Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation
- URL: http://arxiv.org/abs/2411.15971v1
- Date: Sun, 24 Nov 2024 19:53:48 GMT
- Title: Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation
- Authors: Chiranjeevi Bura, Praveen Kumar Myakala,
- Abstract summary: Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement.
This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps.
- Score: 0.0
- License:
- Abstract: Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement. This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps. Ethical considerations such as algorithmic bias, data privacy, and AI role in human centric education are emphasized. The findings underscore the need for responsible AI integration that ensures accessibility, equity, and innovation in educational systems.
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) - The Impact of Generative Artificial Intelligence on Ideation and the performance of Innovation Teams (Preprint) [0.0]
The study applies the Knowledge Spillover Theory of Entrepreneurship to understand the effects of AI on knowledge spillover, gen-eration and application.
Results show that GenAI has a positive impact on important elements of the Knowledge Spillover Theory of Entrepeneurship.
arXiv Detail & Related papers (2024-09-23T18:25:49Z) - 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) - Open Problems in Technical AI Governance [93.89102632003996]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.
This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Collaborative Design of AI-Enhanced Learning Activities [0.0]
We develop a formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices.
Participants reflect on AI's potential in teaching and learning by exploring different activities that can integrate AI literacy in education, including its ethical considerations and potential for innovative pedagogy.
arXiv Detail & Related papers (2024-07-09T08:34:08Z) - The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges [2.569083526579529]
AI in education raises ethical concerns regarding validity, reliability, transparency, fairness, and equity.
Various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education.
In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement.
arXiv Detail & Related papers (2024-06-27T05:28:40Z) - 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) - Generative AI and Its Educational Implications [0.0]
We discuss the implications of generative AI on education across four critical sections.
We propose ways in which generative AI can transform the educational landscape.
Acknowledging the societal impact, we emphasize the need for updating curricula.
arXiv Detail & Related papers (2023-12-26T21:29:31Z) - 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.