Systematic Review of AI-Based Cognitive Training Programs: Algorithms, Populations, and Stimulated Cognitive Domains
Revisión sistemática de programas de entrenamiento cognitivo basados en IA: algoritmos, poblaciones y dominios cognitivos estimulados
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entrenamiento cognitivo
rehabilitación cognitiva
ejercicios cognitivos
software
uso del computador
inteligencia artificial
algoritmos inteligentes
sistemas de recomendación
dominios cognitivos
revisión sistemática
cognitive stimulation
cognitive training
cognitive rehabilitation
brain training
computational system
artificial intelligence
intelligent algorithms
system recommendation
cognitive domains
bibliographic review
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This article presents a literature review divided into two phases. The first phase exposes the findings from the grey literature on cognitive training programs that implement artificial intelligence strategies with commercial use. The second phase shows the results of the search conducted in scientific databases, focusing on studies that describe the design and implementation of software for cognitive training using artificial intelligence algorithms. The objective has been to identify which intelligent algorithms were implemented, which functionalities or moments within the cognitive training these algorithms intervene, which populations have been studied, and which cognitive domains were stimulated. The review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic reviews and Meta-Analyzes), guided by a research question that directed both the search for commercial software in the grey literature and the search in seven scientific databases. In the grey literature, 31 commercially available cognitive training platforms that use intelligent algorithms were identified, while 291 records were extracted from scientific journals. Both commercial programs and articles were filtered according to the established inclusion criteria to obtain a final selection of four programs and nine articles used for the purposes of this study in the analysis phase. The findings showed that the most used intelligent algorithms are recommendation systems, particularly collaborative filtering ones, and they are mainly used to propose challenges during training sessions or to vary the difficulty of exercises based on the participants’ results. The target population for commercial platforms includes participants of any age, particularly middle-aged adults, while the most studied age groups in the research focus on children with learning disorders and adults aged 60 to 90 years with cognitive decline or brain injuries. In both cases, they aim to stimulate cognitive domains such as attention, memory, and executive functions to a greater extent.
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