Advanced generative systems are reshaping education in ways that are only beginning to surface, often triggering psychological and epistemic tensions that hinder effective learning. Because existing models of Student Feedback Literacy (SFL) largely overlook these pressures, a systematic investigation is necessary. This study synthesizes 28 empirical studies to explore three central disruptions, namely the Learner-User Paradox, AI guilt, and the burden of critical labor. Findings drawn from the Carless-Boud SFL framework and Self-Determination Theory reveal how the Learner-User Paradox forces students into a difficult choice between deep, authentic learning and the external pressure for efficiency. Coupled with this conflict, AI guilt serves as a type of moral-affective governance, which manifests as a form of “self-governance,” whereby students monitor and constrain their own AI usage based on perceived ethical boundaries and academic norms, even in the absence of external policing. Scholarly efforts to address these shifts involve evolving the four pillars of the original SFL framework into specific AI-era competencies. These include appreciating multi-source feedback, exercising critical judgment over AI, managing the emotional toll of technology, and taking ethical action. Rather than attempting to eliminate these tensions, the expanded framework provides students with the literacy needed to navigate them purposefully. Ultimately, such an approach offers practical implications for feedback design, teacher training, and institutional policy.