Subjective well-being, collaborative learning and artificial intelligence in the innovation of doctoral research proposals

García Tarazona J.O.; López Malqui S.M.; Mellado Baca I.; Mañaccasa Vásquez M.S. · Revista de Ciencias Sociales (2025) · DOI: 10.31876/rcs.v31i3.44295

Resumen

The factors that influence doctoral students’ ability to design innovative research proposals are a challenge for postgraduate programs, which must meet the demands of their areas of expertise and university social responsibility. Within this framework, this article aimed to establish the level of impact of subjective well-being, collaborative learning, and generative artificial intelligence on the innovation of research proposals among doctoral students at a public university in Lima, Peru. Methodologically, the approach was quantitative, with a non-experimental ex post facto design, explanatory in nature, and applying the hypothetical-deductive method. Among the main findings, the linear regression design demonstrated that the model that explains the capacity for research innovation is collaborative work, followed by artificial intelligence and subjective well-being, with a compensation coefficient (R) of 0.85. The coefficient of determination (R-squared) indicates that the model is capable of explaining 75% of the variation in research innovation. It was concluded that doctoral research is a complex and interdisciplinary process, which must connect with subjective well-being, thus enhancing the innovative capabilities of its participants. © Licencia de Creative Commons Atribución 4.0 Internacional (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.es