mlsanityData to 14 Oct 2026

Unpacking the "Black Box" of AI in Education

arXiv 2301.01602 ↗First posted 31 Dec 2022cs.CY cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations-many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 84 references that are in the map. First posted on arXiv 31 Dec 2022.

Builds on

13 of 84 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  6. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  7. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  8. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,319
  9. 2016Learning to learn by gradient descent by gradient descentcited by 620
  10. 2017Adversarial Patchcited by 517
  11. 2015Deep Knowledge Tracingcited by 332
  12. 2016Learning Representations for Counterfactual Inferencecited by 329
1 more
  1. 2017Deep Reinforcement Learning for De-Novo Drug Designcited by 161

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3.

5 papers.

  1. 2023Applying Large Language Models and Chain-of-Thought for Automatic Scoringcited by 69
  2. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
  3. 2024From Guidelines to Governance: A Study of AI Policies in Educationcited by 4
  4. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  5. 2024Artificial intelligence and the transformation of higher education institutionscited by 1