Foundation Models for Education: Promises and Prospects
- URL: http://arxiv.org/abs/2405.10959v1
- Date: Mon, 8 Apr 2024 15:59:37 GMT
- Title: Foundation Models for Education: Promises and Prospects
- Abstract summary: We discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities.
We highlight the risks and opportunities of AI overreliance and creativity.
We envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.
- Score: 24.75073974210808
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the advent of foundation models like ChatGPT, educators are excited about the transformative role that AI might play in propelling the next education revolution. The developing speed and the profound impact of foundation models in various industries force us to think deeply about the changes they will make to education, a domain that is critically important for the future of humans. In this paper, we discuss the strengths of foundation models, such as personalized learning, education inequality, and reasoning capabilities, as well as the development of agent architecture tailored for education, which integrates AI agents with pedagogical frameworks to create adaptive learning environments. Furthermore, we highlight the risks and opportunities of AI overreliance and creativity. Lastly, we envision a future where foundation models in education harmonize human and AI capabilities, fostering a dynamic, inclusive, and adaptive educational ecosystem.
Related papers
- Bidirectional Human-AI Alignment in Education for Trustworthy Learning Environments [7.0064528229443]
Artificial intelligence (AI) is transforming education, offering unprecedented opportunities to personalize learning, enhance assessment, and support educators.<n>Yet these opportunities also introduce risks related to equity, privacy, and student autonomy.<n>This chapter develops the concept of bidirectional human-AI alignment in education, emphasizing that trustworthy learning environments arise not only from embedding human values into AI systems but also from equipping teachers, students, and institutions with the skills to interpret, critique, and guide these technologies.
arXiv Detail & Related papers (2025-12-25T07:50:56Z) - A principled way to think about AI in education: guidance for action based on goals, models of human learning, and use of technologies [0.0]
I articulate a set of principles that connect broad our educational goalsto actionable practices.<n>The piece illustrates how a principled approach enables higher education to harness new tools while preserving its fundamental mission.
arXiv Detail & Related papers (2025-10-01T21:19:12Z) - Perspectives and potential issues in using artificial intelligence for computer science education [0.0]
ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions.<n>While AI technologies hold potential for enhancing learning experiences, there are also emerging concerns.<n>These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations.
arXiv Detail & Related papers (2025-09-17T06:34:23Z) - Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges [87.02855999212817]
Edge General Intelligence (EGI) represents a transformative evolution of edge computing, where distributed agents possess the capability to perceive, reason, and act autonomously.<n>World models act as proactive internal simulators that not only predict but also actively imagine future trajectories, reason under uncertainty, and plan multi-step actions with foresight.<n>This survey bridges the gap by offering a comprehensive analysis of how world models can empower agentic artificial intelligence (AI) systems at the edge.
arXiv Detail & Related papers (2025-08-13T07:29:40Z) - Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework [0.0]
This study introduces a novel conceptual framework integrating Generative Artificial Intelligence and Learning Analytics to cultivate Self-Directed Growth.
A2PL model reconceptualizes the interplay of learner aspirations, complex thinking, and self-assessment within GAI supported environments.
arXiv Detail & Related papers (2025-04-29T15:19:48Z) - World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child [10.183372891207966]
World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making.
We highlight six key research areas -- physics-informed learning, neurosymbolic learning, continual learning, causal inference, human-in-the-loop AI, and responsible AI -- as essential for enabling true reasoning in AI.
arXiv Detail & Related papers (2025-03-19T12:50:40Z) - Smart Learning in the 21st Century: Advancing Constructionism Across Three Digital Epochs [0.0]
This article explores the evolution of constructionism as an educational framework.
It traces its relevance and transformation across three pivotal eras: the advent of personal computing, the networked society, and the current era of generative AI.
arXiv Detail & Related papers (2025-01-13T17:04:06Z) - 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) - 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) - 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) - A Survey of Reasoning with Foundation Models [235.7288855108172]
Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation.
We introduce seminal foundation models proposed or adaptable for reasoning.
We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
arXiv Detail & Related papers (2023-12-17T15:16:13Z) - World Models and Predictive Coding for Cognitive and Developmental
Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding.
In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z) - 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) - Confronting Structural Inequities in AI for Education [5.371816551086117]
We argue that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce.
We demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance.
arXiv Detail & Related papers (2021-05-18T22:13:35Z)
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.