The Vibe-Automation of Automation: A Proactive Education Framework for Computer Science in the Age of Generative AI

• URL: http://arxiv.org/abs/2602.08295v1
• Date: Mon, 09 Feb 2026 06:02:04 GMT
• Title: The Vibe-Automation of Automation: A Proactive Education Framework for Computer Science in the Age of Generative AI
• Authors: Ilya Levin,
• Abstract summary: generative artificial intelligence (GenAI) represents a qualitative shift in computer science.<n>GenAI operates by navigating contextual, semantic, and coherence rather than optimizing predefined objective metrics.<n>The paper proposes a conceptual framework structured across three analytical levels and three domains of action.
• Score: 0.7252027234425333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of generative artificial intelligence (GenAI) represents not an incremental technological advance but a qualitative epistemological shift that challenges foundational assumptions of computer science. Whereas machine learning has been described as the automation of automation, generative AI operates by navigating contextual, semantic, and stylistic coherence rather than optimizing predefined objective metrics. This paper introduces the concept of Vibe-Automation to characterize this transition. The central claim is that the significance of GenAI lies in its functional access to operationalized tacit regularities: context-sensitive patterns embedded in practice that cannot be fully specified through explicit algorithmic rules. Although generative systems do not possess tacit knowledge in a phenomenological sense, they operationalize sensitivities to tone, intent, and situated judgment encoded in high-dimensional latent representations. On this basis, the human role shifts from algorithmic problem specification toward Vibe-Engineering, understood as the orchestration of alignment and contextual judgment in generative systems. The paper connects this epistemological shift to educational and institutional transformation by proposing a conceptual framework structured across three analytical levels and three domains of action: faculty worldview, industry relations, and curriculum design. The risks of mode collapse and cultural homogenization are briefly discussed, emphasizing the need for deliberate engagement with generative systems to avoid regression toward synthetic uniformity.

Related papers

• Epistemology of Generative AI: The Geometry of Knowing [0.7252027234425333]
  Generative AI presents an unprecedented challenge to our understanding of knowledge and its production.<n>This paper argues that the missing account must begin with a paradigmatic break that has not yet received adequate philosophical attention.
  arXiv  Detail & Related papers  (2026-02-19T06:34:34Z)
• The Post-Turing Condition: Conceptualising Artificial Subjectivity and Synthetic Sociality [0.23332469289621782]
  In the Post-TurTuring era, artificial intelligence increasingly shapes social coordination and meaning formation.<n>The central challenge is whether processes of interpretation and shared reference are automated in ways that marginalize human participation.<n>This paper proposes Quadrangulation as a design principle for socially embedded AI systems.
  arXiv  Detail & Related papers  (2026-01-19T10:46:52Z)
• Systems Explaining Systems: A Framework for Intelligence and Consciousness [0.0]
  This paper proposes a conceptual framework in which intelligence and consciousness emerge from relational structure rather than from prediction or domain-specific mechanisms.<n>We introduce the systems-explaining-systems principle, where consciousness emerges when higher-order systems learn and interpret the relational patterns of lower-order systems across time.<n>The framework reframes predictive processing as an emergent consequence of contextual interpretation rather than explicit forecasting.
  arXiv  Detail & Related papers  (2026-01-07T11:19:22Z)
• Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions [10.453339156813852]
  Agentic AI represents a transformative shift in artificial intelligence.<n>Its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models.<n>This survey introduces a novel dual-paradigm framework that categorizes agentic systems into two distinct lineages.
  arXiv  Detail & Related papers  (2025-10-29T12:11:34Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Synthetic media and computational capitalism: towards a critical theory of artificial intelligence [0.0]
  I argue that we need new critical methods capable of addressing both the technical specificity of AI systems and their role in restructuring forms of life under computational capitalism.<n>The paper concludes by suggesting that critical reflexivity is needed to engage with the algorithmic condition without being subsumed by it.
  arXiv  Detail & Related papers  (2025-03-22T22:59:28Z)
• Mechanistic Interpretability for AI Safety -- A Review [28.427951836334188]
  This review explores mechanistic interpretability. Mechanistic interpretability could help prevent catastrophic outcomes as AI systems become more powerful and inscrutable.
  arXiv  Detail & Related papers  (2024-04-22T11:01:51Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.