Cyber Humanism in Education: Reclaiming Agency through AI and Learning Sciences

• URL: http://arxiv.org/abs/2512.16701v1
• Date: Thu, 18 Dec 2025 16:06:04 GMT
• Title: Cyber Humanism in Education: Reclaiming Agency through AI and Learning Sciences
• Authors: Giovanni Adorni,
• Abstract summary: We conceptualise AI-enabled learning environments as socio-technical infrastructures co-authored by humans and machines.<n>We articulate three pillars for cyber-humanist design, emphreflexive competence, emphalgorithmic citizenship, and emphdialogic design
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Generative Artificial Intelligence (GenAI) is rapidly reshaping how knowledge is produced and validated in education. Rather than adding another digital tool, large language models reconfigure reading, writing, and coding into hybrid human-AI workflows, raising concerns about epistemic automation, cognitive offloading, and the de-professiona\-lisation of teachers. This paper proposes \emph{Cyber Humanism in Education} as a framework for reclaiming human agency in this landscape. We conceptualise AI-enabled learning environments as socio-technical infrastructures co-authored by humans and machines, and position educators and learners as epistemic agents and \emph{algorithmic citizens} who have both the right and the responsibility to shape these infrastructures. We articulate three pillars for cyber-humanist design, \emph{reflexive competence}, \emph{algorithmic citizenship}, and \emph{dialogic design}, and relate them to major international digital and AI competence frameworks. We then present higher-education case studies that operationalise these ideas through \emph{prompt-based learning} and a new \emph{Conversational AI Educator} certification within the EPICT ecosystem. The findings show how such practices can strengthen epistemic agency while surfacing tensions around workload, equity, and governance, and outline implications for the future of AI-rich, human-centred education.

Related papers

• The AI Pyramid A Conceptual Framework for Workforce Capability in the Age of AI [2.134211474877041]
  Recent evidence shows that generative AI disproportionately affects highly educated, white collar work.<n>This paper proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy.<n>The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work.
  arXiv  Detail & Related papers  (2026-01-10T09:27:56Z)
• Integrating Generative AI into LMS: Reshaping Learning and Instructional Design [1.2489632787815885]
  We propose two guiding principles for integrating generative AI into Learning Management Systems.<n>First, From Content Delivery to Fostering Higher-Order Thinking, emphasizing AI's role in supporting inquiry, collaboration, and reflective knowledge building.<n>Second, Toward Meaningful Interaction with AI, highlighting the design of learning environments that nurture critical, intentional, and socially mediated engagement with AI.
  arXiv  Detail & Related papers  (2025-10-20T18:58:47Z)
• From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning [0.0]
  We present a novel conceptual framework that charts the transition from AI as a tool to AI as a collaborative partner.<n>We examine whether an AI, lacking genuine consciousness or shared intentionality, can be considered a true collaborator.<n>This distinction has significant implications for pedagogy, instructional design, and the future research agenda for AI in education.
  arXiv  Detail & Related papers  (2025-08-20T16:17:32Z)
• 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)
• 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.<n>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)
• Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
  This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
  arXiv  Detail & Related papers  (2024-11-29T14:55:15Z)
• 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)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Towards social generative AI for education: theory, practices and ethics [0.0]
  Building social generative AI for education will require development of powerful AI systems that can converse with each other as well as humans. We need to consider how to design and constrain social generative AI for education.
  arXiv  Detail & Related papers  (2023-06-14T17:30:48Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• 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.