Exploring the potential of artificial intelligence in individualized cognitive training: A systematic review

Abstract

To tackle the challenge of responders heterogeneity, Cognitive Training (CT) research currently leverages AI Techniques for providing individualized curriculum rather than one-size-fits-all designs of curriculum. Our systematic review explored these new generations of adaptive methods in computerized CT and analyzed their outcomes in terms of learning mechanics (intra-training performance) and effectiveness (near, far and everyday life transfer effects of CT). A search up to June 2023 with multiple databases selected 19 computerized CT studies using AI techniques for individualized training. After outlining the AI-based individualization approach, this work analyzed CT setting (content, dose, etc.), targeted population, intra-training performance tracking, and pre-post-CT effects. Half of selected studies employed a macro-adaptive approach mostly for multiple-cognitive domain training while the other half used a micro-adaptive approach with various techniques, especially for single-cognitive domain training. Two studies emphasized the favorable influence on CT effectiveness, while five underscored its capacity to enhance the training experience by boosting motivation, engagement, and offering diverse learning pathways. Methodological differences across studies and weaknesses in their design (no control group, small sample, etc.) were observed. Despite promising results in this new research avenue, more research is needed to fully understand and empirically support individualized techniques in cognitive training.

Fig 1. PRISMA Flow chart.

Fig 2. Illustration of macro and micro-adaptive strategies.

(a): Macro-adaptive strategy exemplified by two trajectories within a CT program (unique trajectory or individualized design) spanning sessions 1 to 4, each session offering three potential cognitive tasks (A1 to A3). Arrows depict task order for each session. Non individualized trajectory always propose same order A1, A2 and A3 while individualized path adapts the trajectory according to training objectives. (b): Micro-adaptive strategy demonstrated by two trajectories of task difficulty adjustments specifically for task A3 during session 2. The non-individualized trajectory relies on a staircase approach that falls short of identifying the optimal zone of progress when contrasted with the individualized procedure, which proves to be a more suitable fit.

Fig 3. Distribution of AI techniques depending on type of CT studied (multi or single domain).

Fig 4. Comparison of studies across single and multi-domain interventions in Cognitive Performance, Training Trajectories, and Subjective Experience.

Studies are classified by adaptive strategies (macro-adaptive in gray, micro-adaptive in blue) and rated for methodological quality using the SIGN scale (+/- symbols). “+” indicates higher methodological quality, while “-” indicates lower quality.