Experiential Intelligence: A Framework for Human Formation in AI-Mediated Learning Environments

Generative artificial intelligence can now produce credible work in nearly every professional domain, yet it has never had an experience. This article introduces experiential intelligence as the integrated, embodied, accountable human capacity that grows from repeated encounter with consequential situations, and argues that this capacity is precisely what current AI-mediated learning environments are most at risk of eroding. Drawing on the experiential learning tradition (Dewey, 1938; Kolb, 1984), the Dreyfus model of skill acquisition (Dreyfus Benner, 1984), Polanyi’s account of tacit knowing (Polanyi, 1966), and emerging empirical evidence on automation bias in clinical decision support (Gaube et al., 2021; Küücking et al., 2024; Nguyen, 2024), cognitive offloading and critical thinking (Gerlich, 2025; Lee et al., 2025), and the impact of generative AI on skill acquisition (Bastani et al., 2025; Fan et al., 2025), the article distinguishes experience from information, exposure from learning, and AI output from human formation. Four central claims are advanced: (1) experiential intelligence is real and trainable; (2) it is the human contribution AI cannot replicate; (3) automation bias is its central threat; and (4) learning environment design is the decisive variable in whether learners develop experiential intelligence or merely a sophisticated facility with AI outputs. Implications are drawn for medical education, faculty development, theological education, and professional formation more broadly.