Artificial Intelligence and Psychology: a study of scientific production in Scopus
Carlos Rafael Araujo Inastrilla, Mayelín Llosa Santana, Dalila Cárdenas Hernández, Dayami Gutiérrez Vera
Abstract
Introduction: Artificial intelligence (AI) has become an increasingly used tool in the practice of psychology. It can help psychologists analyze large amounts of data and identify patterns that may be difficult for humans to detect. In addition, it has been used to develop predictive models that can help psychologists predict human behavior and make informed decisions. Objective: To describe the scientific production about AI in the practice of psychology from publications indexed in Scopus between 2003 and 2023. Methods: A bibliometric study of the scientific production indexed in Scopus on AI in psychology was conducted. This study covered a population of 562 articles published between 2003 and 2023. An analysis of scientific productivity indicators was carried out: annual publications, Lotka distribution, productivity index, country and publication mode. Results: The results of the study reveal an exponential growth in scientific production on AI in psychology from 2003 to 2023, with some periodic fluctuations. Four stages of literature growth were identified: precursors, exponential growth, linear growth and collapse of the scientific field for the volume of publications analyzed. The Lotka distribution indicates that there are many authors who publish few articles and few authors who publish many articles, with a Lotka productivity index of 6.76, indicating a moderate concentration of author productivity and greater author participation. As for the country of origin of scientific production, the leading countries in this field are the United States, United Kingdom, China, Germany and Italy. Conclusions: The scientific production of AI in psychology was described, and the findings provide an up-to-date and detailed view of the intersection between AI and psychology, and lay the groundwork for future research and applications in this field. Monitoring scientific productivity in this area allows fostering the development of disruptive technologies for public good