AI-powered Digital Framework for Personalized Economical Quality Learning at Scale

• URL: http://arxiv.org/abs/2412.04483v1
• Date: Wed, 20 Nov 2024 17:44:29 GMT
• Title: AI-powered Digital Framework for Personalized Economical Quality Learning at Scale
• Authors: Mrzieh VatandoustMohammadieh, Mohammad Mahdi Mohajeri, Ali Keramati, Majid Nili Ahmadabadi,
• Abstract summary: This paper proposes an AI-powered digital learning framework grounded in Deep Learning (DL) theory. We outline eight key principles derived from learning science and AI that are essential for implementing DL-based Digital Learning Environments.
• Score: 0.7864304771129749
• License:
• Abstract: The disparity in access to quality education is significant, both between developed and developing countries and within nations, regardless of their economic status. Socioeconomic barriers and rapid changes in the job market further intensify this issue, highlighting the need for innovative solutions that can deliver quality education at scale and low cost. This paper addresses these challenges by proposing an AI-powered digital learning framework grounded in Deep Learning (DL) theory. The DL theory emphasizes learner agency and redefines the role of teachers as facilitators, making it particularly suitable for scalable educational environments. We outline eight key principles derived from learning science and AI that are essential for implementing DL-based Digital Learning Environments (DLEs). Our proposed framework leverages AI for learner modelling based on Open Learner Modeling (OLM), activity suggestions, and AI-assisted support for both learners and facilitators, fostering collaborative and engaging learning experiences. Our framework provides a promising direction for scalable, high-quality education globally, offering practical solutions to some of the AI-related challenges in education.

Related papers

• Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation [0.0]
  Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement. This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps.
  arXiv  Detail & Related papers  (2024-11-24T19:53:48Z)
• Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
  We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
  arXiv  Detail & Related papers  (2024-10-15T00:41:18Z)
• From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents [78.15899922698631]
  MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom. We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
  arXiv  Detail & Related papers  (2024-09-05T13:22:51Z)
• Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration [0.0]
  This paper presents the Comprehensive AI Assessment Framework (CAIAF), an evolved version of the AI Assessment Scale (AIAS) by Perkins, Furze, Roe, and MacVaugh. The CAIAF incorporates stringent ethical guidelines, with clear distinctions based on educational levels, and advanced AI capabilities. The framework will ensure better learning outcomes, uphold academic integrity, and promote responsible use of AI.
  arXiv  Detail & Related papers  (2024-06-07T07:18:42Z)
• Foundation Models for Education: Promises and Prospects [24.75073974210808]
  We discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities. We highlight the risks and opportunities of AI overreliance and creativity. We envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.
  arXiv  Detail & Related papers  (2024-04-08T15:59:37Z)
• 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)
• 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)
• Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model [0.0]
  Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
  arXiv  Detail & Related papers  (2021-08-12T15:42:32Z)
• 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)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• 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.