AI-SUPPORTED LEARNING AND HUMAN CAPITAL DEVELOPMENT THROUGH STUDENT-NEGOTIATED INTELLIGENCE

Abstract

This study examines how students work with artificial intelligence (AI) in learning, moving beyond the simple view of dependence versus control. It focuses on what students actually do when using AI and how they make decisions around it. The study is based on 18 in-depth interviews selected from more than 30 student responses. These were chosen for their clarity and detail, allowing a closer look at learning practices. The findings show that students rarely accept AI-generated content as it is. Instead, they check, adjust, and sometimes rewrite it before using it. In many cases, AI serves as a starting point rather than a final answer. This shifts cognitive effort toward evaluating, selecting, and making sense of information. To explain this, the study introduces the concept of negotiated intelligence, referring to how students regulate AI use while maintaining control over meaning. The results also highlight the role of contextual intelligence, especially when adapting content to local situations or specific audiences. These practices are linked to perceived development in critical evaluation, adaptive reasoning, and contextual understanding, which may be relevant for human capital formation in digital learning environments.