This systematic review examined, through the UTAUT2 model, the factors influencing the acceptance of artificial intelligence (AI) applications in university contexts. A total of 50 scientific texts published between 2018 and 2023 were analyzed and selected after a rigorous search of specialized databases. These findings confirm the versatility of UTAUT2 in elucidating technological adoption processes in higher education. Performance expectancy and hedonic motivation emerged as significant predictors of intentions and effective use among students, faculty, and administrative staff. Among students, perceived ease of use and social influence were also relevant. The analysis revealed differences in adoption patterns between STEM and non-STEM disciplines and between public and private institutions. Despite widespread positive perceptions of AI's potential, barriers such as distrust and lack of knowledge persist. The research also identified moderating and mediating factors, such as prior technology experience and technological self-efficacy. These results have important implications for the implementation of AI in higher education, suggesting the need for differentiated approaches according to the characteristics of each group and institutional context. It is recommended to develop strategies that address the identified barriers and leverage facilitators, with an emphasis on training, ethical design, and contextual adaptation of AI applications. Future research should explore the longitudinal evolution of these factors and examine AI adoption in non-STEM disciplines in greater depth.

The present study examines the impact of academic stress on the psychological well-being of college students during and after the COVID-19 pandemic. The purpose of this study is to investigate similarities and differences in both scenarios. As a method, a review of the scientific literature was carried out following the PRISMA methodology. The Scopus and Web of Science databases were used by applying eligibility criteria and multiple filtering stages. Thirty-seven studies were selected for a qualitative content analysis. The results allowed for five groups of variables associated with academic stress to be categorized: (1) adaptation to change; (2) study modality; (3) learning resources; (4) academic–life balance; and (5) socio-emotional variables. The comparative analysis evidenced the exacerbation of academic stress and the comprehensive affectation of psychological well-being during the social restriction measures put in place due to the COVID-19 pandemic. In post-pandemic contexts, responses were mitigated by available social and affective resources. It was concluded that academic stress and its associated variables had more unfavorable consequences on the psychological well-being of college students due to social isolation measures with remote education during the pandemic.

Background/Objectives: Loneliness is the sensation of feeling alone or emotionally isolated, even when one is surrounded by other people. It is associated with conditions such as depression, anxiety, harmful habits, and cardiovascular problems. The main objective of the present study was to develop and determine the psychometric properties of the Multidimensional Loneliness Scale (MLS), which is a self-report instrument. Methods: The present study is instrumental in nature, as it aims to analyze the psychometric properties of a new assessment instrument. A total of 484 adults, both men and women, aged between 18 and 55 years, participated in this research. Results: Exploratory factor analysis revealed the presence of four dimensions: social disconnection, family estrangement, loss of attachment figure, and intrapersonal emptiness. Confirmatory factor analysis demonstrated that the four-dimensional model exhibited a good fit (CFI = 0.91; RMSEA = 0.07; SRMR = 0.07; AIC = 737.87). The concurrent validity was evidenced by significant correlations with the De Jong Gierveld scale and the Revised UCLA Loneliness Scale. The reliability analysis demonstrated satisfactory internal consistency, with omega coefficients ranging from 0.84 to 0.92 and alpha coefficients from 0.84 to 0.93. Conclusions: The MLS is a self-report instrument designed to assess loneliness, and it has satisfactory psychometric properties.