mlsanityData to 14 Oct 2026

Foundation Models for Education: Promises and Prospects

arXiv 2405.10959 ↗First posted 8 Apr 2024cs.CY cs.LGcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

15 of 20 references.

References · 15

  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  3. 2024Large Language Models for Education: A Survey and Outlookcited by 188
  4. 2023MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoningcited by 156
  5. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  6. 2023Art and the science of generative AI: A deeper divecited by 95
  7. 2023Automatic Model Selection with Large Language Models for Reasoningcited by 35
  8. 2023Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generationcited by 29
  9. 2024Bringing Generative AI to Adaptive Learning in Educationcited by 24
  10. 2023Integrating Graphs with Large Language Models: Methods and Prospectscited by 18
  11. 2024Can AI Be as Creative as Humans?cited by 17
  12. 2024Generative AI for Education (GAIED): Advances, Opportunities, and Challengescited by 13
3 more
  1. 2023Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learningcited by 9
  2. 2024The Ethics of AI in Educationcited by 7
  3. 2024Automate Knowledge Concept Tagging on Math Questions with LLMscited by 6

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Cited by year

Citing papers per year: 2024: 5; 2025: 5.

10 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption
  2. 2025Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations
  3. 2026Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence
  4. 2026Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning
  5. 2026AgentSchool: An LLM-Powered Multi-Agent Simulation for Education
  6. 2025AgentSME for Simulating Diverse Communication Modes in Smart Education
  7. 2025Evolution of AI in Education: Agentic Workflows
  8. 2025A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents