal competences at scale. Building on these qualitative insights, the study discusses how the structured phases, roles, and evidence flows defined in the PREVIEW RIB make it artificial intelligence-ready, enabling learning analytics and educational AI to enhance monitoring, personalisation, formative assessment, and university–enterprise coordination while maintaining human agency and aligning with ethical and sustainability principles.
This research examines the complex relationships between artificial intelligence learning motivation (AILM), intention (AILI), efficacy (AILE), and student creativity (SC) among Saudi Arabian undergraduate and graduate students. A quantitative approach was employed, gathering data from bilingual questionnaires administered to 466 students across five Saudi universities, utilizing validated scales for AILM (intrinsic/extrinsic), AILI, AILE, and SC. Partial least squares structural equation modeling (PLS-SEM) was utilized to access direct and indirect effects. The analysis established the nine hypotheses, signifying that AILM significantly influences AILI and AILE, thus, enhances SC. AILE and AILI could act as mediators between AILM and SC, whereas the serial mediation path (AILM→AILI→AILE→SC) represents the highest mediator. The research implies the importance for Saudi Arabia's Vision 2030-compliant education reforms that promote both intrinsic and extrinsic motivation (ILM and ELM), intention reciprocated into learning behavior, and the development of AILE to enhance SC.
La llegada de la COVID-19 provocó el cierre de las instituciones educativas y la interrupción de la modalidad presencial, impulsando la incorporación de plataformas virtuales de aprendizaje y el uso de recursos digitales como medios para la interacción con los estudiantes y el desarrollo del proceso académico. Este estudio tiene como objetivo determinar qué aspectos de las plataformas virtuales de aprendizaje resultan innovadores y amigables en la interacción docente-alumno en centros educativos de enseñanza secundaria dominicanos, a partir de un diseño ex post facto basado en las experiencias vividas por los participantes. Los resultados evidencian que utilizaron una amplia diversidad de plataformas virtuales, entre las que destacan Zoom, Google Meet, WhatsApp, Classroom, Teams y Moodle. Asimismo, se identifican como aspectos innovadores y amigables las características funcionales de las plataformas, así como la integración de herramientas y recursos digitales considerados indispensables. Otro hallazgo relevante es la alta valoración otorgada a la atención de los estilos de aprendizaje, lo que refuerza la efectividad pedagógica de estos entornos. Finalmente, se concluye que la interacción docente-alumno debe promoverse de forma participativa, colaborativa y comunicativa, con el fin de reducir barreras y propiciar un ambiente de aprendizaje cercano y afectivo.
The growing presence of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in educational contexts requires future education professionals to develop both pedagogical competences and technical knowledge. This study examines pedagogy students’ self-perceived competences related to the educational use of GenAI tools. Specifically, it analyses their levels before and after a university training intervention, as well as potential differences according to sex and the relationship with academic motivation and self-perceived creativity. A quasi-experimental pretest–posttest design was implemented with 112 pedagogy students at the University of Málaga. The study assessed three dimensions: pedagogical competences for creating materials with GenAI tools, pedagogical competences for planning learning activities with GenAI tools, and technical knowledge of GenAI. Data were collected through a validated self-assessment instrument. The results revealed moderate initial levels of pedagogical competences and comparatively lower levels of technical knowledge prior to the intervention. After the training experience, statistically significant improvements were observed across all dimensions, with large effect sizes, particularly in technical knowledge. No significant differences were found according to sex. Academic motivation showed limited associations with competency development, whereas self-perceived creativity in designing digital activities was positively related to several competency dimensions. These findings highlight the importance of integrating training on GenAI tools into higher education programmes to support the development of pedagogical and technical competences among future pedagogy professionals.
The contemporary educational landscape is undergoing a radical transformation characterized by the transition from the Gutenberg Parenthesis to the fluid complexity of the Infosphere. This study proposes a novel pedagogical framework that conceptualizes the integration of Artificial Intelligence in education as an epistemological shift based on the logic of the palimpsest. Beyond the binary discourse of efficiency versus integrity, this research analyzes the classroom as a stratified site where knowledge is reconstructed through the superposition of algorithmic and human layers. The methodology combines a hermeneutic analysis of AI architectures, specifically adapting the U-Net convolutional network structure as a heuristic isomorphism for curricular design, with a prospective modeling of institutional data regarding AI adoption. The findings, visualized through the assimilation gap and a theoretical efficacy model, suggest that treating AI outputs as digital palimpsests significantly reduces hallucination rates while restoring critical authorship. This stratified reading strategy transforms the faculty into a postdigital curator, ensuring that the human trace remains visible within the synthetic archives of the future.
