Cognitive Debt and Neuroplasticity in the Age of Articial Intelligence: Neural Consequences of Large Language Model Dependency, Risk Factors for Cognitive Decline, and Strategies for Brain-Healthy HumanAI Integration

The rapid and largely unregulated proliferation of large language model (LLM)-based artificial intelligence tools—most prominently conversational agents such as ChatGPT, Gemini, Grok, and Claude—presents a set of neuroscientific and public-health challenges that have not yet received proportionate scholarly attention. A landmark 2025 preprint from the MIT Media Lab (Kosmyna et al., arXiv:2506.08872) demonstrated, via electroencephalographic (EEG) measurement of dynamic Directed Transfer Function (dDTF) connectivity, that participants who composed essays with LLM assistance exhibited the weakest neural connectivity profiles of three experimental groups, while unassisted writers displayed the broadest and most richly interconnected neural networks. The phenomenon of cognitive debt—the progressive reduction in self-generated cognitive engagement resulting from habitual AI offloading—is here contextualized within a wider framework integrating established neuroscience of memory consolidation, long-term potentiation, the “use-it-or-lose-it” principle of synaptic maintenance, dementia risk stratification, pharmacological effects of selective serotonin reuptake inhibitors (SSRIs) on cognition, adolescent neurodevelopment, and AI-mediated parasocial bonding. Drawing on expert clinical and computational neuroscience perspectives, we synthesize the evidence base, delineate critical risk populations, and propose an evidence-grounded framework of twelve practical recommendations for individuals, educators, and policy-makers aimed at preserving cognitive capital while harnessing the genuine productivity benefits of AI tools. We argue that the current moment constitutes a Sputnik-level inflection point for neuroscience policy: the failure to study, regulate, and educate populations about AI’s cognitive trade-offs before mass adoption repeats the errors made with social media, opioids, and ultra-processed foods—all of which were embraced before their neurobiological costs were fully understood.