Rapid Virtual Simulations: Achieving 'Satisficing Learning Impact' with 'Realistic-Enough' Activities in Health Science Education

• URL: http://arxiv.org/abs/2407.05179v1
• Date: Mon, 22 Apr 2024 10:03:29 GMT
• Title: Rapid Virtual Simulations: Achieving 'Satisficing Learning Impact' with 'Realistic-Enough' Activities in Health Science Education
• Authors: Emmanuel G. Blanchard, Jeffrey Wiseman,
• Abstract summary: This manuscript introduces the concept of Rapid Virtual Simulations, a new techno-pedagogical activity that fosters expert autonomy for creating virtual educational simulations. It is grounded in a Realistic-Enough Philosophy that consists of pursuing the development of the least complex simulation while still ensuring a Satisficing (or good enough) Learning Impact.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This manuscript introduces the concept of Rapid Virtual Simulations, a new techno-pedagogical activity that fosters expert autonomy for creating virtual educational simulations. It is grounded in a Realistic-Enough Philosophy that consists of pursuing the development of the least complex simulation while still ensuring a Satisficing (or good enough) Learning Impact. It also introduces the concept of a Rapid Virtual Simulation Ecosystem as an integrated set of technological modules that facilitates the work of health professional educators while multiplying educational affordances for learners. Finally, this manuscript presents an argument for technological agility and simplicity as key guiding principles for the design of future simulation-based educational systems.

Related papers

• MicroSims: A Framework for AI-Generated, Scalable Educational Simulations with Universal Embedding and Adaptive Learning Support [0.0]
  This paper introduces MicroSims, a framework for creating lightweight, interactive educational simulations.<n>MicroSims can be rapidly generated using artificial intelligence, embedded across digital learning platforms, and easily customized without programming knowledge.<n>We show that interactive simulations can improve conceptual understanding by up to 30-40% compared to traditional instruction.
  arXiv  Detail & Related papers  (2025-11-25T03:14:39Z)
• The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation [55.722188569369656]
  This paper introduces a novel framework for memory-based student simulation.<n>It incorporates developmental trajectories through a hierarchical memory mechanism with structured knowledge representation.<n>In practice, we implement a curriculum-aligned simulator grounded on the Next Generation Science Standards.
  arXiv  Detail & Related papers  (2025-11-08T08:05:43Z)
• Learning Ecology with VERA Using Conceptual Models and Simulations [1.631115063641726]
  The VERA system is a conceptual modeling tool used since 2016 to provide introductory college biology students with the capability of conceptual modeling and agent-based simulation in the ecological domain.<n>This paper describes VERA and its approach to coupling conceptual modeling and simulation with emphasis on how a model's visual syntax is compiled into code executable on a NetLogo simulation engine.
  arXiv  Detail & Related papers  (2025-10-19T17:48:29Z)
• MultiGen: Using Multimodal Generation in Simulation to Learn Multimodal Policies in Real [128.7629907902049]
  MultiGen is a framework that integrates large-scale generative models into traditional physics simulators.<n>We demonstrate effective zero-shot transfer to real-world pouring with novel containers and liquids.
  arXiv  Detail & Related papers  (2025-07-03T17:59:58Z)
• Enter: Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training [0.0]
  We argue that hyper-realism is not always optimal, as high-fidelity avatars can impose excessive extraneous cognitive load on novices.<n>A significant gap exists between the technological drive for photorealism and the pedagogical need for scaffolded learning.<n>We propose Graduated Realism, a framework advocating for starting trainees with lower-fidelity avatars.
  arXiv  Detail & Related papers  (2025-06-13T15:37:36Z)
• Immersive Virtual Reality Environments for Embodied Learning of Engineering Students [0.0]
  This paper presents a novel framework for virtual laboratory environments (VLEs) focused on embodied learning. Our framework employs an event-driven, directed-graph-based architecture developed with Unity 3D and C#, ensuring modularity and scalability. Results demonstrated significant improvements in student comprehension and retention, with notable increases in test scores compared to traditional non-embodied VR methods.
  arXiv  Detail & Related papers  (2025-03-17T00:52:31Z)
• Generative Physical AI in Vision: A Survey [78.07014292304373]
  Gene Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. As generative models evolve to increasingly integrate physical realism and dynamic simulation, their potential to function as "world simulators" expands.
  arXiv  Detail & Related papers  (2025-01-19T03:19:47Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter. In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• AI Agents and Education: Simulated Practice at Scale [0.0]
  This paper explores the potential of generative AI in creating adaptive educational simulations. By leveraging a system of multiple AI agents, simulations can provide personalized learning experiences. We describe a prototype, PitchQuest, a venture capital pitching simulator that showcases the capabilities of AI in delivering instruction.
  arXiv  Detail & Related papers  (2024-06-20T05:26:04Z)
• DrEureka: Language Model Guided Sim-To-Real Transfer [64.14314476811806]
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball.
  arXiv  Detail & Related papers  (2024-06-04T04:53:05Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Reward Function Design for Crowd Simulation via Reinforcement Learning [12.449513548800466]
  Reinforcement learning has shown great potential in simulating virtual crowds, but the design of the reward function is critical to achieving effective and efficient results. We provide theoretical insights on the validity of certain reward functions according to their analytical properties, and evaluate them empirically using a range of scenarios. Our findings can inform the development of new crowd simulation techniques, and contribute to the wider study of human-like navigation.
  arXiv  Detail & Related papers  (2023-09-22T12:55:30Z)
• Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs [75.7104463046767]
  This paper proposes a novel learning based simulation model that characterizes the varying spatial and temporal dependencies in particle systems. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb.
  arXiv  Detail & Related papers  (2023-05-21T03:51:03Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Thermodynamics-informed neural networks for physically realistic mixed reality [0.09332987715848712]
  We present a method for computing the dynamic response of deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience.
  arXiv  Detail & Related papers  (2022-10-24T17:30:08Z)
• Virtual Reality based Digital Twin System for remote laboratories and online practical learning [0.08431877864777444]
  There is a need for remote learning and virtual learning applications such as virtual reality (VR) and tablet-based solutions. A case study describing the creation of a virtual learning application for an electrical laboratory tutorial is presented.
  arXiv  Detail & Related papers  (2021-06-17T09:38:24Z)
• Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning [0.0]
  We develop a web application that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs. The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment.
  arXiv  Detail & Related papers  (2020-08-24T22:48:21Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

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.