This study delves into the intricate challenges surrounding incorporating information and communication technologies (ICTs) in education, particularly in regions characterized by digital exclusion, such as rural areas. By recognizing the potential of ICTs to support and expand student learning opportunities, this research explores the necessity for effective implementation through tailored professional development aligned with teachers' contextual conditions and needs. In addressing these challenges, the study explores the concept of technological appropriation, underscoring the significance of individual and contextual elements in adapting ICTs to users' specific needs and environments. Through unstructured face-to-face interviews with 21 teachers immersed in a digitally vulnerable rural context, this research unveils insights into how educators’ appropriate technologies to enhance student learning. The findings of this study contribute to the development of educational practices tailored to rural contexts, focusing on providing a meaningful and effective learning experience for students. By shedding light on teachers' strategies for navigating the digital landscape in challenging environments, the research aims to inform policies and practices that bridge the technological gap, ultimately fostering equitable access and enhanced educational outcomes in rural settings.

Background/Objectives: Mindfulness has emerged as a widely studied approach for promoting psychological well-being, evolving from its contemplative origins into a secular, evidence-based intervention. In recent years, the proliferation of mobile applications has enabled the delivery of mindfulness-based interventions (MBIs) in accessible and scalable formats. This systematic review examined the efficacy of MBIs delivered via mobile applications in non-clinical adult populations, with a focus on well-being, compassion, and aggression. Methods: A comprehensive search was conducted across six databases (Web of Science, PubMed, MEDLINE, Scopus, SciELO, and Dialnet) for studies published from 2014 onward, following PRISMA 2020 guidelines. A total of 23 randomized controlled trials met the inclusion criteria. Additionally, a risk-of-bias assessment was performed using the Cochrane RoB-2 tool. Results: Results indicated small to moderate improvements in well-being outcomes, including positive affect, life satisfaction, and psychological well-being. Evidence regarding compassion was limited and mixed, while no studies addressing aggression met the inclusion criteria. Additionally, substantial variability in adherence rates and a high overall risk of bias were observed. Conclusions: These findings suggest that mindfulness applications may represent accessible tools for enhancing individual well-being; however, their effect on broader socio-emotional functioning remains unclear. Further research should prioritize more rigorous study designs, including active control conditions and behavioral outcome measures, to better establish their effectiveness and underlying mechanisms.

Background: Digital mental health interventions (DMHIs) can help close persistent gaps in access to assessment, prevention, and treatment. Recent advances in generative AI, particularly large language models (LLMs), further expand this promise by enabling natural language understanding, personalization, and empathic interaction across assessment, support, and therapeutic contexts. However, significant evaluation challenges persist, including a lack of standardized constructs and validated instruments, which limit the comparability, reproducibility, and generalizability of the findings. No systematic synthesis currently documents the frameworks, methodologies, and tools used to evaluate LLMs in DMHIs, thereby hampering the development of a comprehensive evidence base to guide future evaluation efforts. Objective: This scoping review aims to systematically map and synthesize the available evidence on frameworks, methodologies, and tools used to evaluate LLMs applied to DMHIs. Specifically, it aims to identify the constructs assessed, the instruments used, and the evaluation procedures and stages addressed. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this scoping review will search 5 electronic databases (PubMed, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library) from January 1, 2019, to September 15, 2025. Eligibility criteria will encompass both empirical studies and theoretical proposals evaluating LLMs embedded within DMHIs. Studies limited to risk detection or decision support systems without an intervention component will be excluded. Data extraction will capture information on conceptual frameworks, methodological designs, evaluation procedures and tools, measured constructs, and other relevant contextual information. Results: The systematic search was conducted between September 1 and 15, 2025, yielding 4273 records across the 5 databases. After duplicate removal, of the 4273 records, 2980 (69.7%) remained for screening. A pilot screening exercise involving 4 independent reviewers achieved high interrater reliability (free-marginal Randolph κ=0.81), with 76% (19/25 of the pilot sample) unanimous agreement, indicating adequate calibration of selection criteria. These figures are interim process indicators rather than final review findings as title and abstract screening of the remaining records is currently underway. Conclusions: This scoping review is expected to provide one of the first systematic syntheses of frameworks, methodologies, and tools used to evaluate LLMs in DMHIs. By identifying prevailing patterns and gaps, the resulting evidence map is intended to serve as a practical reference for researchers, developers, and policymakers working toward a scientifically grounded, safe, ethical, and effective deployment of LLMs in mental health interventions.