Using Curriculum Theory to Inform Approaches to Generative AI in Schools

• URL: http://arxiv.org/abs/2309.13053v1
• Date: Thu, 7 Sep 2023 05:38:36 GMT
• Title: Using Curriculum Theory to Inform Approaches to Generative AI in Schools
• Authors: Myke Healy
• Abstract summary: The study delineates the multifaceted relationship between Generative AI and Elliot Eisner's explicit, implicit, and null curriculum concepts. It scrutinizes the logistical and ethical challenges, such as the reliability of AI detectors, that educators confront when attempting to assimilate this nascent technology into long-standing curricular structures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In an educational landscape dramatically altered by the swift proliferation of Large Language Models, this essay interrogates the urgent this essay interrogates the urgent pedagogical modifications required in secondary schooling. Anchored in Madeline Grumet's triadic framework of curriculum inquiry, the study delineates the multifaceted relationship between Generative AI and Elliot Eisner's explicit, implicit, and null curriculum concepts. It scrutinizes the logistical and ethical challenges, such as the reliability of AI detectors, that educators confront when attempting to assimilate this nascent technology into long-standing curricular structures. Engaging with Ted Aoki's theory of the "zone of between", the essay illuminates educators' dilemmas in reconciling prescriptive curricular aims with the fluid realities of classroom life, all within an educational milieu in constant flux due to Generative AI. The paper culminates in a reflective analysis by the researcher, identifying avenues for further scholarly investigation within each of Grumet's constitutive strands of curriculum theory, thereby providing a roadmap for future research on Generative AI's transformative impact on educational practice.

Related papers

• Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis [0.0]
  This review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts.
  arXiv  Detail & Related papers  (2024-11-01T14:40:31Z)
• 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)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Generative Artificial Intelligence: A Systematic Review and Applications [7.729155237285151]
  This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI. The major impact that generative AI has made to date, has been in language generation with the development of large language models. The paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.
  arXiv  Detail & Related papers  (2024-05-17T18:03:59Z)
• 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)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Language Models: A Guide for the Perplexed [51.88841610098437]
  This tutorial aims to help narrow the gap between those who study language models and those who are intrigued and want to learn more. We offer a scientific viewpoint that focuses on questions amenable to study through experimentation. We situate language models as they are today in the context of the research that led to their development.
  arXiv  Detail & Related papers  (2023-11-29T01:19:02Z)
• White Paper: The Generative Education (GenEd) Framework [0.0]
  The Generative Education (GenEd) Framework explores the transition from Large Language Models (LLMs) to Large Multimodal Models (LMMs) in education. This paper delves into the potential of LMMs to create personalized, interactive, and emotionally-aware learning environments.
  arXiv  Detail & Related papers  (2023-10-16T23:30:42Z)
• The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie [0.196629787330046]
  This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie. The study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement. However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity.
  arXiv  Detail & Related papers  (2023-09-05T08:15:00Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)
• 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.