Artificial Intelligence Ecosystem for Automating Self-Directed Teaching

• URL: http://arxiv.org/abs/2411.07300v1
• Date: Mon, 11 Nov 2024 19:00:22 GMT
• Title: Artificial Intelligence Ecosystem for Automating Self-Directed Teaching
• Authors: Tejas Satish Gotavade,
• Abstract summary: This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization.
• Score: 0.0
• License:
• Abstract: This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization. By integrating real-time virtual assistance for doubt resolution, the platform addresses the immediate educational needs of learners while promoting autonomous learning practices. This study explores the psychological advantages of self-directed learning and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms. The research contributes to the growing field of educational technology by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies.

Related papers

• 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)
• 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)
• Ontology-driven Reinforcement Learning for Personalized Student Support [1.8972913066829966]
  This paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system. We apply for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students.
  arXiv  Detail & Related papers  (2024-07-14T21:11:44Z)
• SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation [54.97931304488993]
  Self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. We propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tune pre-trained control policies. We report improvements in terms of collision avoidance, as well as more socially compliant behavior, measured by a human user study.
  arXiv  Detail & Related papers  (2024-03-01T21:27:03Z)
• The Physics of Learning: From Autoencoders to Truly Autonomous Learning Machines [0.0]
  We propose that any unsupervised learning apparatus could achieve complete independence from external energy sources. By reconceptualizing learning as an energy-seeking process, we highlight the potential for achieving true autonomy in learning systems.
  arXiv  Detail & Related papers  (2024-02-12T01:36:26Z)
• 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)
• Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education [0.2812395851874055]
  This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA) for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform.
  arXiv  Detail & Related papers  (2023-09-19T19:31:15Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Self-directed Machine Learning [86.3709575146414]
  In education science, self-directed learning has been shown to be more effective than passive teacher-guided learning. We introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML. Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection.
  arXiv  Detail & Related papers  (2022-01-04T18:32:06Z)
• Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities [1.9116784879310027]
  We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation.
  arXiv  Detail & Related papers  (2021-07-05T08:26:50Z)
• 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.