The integration of artificial intelligence (AI) in educational settings has marked a shift in how students engage with classroom content, particularly in their notetaking practices. This study introduces the Generative Artificial Intelligence Note-taking (GAIN) framework, a new approach designed to balance the efficiency of AI tools with the cognitive benefits of traditional note-taking methods. Our research examines the complex relationship between technological innovation and cognitive processing in the context of academic notetaking, addressing a growing concern that while AI tools enhance efficiency, they may compromise the learning process. Through comprehensive analysis of established note-taking practices and emerging AI technologies, we evaluate the impact of these tools across four learning phases: pre-class preparation, during-class note-taking, post-class review, and long-term retention. The methodology analyzes various AI platforms, including real-time transcription services, interactive study tools, and AI-powered note enhancement applications, examining the effectiveness in each learning phase and the impact on student engagement and information retention. Our investigation shows that while AI tools offer significant advantages in capturing and organizing information, they may reduce the "desirable difficulty" essential for deep learning and retention. This reduction in cognitive engagement poses a challenge to effective learning outcomes, as traditional handwritten notes have been shown to promote deeper processing through active synthesis and paraphrasing. The GAIN framework addresses these challenges by proposing structured guidelines for integrating AI tools while maintaining active cognitive engagement. It emphasizes the importance of metacognitive practices and provides strategies for each learning phase, ensuring that technology enhances rather than replaces critical thinking processes. Our findings suggest optimal learning outcomes are achieved when AI tools supplement rather than replace traditional note-taking methods, with specific recommendations for implementation across different educational contexts and subject areas. The framework offers practical guidance for both educators and students, detailing how to leverage various AI tools effectively while preserving the cognitive benefits inherent in manual notetaking. This includes strategies for using AI transcription during lectures while maintaining active engagement, employing AI-powered review tools to enhance post-class synthesis, and utilizing AI features for long-term retention without compromising deep learning. Additionally, our research explores the potential of AI tools to support diverse learning styles and accommodate different educational needs, while maintaining the critical balance between technological assistance and cognitive development. This study contributes significantly to the growing discourse on educational technology by providing a structured approach to incorporating AI tools in academic settings. The GAIN framework represents a practical solution to the challenge of modernizing educational practices while preserving the fundamental cognitive processes essential for effective learning. Our findings have important implications for curriculum design, teaching methodologies, and the development of future educational technologies, suggesting a path forward that embraces innovation while maintaining the integrity of the learning process.

Keywords: Generative artificial intelligence note-taking, AI-enhanced metacognitive practices, Multimodal learning with artificial Intelligence.