Overview of the development of smart classrooms under information technology: development and innovation of hardware and software

• URL: http://arxiv.org/abs/2412.20730v1
• Date: Mon, 30 Dec 2024 06:01:10 GMT
• Title: Overview of the development of smart classrooms under information technology: development and innovation of hardware and software
• Authors: Yanying Cheng,
• Abstract summary: This article reviews the development of smart classrooms from the hardware and software levels.<n>The hardware describes the transformation from basic ICT facilities in single mode to a multi-modal information cloud platform.<n>In terms of software, we look at the evolution of related supporting algorithms and technologies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of information and communication technology (ICT), smart classroom has become an important trend in education modernization. This article reviews the development of smart classrooms from the hardware and software levels. The hardware describes the transformation from the construction of basic ICT facilities in single mode to a multi-modal information cloud platform. In terms of software, we look at the evolution of related supporting algorithms and technologies from the platform construction technology to the integration of advanced artificial intelligence (AI) technology from the perspectives of learning analysis and data mining. Provide guidance and suggestions for future educators, researchers and policymakers on the future direction of smart classrooms.

Related papers

• Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models [16.16798813072285]
  The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models.
  arXiv  Detail & Related papers  (2025-03-08T02:59:51Z)
• Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
  The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
  arXiv  Detail & Related papers  (2024-10-28T13:03:09Z)
• Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications [17.624263707781655]
  Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management.<n>This article delves into the foundational concepts and cutting-edge developments in these fields.<n>By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management.
  arXiv  Detail & Related papers  (2024-10-02T06:24:51Z)
• 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)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Agility in Software 2.0 -- Notebook Interfaces and MLOps with Buttresses and Rebars [9.327920030279586]
  We discuss two contemporary development phenomena that are fundamental in machine learning development. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context.
  arXiv  Detail & Related papers  (2021-11-28T13:40:30Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• AI in Smart Cities: Challenges and approaches to enable road vehicle automation and smart traffic control [56.73750387509709]
  SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
  arXiv  Detail & Related papers  (2021-04-07T14:31:08Z)
• Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation [8.54335661175611]
  We have identified the benefits and challenges of implementing artificial intelligence in the education sector. We have also reviewed modern AI technologies for learners and educators, currently available on the software market. We have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide.
  arXiv  Detail & Related papers  (2021-02-11T11:09:41Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT [0.0]
  The paper first reviews the evolution of reliability modelling technology in the past 90 years and discusses major technologies developed in industry and academia. We then introduce the next generation maintenance framework - Intelligent Maintenance, and discuss its key components. This AI and IIoT based Intelligent Maintenance framework is composed of (1) latest machine learning algorithms including probabilistic reliability modelling with deep learning, (2) real-time data collection, transfer, and storage through wireless smart sensors, (3) Big Data technologies, (4) continuously integration and deployment of machine learning models, (5) mobile device and AR/VR applications for fast and better decision-making in the field.
  arXiv  Detail & Related papers  (2020-09-01T11:10:13Z)

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.