Abstract: This interdisciplinary study challenges the prevailing techno-optimist assumptions about artificial intelligence by reframing the concept of “unintelligence” not as a lack or failure, but as a generative state of epistemic openness. Through comparative philosophical analysis, media criticism, and cultural-historical case studies, we argue that both human and machine intelligences are currently trapped in a model of knowledge accumulation that privileges information over insight, and computational rationality over holistic reflection. This prevailing model is deeply rooted in Enlightenment-era assumptions and the rationalist-technocratic paradigms of modernity, which emphasize intelligence as a measurable, quantifiable, and extractive function. In contrast, this paper turns to ancient East Asian epistemologies—from Confucian notions of ‘知’ and ‘智’ to Daoist reflections on ‘無’ and ‘虛’—to suggest alternative ways of conceptualizing intelligence that emphasize relationality, humility, stillness, and the transformative value of not-knowing. Drawing on these traditions, as well as modern critiques of algorithmic cognition, we propose that humane innovation, ethical discernment, and mental well-being in the AI age may depend less on quantitatively scaled-up knowing, and more on reclaiming spaces of intentional unknowing, generative pause, and epistemological humility.
Intelligence therefore, we argue, must be understood as inherently diverse and culturally situated, and that cultivating intercultural sensitivity is essential in building more inclusive and ethical AI systems. To this end, we offer a framework for “critical unintelligence” that challenges monolithic constructions of intelligence and explores how ignorance—when cultivated with reflexivity—can become a strategy for resisting algorithmic saturation, reactivating creative thought, and restoring contemplative agency in human-machine interaction.