University students may experience both general perceived stress and stress arising specifically from academic demands. Whether these two forms of stress show independent associations with academic performance is less clear when they are considered in the same model. Objective: This study investigated perceived stress, academic stress, and academic performance among university students in Tirana, Albania, with emphasis on the independent contribution of each stress construct after accounting for demographic and institutional characteristics.Between June and July 2026, a cross-sectional survey was completed by 264 students aged 18–25 years enrolled in public and non-public universities. The survey included the PSS-10, a study-specific 10-item academic stress measure, and self-reported GPA categories.Perceived stress and academic stress were moderately associated (r=.579, p<.001), and academic stress, but not perceived stress, was higher among students at public universities in unadjusted comparisons. Curvilinear patterns appeared in separate stress–GPA analyses, but they were not reproduced when the two stress measures were modeled together. In the adjusted joint model, neither perceived stress (p = .195) nor academic stress (p = .864) showed a statistically significant independent association with GPA, whereas socioeconomic status was significantly associated with GPA (p = .002).The findings distinguish descriptive and exploratory stress–performance patterns from the adjusted associations observed in the joint model. In this sample, socioeconomic circumstances were more consistently related to GPA than either stress measure, indicating that student performance should be considered within a broader social context. Received: 01 September 2026 / Accepted: 28 October 2026 / Published: November 2026
The rapid development of digital technologies in the current educational system is opening new opportunities for the modernization of the learning process. In particular, teaching geometric constructions often presents challenges for students, especially in accurately constructing, measuring, and comparing line segments and understanding the relationships between their lengths. In this regard, this study aims to investigate the effectiveness of digital technologies in teaching line segment construction during geometry lessons and to evaluate their impact on students’ understanding of geometric relationships and learning outcomes. The study employed a systematic literature review and comparative analysis methods. A total of 27 scientific works were analyzed, and GeoGebra software was utilized as the primary digital tool. During the experimental phase, the academic performance of control and experimental groups was compared, and a significant difference was revealed through a t-test (t(78) = 13.02, p < 0.0001). The results revealed significantly higher academic achievement among students who received GeoGebra-supported instruction compared with those taught using traditional methods. Furthermore, the interactive features of GeoGebra may offer additional pedagogical benefits, including support for geometric visualization, student engagement, and independent exploration of mathematical concepts. Moreover, the findings highlighted the crucial role of teachers’ digital competence and methodological preparedness. The novelty of this research lies in examining the educational potential of digital technologies for teaching line segment construction and evaluating their effectiveness through a systematic literature review and a comparative quasi-experimental study. These results contribute to the improvement of methodological foundations for teaching geometry by promoting the effective integration of digital technologies in the classroom. Received: 06 August 2026 / Accepted: 24 October 2026 / Published: November 2026
This paper explores the potential of artificial intelligence (AI) to enhance Project-Based Learning (PBL) and evaluates the effectiveness of an AI-powered PBL model for pre-service primary school teachers. Integrating AI into PBL represents a promising direction in teacher education by combining the hands-on, inquiry-based nature of project-based learning with the opportunities created by the digital transformation of education. AI enhances project-based instruction by making the learning process more efficient, personalised, and engaging. To evaluate the model’s effectiveness, the authors conducted a pedagogical experiment involving knowledge tests and project presentations to assess the knowledge and project-related skills of pre-service teachers in the experimental and control groups before and after intervention. The findings indicate that the AI-supported PBL model can significantly improved the project competence of pre-service teachers in the experimental group. The authors conclude that the proposed model is as an effective tool for preparing future primary school teachers, fostering their critical thinking, collaboration, and ability to integrate AI into classroom practice. Received: 16 April 2026 / Accepted: 16 October 2026 / Published: November 2026
Life satisfaction is relevant to educational research because it has been associated with students’ engagement, academic self-efficacy, stress, and broader educational functioning. However, the use of the Satisfaction With Life Scale (SWLS) in schools and universities requires evidence that its scores are reliable and comparable across developmental, cultural, and educational groups. This structured narrative review synthesized evidence on the internal structure, reliability, measurement invariance, and educational interpretability of the SWLS, primarily in adolescent and university samples, alongside the limited available evidence from teachers. Searches were conducted in Scopus and PubMed, using combinations of SWLS, psychometric, student, school, university, teacher, reliability, validity, and measurement invariance. After screening, 13 studies met the eligibility criteria. Evidence from adolescent and university samples generally supported a one-factor structure and adequate internal consistency, although alternative factorial representations and incomplete scalar invariance across countries or languages indicate that measurement equivalence should not be assumed. Teacher-specific psychometric evidence was limited to one South African study involving 355 primary and secondary schoolteachers, which supported reliability and unidimensionality but did not establish generalizability across teacher populations. The SWLS may contribute to research and low-stakes monitoring of global life satisfaction, provided that its measurement properties are supported in the target population. It should not be interpreted as a diagnostic instrument or as a direct measure of school satisfaction, teaching quality, or academic performance. Received: 10 August 2026 / Accepted: 22 October 2026 / Published: November 2026
AI-based proctoring has become a contested instrument for safeguarding examination integrity in higher education, yet it processes extensive personal data, including video, audio and potentially biometric data. No publicly documented deployment of AI-based proctoring in Albanian universities was identified, although digitalisation and European Union accession make future adoption plausible. This article therefore asks whether the Albanian data protection framework is ready for that prospect. It applies doctrinal analysis to the Albanian-language text of Law No. 124/2024 “On Personal Data Protection”, the Constitution and the higher education legislation, benchmarks them against the General Data Protection Regulation and the EU Artificial Intelligence Act, and draws on decisions from Italy, the Netherlands and the United States as evidence of foreseeable legal risks. Hypothetical deployments are assessed in terms of legal risk, with attention to legal basis, the validity of student consent, special-category data, automated decision-making, impact assessment and proportionality. The analysis finds that Law 124/2024 would make the most intrusive designs, such as automated facial matching, home scans and solely automated sanctions, difficult to justify. It also finds that the legal basis for monitoring in public universities remains uncertain, that no dedicated AI legislation is in force, and that supervisory practice is untested. The article concludes with conditions under which less intrusive designs could potentially comply. Received: 01 September 2026 / Accepted: 26 October 2026 / Published: November 2026
