<![CDATA[The development of Artificial Intelligence (AI) has brought significant transformations to higher education, particularly in enhancing the efficiency and effectiveness of learning processes. However, the optimal utilization of AI is influenced by several factors, including user experience, the relevance of AI-generated content, and students’ academic attitudes toward the technology. This study aims to examine the effects of AI usage experience, AI content relevance, and students’ academic attitudes on AI-based learning productivity, both partially and simultaneously. A quantitative associative approach was employed using multiple linear regression analysis. Data were collected through an online questionnaire distributed to 75 active students of the Institut Bisnis dan Komunikasi Swadaya (SWINS) Jakarta, selected through purposive sampling. The results indicate that AI usage experience and students’ academic attitudes have a positive and significant effect on AI-based learning productivity, while AI content relevance does not show a significant partial effect. Nevertheless, the three independent variables simultaneously have a positive and significant influence on learning productivity, with an Adjusted R² value of 0.689. These findings support the Technology Acceptance Model (TAM) and Self-Regulated Learning Theory, emphasizing the importance of experience and academic attitudes in optimizing technology-based learning. This study highlights the need to strengthen digital literacy, provide continuous AI utilization training, and foster positive academic attitudes to ensure that AI integration in higher education is effective, ethical, and sustainable.]]>
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