It is argued that SI construct gives the philosophy of AI a normative vocabulary that interfaces classical philosophy with current machine-learning practice.
Abstract
This essay introduces the concept of Socratic Intelligence (SI) and proposes five premises: (1) artificial and human agents learn through observation, interaction, and reflective examination of those interactions; (2) what is learned depends on metacognition and reinforcement; (3) SI comprises five interlocking dimensions: epistemic curiosity, critical self-awareness, assumption analysis, dialogical competence, and intellectual humility; (4) Generative SI, embedded in Intelligent Tutoring Systems (ITS) or Large Language Models (LLMs), can scaffold human learning; and (5) Generative SI can apply the Socratic method recursively to a system’s own computational processes as a form of internal epistemic self-regulation. We situate SI within several philosophical traditions: Plato’s elenchus and docta ignorantia, Aristotle’s phronesis, virtue epistemology on intellectual humility, Dreyfus’s critique of cognitivism, and recent epistemology of LLMs. We argue that SI construct gives the philosophy of AI a normative vocabulary that interfaces classical philosophy with current machine-learning practice.
The concept of “alienated intelligence” is introduced to critically examine contemporary cultural perceptions of artificial intelligence to argue that certain visions of AI function as cultural projections of human cognitive capacities that become externalized, idealized, and reified in technological systems.
This study argues that AI embodies a calculative mode of thought that reduces knowledge to data, patterns, and predictive models, treating both the world and human subjects as resources for optimization, and proposes Heideggerian meditative thinking (Gelassenheit) as a framework for preserving human insight, reflection...
Generative artificial intelligence has entered science classrooms faster than the epistemological questions it raises have been addressed. This study is an attempt to reveal how the knowledge-producing character of text-based conversational systems diverges from institutional science, and what that divergence implies f...
Z. Muminova, Dilmurod Mamadaliyevich Mamatkulov, Rano Ibrogimovna Turokulova et al.· Eurasia Journal of Mathemati...· 0 citations
It is argued that generative AI does not make human beings smarter as such; its epistemic significance depends on how human–AI interaction is structured and normatively governed.
Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) have
revolutionised the interaction between humans, knowledge and technology. What
once needed years of formal education, years of institutional access or specialist's
help is now obtained, translated, explained, reorganised in seconds. This
un...
Srija Chakraborty· International Journal of Mod...· 0 citations
Abstract Artificial Intelligence (AI) continues to seep into all facets of life, bringing with it excitement and uncertainty as it forces humanity to grapple with existential questions about our uniqueness and purpose. These questions are often grounded in what AI can do and its potential for replacing the very humans...
L. Stephens· Leisure Sciences· 0 citations
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