El Oro province undergoes a high rate of student’s withdrawal from distance courses offered by the local university. The distance education model gives for granted that the regular teachers are proficient for remote tutoring. This research attempts to reveal the state-of-art of this educative model in a provincial university. The study focused on a quantitative-qualitative explorative approach to determine, whether, if withdrawal is more frequent in introductory courses or not. Among the main causes for withdrawal were found: (1) the naive of learners in high education programs, (2) the inconsistency of the university proposals (academic and administrative) and; (3) the most important, the deficiencies in remote tutoring.

Developing students' competency in constructing scientific explanations is a critical aspect of science learning. However, limited research has been conducted to explore the role of Generative Artificial Intelligence (Gen AI) in fostering this competency. Moreover, the factors influencing this competency development in the Gen AI-assisted learning environment remain underexamined. This study aimed to compare students' competency in constructing scientific explanations before and after participating in phenomenon-based learning with Microsoft Copilot and to investigate the factors influencing the development of this competency. A pretest-posttest quasi-experimental design was employed with 23 eighth-grade students from an all-girls school in Thailand. The research instruments included lesson plans for phenomenon-based learning with Microsoft Copilot, a competency test for constructing scientific explanations, and a mixed-format questionnaire. The results from the Wilcoxon Signed-Ranks Test revealed a statistically significant improvement in students' competency in constructing scientific explanations after the learning intervention (Z = 4.213, p < .001). Thematic analysis identified four key factors contributing to this development: (a) the role of Microsoft Copilot in enhancing deep understanding, (b) connecting theories to real-world phenomena through learning media, (c) collaborative learning activities, and (d) enjoyable learning experiences and student engagement. These findings suggest that the integration of Gen AI technology with phenomenon-based learning can effectively enhance students’ competency in constructing scientific explanations and provide valuable insights for the development of technology-enhanced science education.