Multimodality of AI for Education: Towards Artificial General
Intelligence
- URL: http://arxiv.org/abs/2312.06037v2
- Date: Tue, 12 Dec 2023 15:26:38 GMT
- Title: Multimodality of AI for Education: Towards Artificial General
Intelligence
- Authors: Gyeong-Geon Lee, Lehong Shi, Ehsan Latif, Yizhu Gao, Arne Bewersdorff,
Matthew Nyaaba, Shuchen Guo, Zihao Wu, Zhengliang Liu, Hui Wang, Gengchen
Mai, Tiaming Liu, and Xiaoming Zhai
- Abstract summary: multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts.
This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, and the integration of diverse multimodal data sources.
The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development.
- Score: 14.121655991753483
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper presents a comprehensive examination of how multimodal artificial
intelligence (AI) approaches are paving the way towards the realization of
Artificial General Intelligence (AGI) in educational contexts. It scrutinizes
the evolution and integration of AI in educational systems, emphasizing the
crucial role of multimodality, which encompasses auditory, visual, kinesthetic,
and linguistic modes of learning. This research delves deeply into the key
facets of AGI, including cognitive frameworks, advanced knowledge
representation, adaptive learning mechanisms, strategic planning, sophisticated
language processing, and the integration of diverse multimodal data sources. It
critically assesses AGI's transformative potential in reshaping educational
paradigms, focusing on enhancing teaching and learning effectiveness, filling
gaps in existing methodologies, and addressing ethical considerations and
responsible usage of AGI in educational settings. The paper also discusses the
implications of multimodal AI's role in education, offering insights into
future directions and challenges in AGI development. This exploration aims to
provide a nuanced understanding of the intersection between AI, multimodality,
and education, setting a foundation for future research and development in AGI.
Related papers
- Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [54.247747237176625]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - A call for embodied AI [1.7544885995294304]
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence.
By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures.
This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
arXiv Detail & Related papers (2024-02-06T09:11:20Z) - From Algorithm Worship to the Art of Human Learning: Insights from 50-year journey of AI in Education [0.0]
Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension.
This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators.
It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications.
arXiv Detail & Related papers (2024-02-05T16:12:14Z) - 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) - Taking the Next Step with Generative Artificial Intelligence: The
Transformative Role of Multimodal Large Language Models in Science Education [14.679589098673416]
Multimodal Large Language Models (MLLMs) are capable of processing multimodal data including text, sound, and visual inputs.
This paper explores the transformative role of MLLMs in central aspects of science education by presenting exemplary innovative learning scenarios.
arXiv Detail & Related papers (2024-01-01T18:11:43Z) - A Vision for Operationalising Diversity and Inclusion in AI [5.4897262701261225]
This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems.
A significant challenge in AI development is the effective operationalization of D&I principles.
This paper proposes a vision of a framework for developing a tool utilizing persona-based simulation by Generative AI (GenAI)
arXiv Detail & Related papers (2023-12-11T02:44:39Z) - 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) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - 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) - 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.