CoChat
Sign Up
Cited by 379Ethics and Social Impacts of AIArtificial Intelligence in EducationArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index, and 1 are not checked yet.

Human-centered artificial intelligence in education: Seeing the invisible through the visible

Open access
Yang, Stephen J.H., Ogata, Hiroaki, Matsui, Tatsunori, Chen, Nian-Shing·2021·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2021.100008
Open this paper in CoChat — free accountOpen PDF

Abstract

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.caeai.2021.100008

Cites

31 of 32 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 32 references

Research works printed but not linked: 1

  1. Yang, 2019, Precision education: new challenges for AI in education [conference keynote]

Cited by

Sorted by citations

Items 101–200 of 373

Related

See all

Metadata

Authors
Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S.
Affiliations
  • National Central University (TW)
  • Kyoto University (JP)
  • Waseda University (JP)
  • National Yunlin University of Science and Technology (TW)
Corresponding author
Yang, Stephen J.H.
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2021
Volume
2
Pages
100008
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2021.100008
OpenAlex
W3123649662
Cited by
379
References
32
Citations by year
2026: 41 · 2025: 93 · 2024: 113 · 2023: 66 · 2022: 42 · 2021: 24
FWCI
76.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by-nc-nd)
Funders
Ministry of Science and Technology, Taiwan
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
human-centered AI, artificial intelligence in education, AI evaluation, algorithmic bias, AI governance, human rights, inequality, human welfare

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review