Trustworthy artificial intelligence (AI) in education: Promises and challenges
Open accessAbstract
This paper was written to support the G20 artificial intelligence (AI) dialogue. With the rise of artificial intelligence (AI), education faces two challenges: reaping the benefits of AI to improve education processes, both in the classroom and at the system level; and preparing students for new skillsets for increasingly automated economies and societies. AI applications are often still nascent, but there are many examples of promising uses that foreshadow how AI might transform education. With regard to the classroom, this paper highlights how AI can accelerate personalised learning, the support of students with special needs. At the system level, promising uses include predictive analysis to reduce dropout, and assessing new skillsets. A new demand for complex skills that are less easy to automate (e.g. higher cognitive skills like creativity and critical thinking) is also the consequence of AI and digitalisation. Reaching the full potential of AI requires that stakeholders trust not only the technology, but also its use by humans. This raises new policy challenges around “trustworthy AI”, encompassing the privacy and security of data, but also possible wrongful uses of data leading to biases against individuals or groups.
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.
Integrity signals
Registry
Cites
Statistics
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.
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?
International organizations such as UNICEF and OECD argue for the need of transparency and explainability in AI to offer meaningful information to understand AI systems, user interactions and societal impacts.
“In our review, Schaper et al. (2020) reflected that international organizations such as UNICEF and OECD argue for the need of transparency and explainability in AI to offer meaningful information to understand AI systems, user interactions and societal impacts (Vincent-Lancrin & Van der Vlies, 2020; UNICEF, 2019).”
From Conceptualizing AI literacy: An exploratory review (2021) · cites this paper as Vincent-Lancrin & Van der Vlies, 2020
The “Transparency and explainability” section calls for meaningful information that helps people understand AI systems, recognize their interactions with them, and understand or challenge outcomes. It does not specifically frame that information as a way to understand broader societal impacts. UNICEF is separately cited in the sentence, so this verdict addresses only the OECD paper. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 8
- Drigas, A. and R. Ioannidou (2013), "A Review on Artificial Intelligence in Special Education", in Information Systems, E-learning, and Knowledge Management Research, Communications in Computer and Information Science, Springer Berlin Heidelberg, Berlin, Heidelberg, http://dx.doi.org/10.1007/978-3-642-35879-1_46. · DOI
- Elliott, S. (2017), Computers and the Future of Skill Demand, Educational Research and Innovation, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264284395-en. · DOI
- G20 (n.d.), G20 Ministerial Statement on Trade and Digital Economy, https://www.meti.go.jp/press/2019/06/20190610010/20190610010-1.pdf.
- OECD (2019), Artificial Intelligence in Society, OECD Publishing, Paris, https://dx.doi.org/10.1787/eedfee77-en. · DOI · Paper page
- OECD (2019), OECD Employment Outlook 2019: The Future of Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9ee00155-en. · DOI
- OECD (2019), OECD Skills Outlook 2019 : Thriving in a Digital World, OECD Publishing, Paris, https://dx.doi.org/10.1787/df80bc12-en. · DOI
- Vincent-Lancrin, S., C. González-Sancho, M. Bouckaert, F. de Luca, M. Fernández-Barrerra, G. Jacotin, J. Urgel and Q. Vidal (2019), Fostering Students' Creativity and Critical Thinking: What it Means in School, Educational Research and Innovation, OECD Publishing, Paris, https://dx.doi.org/10.1787/62212c37-en. · DOI
- Vincent-Lancrin, S., J. Urgel, S. Kar, and G. Jacotin (2019), Measuring Innovation in Education 2019: What Has Changed in the Classroom?, Educational Research and Innovation, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264311671-en. · DOI
Cited by
Sorted by citations
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · see citing works
Revolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975 · see citing works
“AI introduces adaptive teaching strategies (Aldeman et al., 2021) because it enhances teachers ’ insight into students ’ learning processes and presents ways to support their learners (Jia et al., 2021; Vincent-Lancrin & Van der Vlies, 2020).”
BackgroundA Systematic Review on AI-based Proctoring Systems: Past, Present and Future
Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10597-x · Cited by 267 · see citing works
Design implications of generative AI systems for visual storytelling for young learners
Han, A., & Cai, Z. (2023) · Proceedings of the 22nd Annual ACM Interaction Design and Children Conference · doi:10.1145/3585088.3593867 · Cited by 127 · see citing works
Wang, F., King, R. B., Chai, C. S., & Zhou, Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00417-2 · Cited by 124 · see citing works
Cao, H. J., Choi, K., Park, C., & Lee, H. R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713173 · Cited by 9 · see citing works
- Authors
- Vincent‐Lancrin, S., & van der Vlies, R.
- Affiliations
- Organisation de Coopération et de Développement Economiques (FR)
- Venue
- OECD Education Working Papers
- Published
- 2020-04-08
- ISSN
- 1993-9019 (ISSN-L)
- OpenAlex
- W3016229568
- Cited by
- 135
- References
- 0
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 26 · 2023: 27 · 2022: 13 · 2021: 14
- Type
- report
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- OECD education working papers (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- trustworthy AI, artificial intelligence in education, personalized learning, special needs students, student dropout prediction, skill assessment, critical and creative thinking, data privacy and security, algorithmic bias, AI policy, digitalization of education
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