Feedforward, Video Self-Modeling, and Avatar-Based Observational Learning: A Critical Review of the Evidence Base for Motor Skill and Psychological Outcomes

Background/Objectives: Feedforward—viewing a self-representation performing at a level not yet achieved—is increasingly delivered through virtual reality, avatars and generative artificial intelligence. This critical review synthesizes the evidence base for feedforward, video self-modeling and avatar-based observational learning on motor skill performance, self-efficacy and state anxiety, and appraises the methodological quality of that evidence. Methods: Source identification followed snowballing guidelines across four streams: a start set, a targeted supplementary search (six databases, one academic search engine, two conference series; 1999–2026), and backward and forward citation chaining. Of 24 core sources, 18 were primary empirical studies; the 17 testing an intervention effect were coded independently by two raters across five methodological quality dimensions, without summing them. Results: In only four of the 18 primary studies does the observed movement belong to the participant; three use a hybrid stimulus combining another person’s movement with the participant’s identity, and one tests a generative AI implementation. Sixteen of the 17 appraised studies fell below the sample required to detect a medium effect (d = 0.50) at α = 0.05 with power = 0.80, and no between-subjects study reached it. Assessor blinding was reported in two studies, and a delayed retention or transfer test in seven. A between-condition difference in self-efficacy could not be demonstrated in six of the seven studies measuring it, none of which reported an equivalence test. Conclusions: The evidence does not support practice recommendations, least of all for the AI-supported variant, which rests on one conference paper. The weakness lies in statistical power, assessor blinding and retention testing rather than in an overall quality ranking.