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Generative AI for Education (GAIED): Advances, Opportunities, and Challenges

Paul Denny, Sumit Gulwani, Neil T. Heffernan, Tanja Käser, Steven Moore, Anna N. Rafferty, Adish Singla

TL;DR

The paper surveys the GAIED@NeurIPS'23 workshop to elucidate how generative AI can transform education while highlighting governance and equity challenges. It catalogs the workshop's activities—topic thrusts, accepted papers, invited talks, and a diversity effort—and distills a dual focus on leveraging AI for education and safeguarding against its risks. Key contributions include mapping 33 papers to domains and learning techniques, and outlining directions in emerging curricula, DEI, and evaluation metrics. The authors argue for sustained, multidisciplinary collaboration to responsibly advance AI-enabled education and to scale impact across diverse learning communities.

Abstract

This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a community-building effort to bring together researchers, educators, and practitioners to explore the potential of generative AI for enhancing education. This article aims to provide an overview of the workshop activities and highlight several future research directions in the area of GAIED.

Generative AI for Education (GAIED): Advances, Opportunities, and Challenges

TL;DR

The paper surveys the GAIED@NeurIPS'23 workshop to elucidate how generative AI can transform education while highlighting governance and equity challenges. It catalogs the workshop's activities—topic thrusts, accepted papers, invited talks, and a diversity effort—and distills a dual focus on leveraging AI for education and safeguarding against its risks. Key contributions include mapping 33 papers to domains and learning techniques, and outlining directions in emerging curricula, DEI, and evaluation metrics. The authors argue for sustained, multidisciplinary collaboration to responsibly advance AI-enabled education and to scale impact across diverse learning communities.

Abstract

This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a community-building effort to bring together researchers, educators, and practitioners to explore the potential of generative AI for enhancing education. This article aims to provide an overview of the workshop activities and highlight several future research directions in the area of GAIED.
Paper Structure (13 sections, 2 figures)

This paper contains 13 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Overview of the GAIED@NeurIPS'23 Workshop Activities
  3. Topics of Interests
  4. Accepted Papers and Poster Sessions
  5. Invited Talks and Panel Session
  6. Diversity Statement
  7. Research Topics and Directions in GAIED
  8. Popular Research Topics
  9. Key Research Directions
  10. Emerging curricula and skill sets.
  11. Diversity, equity, inclusion, and accessibility.
  12. Metrics and evaluation.
  13. Conclusions

Figures (2)

  • Figure 1: Word cloud created from titles of the $33$ accepted papers at the workshop.
  • Figure 2: Summary of the $33$ accepted papers at the workshop along different dimensions, including the application domain and educational scenario considered, models used, and specific learning/training techniques employed.