Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation
- URL: http://arxiv.org/abs/2604.07285v1
- Date: Wed, 08 Apr 2026 16:49:24 GMT
- Title: Why teaching resists automation in an AI-inundated era: Human judgment, non-modular work, and the limits of delegation
- Abstract summary: I argue that although AI can support some bounded functions, instructional work remains difficult to automate in meaningful ways.<n>As long as educational practice relies on emergent understanding of human cognition and learning, teaching remains a form of professional work that resists automation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Debates about artificial intelligence (AI) in education often portray teaching as a modular and procedural job that can increasingly be automated or delegated to technology. This brief communication paper argues that such claims depend on treating teaching as more separable than it is in practice. Drawing on recent literature and empirical studies of large language models and retrieval-augmented generation systems, I argue that although AI can support some bounded functions, instructional work remains difficult to automate in meaningful ways because it is inherently interpretive, relational, and grounded in professional judgment. More fundamentally, teaching and learning are shaped by human cognition, behavior, motivation, and social interaction in ways that cannot be fully specified, predicted, or exhaustively modeled. Tasks that may appear separable in principle derive their instructional value in practice from ongoing contextual interpretation across learners, situations, and relationships. As long as educational practice relies on emergent understanding of human cognition and learning, teaching remains a form of professional work that resists automation. AI may improve access to information and support selected instructional activities, but it does not remove the need for human judgment and relational accountability that effective teaching requires.
Related papers
- Evaluation in the Age of AI: Output as Evidence of Learning [0.0]
The rapid adoption of artificial intelligence (AI) has disrupted how learning is demonstrated and evaluated in higher education.<n>This paper examines the ethical challenges of educational evaluation in the age of AI from a university-level perspective.
arXiv Detail & Related papers (2026-08-23T23:40:30Z) - The New Associationism: Lessons from Deep Learning [0.0]
This paper argues that taking AI seriously supports a modest but genuine associationism.<n>The central finding is that supervised learning underlies a surprisingly wide range of contemporary AI systems.<n>It defuses the once-influential argument that associationist mechanisms are too limited to account for human cognitive capacities.
arXiv Detail & Related papers (2026-05-19T10:51:37Z) - Agentivism: a learning theory for the age of artificial intelligence [2.278981812218892]
Generative and agentic AI create a new condition by allowing learners to delegate cognitive work to systems that can generate, recommend, and sometimes act on the learner's behalf.<n>This creates a fundamental challenge for learning theory: successful performance can no longer be assumed to indicate learning.<n>We propose Agentivism, a learning theory for human-AI interaction.
arXiv Detail & Related papers (2026-04-09T05:09:48Z) - Beyond Detection: Rethinking Education in the Age of AI-writing [58.49695743664001]
As generative AI tools like ChatGPT enter classrooms, workplaces and everyday thinking, writing is at risk of becoming a formality.<n>This paper explores what is lost when we let machines write for us, drawing on cognitive psychology, educational theory and real classroom practices.
arXiv Detail & Related papers (2026-03-26T11:16:01Z) - Comprehensive AI Literacy: The Case for Centering Human Agency [0.0]
We are witnessing the rise of a dangerous gap, where a focus on the functional, operational skills of using AI tools is eclipsing the development of critical and ethical reasoning about them.<n>This position paper argues for a systemic shift toward comprehensive AI literacy that centers human agency.
arXiv Detail & Related papers (2025-12-18T15:25:38Z) - Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence [59.07578850674114]
Sound deductive reasoning is an indisputably desirable aspect of general intelligence.<n>It is well-documented that even the most advanced frontier systems regularly and consistently falter on easily-solvable reasoning tasks.<n>We argue that their unsound behavior is a consequence of the statistical learning approach powering their development.
arXiv Detail & Related papers (2025-06-30T14:37:50Z) - AI Pedagogy: Dialogic Social Learning for Artificial Agents [0.6553587309274792]
This study explores the potential of socially mediated learning paradigms to address limitations of traditional AI training approaches.<n>We introduce a dynamic environment, termed the 'AI Social Gym', where an AI learner agent engages in dyadic pedagogical dialogues with knowledgeable AI teacher agents.<n>Our investigation focuses on how different pedagogical strategies impact the AI learning process in the context of acquisition.
arXiv Detail & Related papers (2025-05-25T11:19:48Z) - Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room [2.674109837115132]
This analysis identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods in education.<n>These include the lack of clarity around what AI for education truly means and the trend of equating it with domain-agnostic, company-driven large language models.<n>We demonstrate how hybrid AI methods -- specifically neural-symbolic AI -- can address the elephant in the room and serve as the foundation for responsible, trustworthy AI systems in education.
arXiv Detail & Related papers (2025-04-22T13:20:43Z) - AI in Education: Rationale, Principles, and Instructional Implications [0.0]
Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role.<n>The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort.
arXiv Detail & Related papers (2024-12-02T14:08:07Z) - YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
This paper introduces YODA, a teacher-student progressive learning framework.
It emulates the teacher-student education process to improve the efficacy of model fine-tuning.
Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
arXiv Detail & Related papers (2024-01-28T14:32:15Z) - Progressively Efficient Learning [58.6490456517954]
We develop a novel learning framework named Communication-Efficient Interactive Learning (CEIL)
CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate efficiently by exchanging increasingly more abstract intentions.
Agents trained with CEIL quickly master new tasks, outperforming non-hierarchical and hierarchical imitation learning by up to 50% and 20% in absolute success rate.
arXiv Detail & Related papers (2023-10-13T07:52:04Z) - The Responsible Development of Automated Student Feedback with Generative AI [6.008616775722921]
Recent advancements in AI, particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback.<n>However, implementing these technologies also introduces a host of ethical considerations that must thoughtfully be addressed.<n>One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work.<n>However, the ease of automation risks a tyranny of the majority'', where the diverse needs of minority or unique learners are overlooked.
arXiv Detail & Related papers (2023-08-29T14:29:57Z) - "No, to the Right" -- Online Language Corrections for Robotic
Manipulation via Shared Autonomy [70.45420918526926]
We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution.
Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot.
We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
arXiv Detail & Related papers (2023-01-06T15:03:27Z) - Explainable Active Learning (XAL): An Empirical Study of How Local
Explanations Impact Annotator Experience [76.9910678786031]
We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting.
Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
arXiv Detail & Related papers (2020-01-24T22:52:18Z)
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.