Human-centered Artificial Intelligence in education: Seeing the invisible through the visible
Open accessAbstract
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.
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This finding resonates with the call for explainable and human-centred AI, which stresses the vital role of stakeholders in developing meaningful and impactful educational technology.
“This finding resonates with the call for explainable and human-centred AI, which stresses the vital role of stakeholders in developing meaningful and impactful educational technology (Khosravi et al., 2022;Yang et al., 2021).”
From Practical and ethical challenges of large language models in education: A systematic scoping review (2024) · cites this paper as Yang et al., 2021
Section 2.2 calls for explainable AI, and Section 3 says learning technology must be human-centred and attentive to human feelings and outcomes. The Abstract advocates dialogue between technology- and humanity-based researchers, but the copy does not make the broader claim that stakeholders play a vital role in developing meaningful and impactful educational technology. No page numbers are available in the indexed passages. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTeachers struggle to guide students with AI applications because they lack the ability to efficiently analyze data or integrate AI into instructional design.
“It is a struggle for teachers to guide students with the help of AI applications, since they still lack the ability to efficiently analyze data or integrate AI into instructional design [35,36].”
From Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis (2022) · cites this paper as [35]
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- 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.
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 2
- Pages
- 100008
- ISSN
- 2666-920X (ISSN-L)
- 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
- gold
- doi.org
- 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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › 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
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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.
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