Capacity building on AIED ethics covers 41% of studies, yet only four of 42 training studies assessed impact, and ethics training is largely absent¶
Claim · All claims
Evidence · 1 study · reviewr2·q3
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person · recheck by 2028: baselines and conditions change
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? Although 42 studies address AIED ethics capacity building, only four covered the impact assessment phase of these training programmes, and ethical training for AI implementation in education was found almost absent. → Ana Mouta 2023
Evidence¶
Ana Mouta 2023¶
Ana Mouta, Ana María Pinto-Llorente, Eva María Torrecilla-Sánchez. (2023). Uncovering Blind Spots in Education Ethics: Insights from a Systematic Literature Review on Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00384-9
q3 · i? · review · r2
RQ4 analysis of Table 8, covering 41% of the review's studies, finds ethics training targets future AI professionals, the general public, students, and teachers, but "only four of them covered the impact assessment phase". The review also reports Schiff's policy review found ethical training for AI in education almost absent.
"Although 42 studies were dedicated to the topic, only four of them covered the impact assessment phase of these training programmes."
Discussion¶
Related Claims¶
- Ethical reflection is often addressed separately from technical and creative AI activities, showing weak co-occurrence with Social Reasoning (42%), Using AI (41%) and Creating with AI (55%) — related
- Universities' GenAI policies widely embrace core ethical and governance principles but often overlook inclusion, equity, and sustainability issues such as internet access, gender parity in AI, and environmental impact — related
- GenAI raises privacy, transparency, bias and accuracy concerns in group work, though empirical evidence remains limited — related
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