Beyond self-directed learning: An ai-augmented conceptual framework for future learning in higher education

The rapid advancement of artificial intelligence (AI) is transforming higher education by reshaping how students acquire knowledge, solve problems, collaborate, and regulate their own learning. Generative Al technologies, including ChatGPT, Gemini, Microsoft Copilot, and other intelligent learning systems, have created unprecedented opportunities for personalized learning while simultaneously challenging traditional conceptions of self-directed learning (SDL). Although SDL has long been recognized as a fundamental competency for lifelong learning, existing theoretical models were developed before the emergence of Al-enhanced learning environments and therefore provide limited guidance for understanding learner autonomy within human- Al collaborative contexts. This conceptual paper reconceptualizes self-directed learning in the era of artificial intelligence by proposing an AI-Augmented Self-Directed Learning (AI-ASDL) Framework for future learning in higher education. Employing documentary research and conceptual synthesis, the paper integrates classical theories of self-directed learning, self-regulated learning, self-determination theory, and contemporary scholarship on artificial intelligence in education. The conceptual synthesis suggests that future learners require competencies extending beyond independent learning, including Al literacy, critical evaluation of Al-generated information, ethical Al use, reflective learning, and human-Al collaborative problem solving. Accordingly, the proposed framework comprises eight interconnected dimensions: learning motivation, self-directed goal setting, Al-assisted information seeking, human-Al interaction, critical evaluation, reflective learning, AI-augmented self-regulation, and lifelong learning orientation. The framework reconceptualizes self-directed learning as a collaborative process in which learners strategically engage with artificial intelligence while maintaining human agency, critical judgment, ethical responsibility, and continuous self-improvement. By extending traditional self-directed learning theory to Al-enhanced educational environments, the proposed framework provides a theoretical foundation for future research and offers practical implications for curriculum development, instructional design, assessment, and educational policy in higher education.