Transforming Individuals with Borderline Intellectual Functioning into Cognitively Augmented Workers: AI-Integrated Co-Adaptive, Closed-Loop Brain–Computer Interface

Individuals with borderline intellectual functioning (BIF), defined by intelligence quotients (IQ) between 70 and 85, face persistent disadvantages in education, employment, and social participation. Brain–artificial intelligence interfaces (BAIs) are defined as AI–integrated, co-adaptive, closed-loop extensions of bidirectional brain–computer interfaces (BCIs) that decode neural signals and deliver context-aware feedback in real-time. Unlike open-loop BCIs, BAIs enable continuous two-way interaction between the human brain and AI, providing adaptive support for working memory, attentional control, and procedural guidance. This paper analyzes the structural barriers affecting individuals with BIF and evaluates the potential for ethically designed BAIs to enhance workforce participation through integration as cognitively augmented workers (CAWs). Economic modeling suggests substantial national benefits, including gains in gross domestic product (GDP), higher tax revenues, and reduced reliance on welfare systems. Safeguards are outlined for protecting mental autonomy, governing neural data, and ensuring equitable labor regulation. A phased implementation program is further proposed, linking engineering trials and workplace pilots to quasi-experimental evaluation and general equilibrium analysis. Taken together, these elements constitute the paper’s core contribution: a unified conceptual, economic, and governance framework for integrating individuals with BIF as CAWs through co-adaptive BAIs. Responsibly developed BAIs, grounded in co-adaptation, offer a pathway to individual empowerment and inclusive societal progress through scalable cognitive augmentation.