AI Agents and Education: Simulated Practice at Scale
- URL: http://arxiv.org/abs/2407.12796v1
- Date: Thu, 20 Jun 2024 05:26:04 GMT
- Title: AI Agents and Education: Simulated Practice at Scale
- Authors: Ethan Mollick, Lilach Mollick, Natalie Bach, LJ Ciccarelli, Ben Przystanski, Daniel Ravipinto,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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, offering students the opportunity to practice skills in scenarios with AI-generated mentors, role-players, and instructor-facing evaluators. We describe a prototype, PitchQuest, a venture capital pitching simulator that showcases the capabilities of AI in delivering instruction, facilitating practice, and providing tailored feedback. The paper discusses the pedagogy behind the simulation, the technology powering it, and the ethical considerations in using AI for education. While acknowledging the limitations and need for rigorous testing, we propose that generative AI can significantly lower the barriers to creating effective, engaging simulations, opening up new possibilities for experiential learning at scale.
Related papers
- Generative AI in Simulation-Based Test Environments for Large-Scale Cyber-Physical Systems: An Industrial Study [2.432409923443071]
Quality assurance for large-scale cyber-physical systems relies on sophisticated test activities.<n>Recent advances in generative AI have led to tools that can produce executable test cases for software systems.<n>The application of generative AI techniques to simulation-based testing of large-scale cyber-physical systems remains underexplored.
arXiv Detail & Related papers (2025-12-05T08:09:13Z) - Dyna-Mind: Learning to Simulate from Experience for Better AI Agents [62.21219817256246]
We argue that current AI agents need ''vicarious trial and error'' - the capacity to mentally simulate alternative futures before acting.<n>We introduce Dyna-Mind, a two-stage training framework that explicitly teaches (V)LM agents to integrate such simulation into their reasoning.
arXiv Detail & Related papers (2025-10-10T17:30:18Z) - Future-Proofing Programmers: Optimal Knowledge Tracing for AI-Assisted Personalized Education [17.137781747517522]
CoTutor is an AI-driven model that enhances Bayesian Knowledge Tracing with signal processing techniques to improve student progress modeling.<n>In university trials, it has demonstrated measurable improvements in learning outcomes while outperforming conventional educational tools.<n>Our results highlight its potential for AI-driven personalization, scalability, and future opportunities for advancing privacy and ethical considerations in educational technology.
arXiv Detail & Related papers (2025-09-28T17:40:39Z) - Agentic Workflow for Education: Concepts and Applications [7.875055566698523]
This study introduces the Agentic for Education (AWE), a four-component model comprising self-reflection, tool invocation, task planning, and multi-agent collaboration.<n>AWE offers a promising path toward reducing teacher workload, enhancing instructional quality, and enabling broader educational innovation.
arXiv Detail & Related papers (2025-09-01T14:39:48Z) - YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models [50.86336063222539]
We introduce a novel social simulator called YuLan-OneSim.<n>Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator.<n>We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication.
arXiv Detail & Related papers (2025-05-12T14:05:17Z) - Evolution of AI in Education: Agentic Workflows [2.1681971652284857]
Artificial intelligence (AI) has transformed various aspects of education.
Large language models (LLMs) are driving advancements in automated tutoring, assessment, and content generation.
To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation.
arXiv Detail & Related papers (2025-04-25T13:44:57Z) - Generative AI and Its Impact on Personalized Intelligent Tutoring Systems [0.0]
Generative AI enables personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways.
Report explores key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems.
Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education.
arXiv Detail & Related papers (2024-10-14T16:01:01Z) - AI-Tutoring in Software Engineering Education [0.7631288333466648]
We conducted an exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis.
The findings highlight advantages, such as timely feedback and scalability.
However, challenges like generic responses and students' concerns about a learning progress inhibition when using the AI-Tutor were also evident.
arXiv Detail & Related papers (2024-04-03T08:15:08Z) - RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios [58.62407014256686]
RealGen is a novel retrieval-based in-context learning framework for traffic scenario generation.
RealGen synthesizes new scenarios by combining behaviors from multiple retrieved examples in a gradient-free way.
This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios.
arXiv Detail & Related papers (2023-12-19T23:11:06Z) - RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
arXiv Detail & Related papers (2023-11-02T17:59:21Z) - Exploration with Principles for Diverse AI Supervision [88.61687950039662]
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI.
While this generative AI approach has produced impressive results, it heavily leans on human supervision.
This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation.
We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
arXiv Detail & Related papers (2023-10-13T07:03:39Z) - Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous
Driving Research [76.93956925360638]
Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes.
It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training.
We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
arXiv Detail & Related papers (2023-10-12T20:49:15Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - 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.
However, implementing these technologies also introduces a host of ethical considerations that must thoughtfully be addressed.
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.
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) - Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark
and Case Study for Robotics Manipulation [18.392301524812645]
As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes.
Recent studies in robotics manipulation have started employing artificial intelligence (AI) approaches as controllers to achieve better adaptability and performance.
We propose a public industrial benchmark for robotics manipulation in this paper.
arXiv Detail & Related papers (2023-07-31T18:21:45Z) - Explainability via Responsibility [0.9645196221785693]
We present an approach to explainable artificial intelligence in which certain training instances are offered to human users.
We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions.
arXiv Detail & Related papers (2020-10-04T20:41:03Z) - The Chef's Hat Simulation Environment for Reinforcement-Learning-Based
Agents [54.63186041942257]
We propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in Human-Robot Interaction scenarios.
This paper provides a controllable and reproducible scenario for reinforcement-learning algorithms.
arXiv Detail & Related papers (2020-03-12T15:52:49Z)
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.