Beyond Tools: Generative AI as Epistemic Infrastructure in Education

• URL: http://arxiv.org/abs/2504.06928v1
• Date: Wed, 09 Apr 2025 14:35:30 GMT
• Title: Beyond Tools: Generative AI as Epistemic Infrastructure in Education
• Authors: Bodong Chen,
• Abstract summary: generative AI rapidly integrates into educational infrastructures worldwide.<n>This paper investigates how AI systems function as epistemic infrastructures in education.<n>It analyzes their impact on teacher practice across three dimensions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As generative AI rapidly integrates into educational infrastructures worldwide, it transforms how knowledge gets created, validated, and shared, yet current discourse inadequately addresses its implications as epistemic infrastructure mediating teaching and learning. This paper investigates how AI systems function as epistemic infrastructures in education and their impact on human epistemic agency. Adopting a situated cognition perspective and following a value-sensitive design approach, the study conducts a technical investigation of two representative AI systems in educational settings, analyzing their impact on teacher practice across three dimensions: affordances for skilled epistemic actions, support for epistemic sensitivity, and implications for long-term habit formation. The analysis reveals that current AI systems inadequately support teachers' skilled epistemic actions, insufficiently foster epistemic sensitivity, and potentially cultivate problematic habits that prioritize efficiency over epistemic agency. To address these challenges, the paper recommends recognizing the infrastructural transformation occurring in education, developing AI environments that stimulate skilled actions while upholding epistemic norms, and involving educators in AI design processes -- recommendations aimed at fostering AI integration that aligns with core educational values and maintains human epistemic agency.

Related papers

• Strategic Integration of AI Chatbots in Physics Teacher Preparation: A TPACK-SWOT Analysis of Pedagogical, Epistemic, and Cybersecurity Dimensions [0.0]
  The study was conducted within a university-level capstone course on innovative tools for physics instruction.<n>Results highlight internal strengths such as enhanced information-seeking behavior, scaffolded pedagogical planning, and support for symbolic reasoning.<n> External opportunities were found in promoting inclusive education, multilingual engagement, and expanded zones of proximal development.
  arXiv  Detail & Related papers  (2025-07-20T08:04:07Z)
• Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment [2.3359837623080613]
  We examine how academic skills, cognitive traits, and AI scepticism influence students' ability to detect factually incorrect AI-generated responses (hallucinations) in a high-stakes assessment at a UK business school.<n>We find that only 20% successfully identified the hallucination, with strong academic performance, interpretive skills thinking, writing proficiency, and AI scepticism emerging as key predictors.
  arXiv  Detail & Related papers  (2025-05-28T18:39:57Z)
• Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation [0.0]
  This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools. We integrate cognitive assessment frameworks, linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools.
  arXiv  Detail & Related papers  (2025-05-01T06:36:21Z)
• Agentic AI Needs a Systems Theory [46.36636351388794]
  We argue that AI development is currently overly focused on individual model capabilities.<n>We outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness.<n>We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.
  arXiv  Detail & Related papers  (2025-02-28T22:51:32Z)
• Integrating Generative AI in Cybersecurity Education: Case Study Insights on Pedagogical Strategies, Critical Thinking, and Responsible AI Use [0.0]
  This study presents a structured framework for integrating GenAI tools into cybersecurity education.<n>The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks.<n>Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application.
  arXiv  Detail & Related papers  (2025-02-21T10:14:07Z)
• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Human-Centric eXplainable AI in Education [0.0]
  This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape. It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools. It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
  arXiv  Detail & Related papers  (2024-10-18T14:02:47Z)
• Generative AI and Its Impact on Personalized Intelligent Tutoring Systems [0.0]
  Generative AI enables personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways. Report explores key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems. Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education.
  arXiv  Detail & Related papers  (2024-10-14T16:01:01Z)
• To accept or not to accept? An IRT-TOE Framework to Understand Educators' Resistance to Generative AI in Higher Education [0.0]
  This study aims to develop a theoretical model to empirically predict the barriers preventing educators from adopting Generative Artificial Intelligence in their classrooms. Our approach is grounded in the Innovation Resistance Theory (IRT) framework and augmented with constructs from the Technology-Organization-Environment (TOE) framework.
  arXiv  Detail & Related papers  (2024-07-29T15:59:19Z)
• Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors [2.217351976766501]
  This study delves into university instructors' experiences and attitudes toward AI language models. We find no correlation between teaching style and attitude toward generative AI. While CS educators show far more confidence in their technical understanding of generative AI tools, they show no more confidence in their ability to detect AI-generated work.
  arXiv  Detail & Related papers  (2024-03-22T19:21:29Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• 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)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

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.