Respondents identified algorithmic bias (31.9%), data privacy (27.5%), and technical reliability (27.5%) as the primary risks of AI in education¶
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Evidence · 1 study · qualitativer2·q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person · recheck by 2029: 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.
q2 i? Algorithmic bias was the most commonly identified risk (31.9%), followed by data privacy (27.5%) and technical reliability (27.5%). → rfi-risks
Evidence¶
rfi-risks¶
Noakes, S., Shell, A., Van Nostrand, P., Murillo, A., Ruiz, P., and Liberman, B. (2024, September). AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings. Digital Promise. https://doi.org/10.51388/20.500.12265/229
q2 · i? · qualitative · r2
Thematic coding of risks across RFI submissions (N=185 tagged responses, per Figure 7). The report reports the three leading risks with these percentages; smaller categories were emerging-technology challenges (6.0%), barriers to use (4.9%), and lack of human connection (2.2%).
"Respondents most commonly identified algorithmic bias (31.9%) as a risk, followed by data privacy (27.5%) and technical reliability (27.5%)."
Discussion¶
Related Claims¶
- Data privacy mitigations centered on data governance and monitoring (68.9%), while technical reliability mitigations centered on human involvement (36.9%) — related
- Respondents proposed testing and monitoring (61.9%) and human involvement (22.7%) as leading mitigations for algorithmic bias — related
- Positioning personal experience as an epistemic resource supports expansion from inward to outward critical orientations — related
- Ethical and governance dimensions of AI were less prominent in teachers' narratives than pedagogical opportunities — reports the opposite
- The most reported risks of AI tools in science and chemistry education are ethical issues, including gender and racial bias, hallucinations, copyright infringement, and plagiarism — related
- Faculty members and students identify both user-related and algorithm-related ethical risks in AI use in education — related
- Educator- and institutional-level challenges include curricular misalignment, need for teacher mediation, unequal access, privacy concerns, academic integrity risks, and algorithmic bias — related
- Algorithmic bias and data privacy are central ethical risks of AI in adult education — related
- Reviewed studies identify data privacy, algorithmic bias, unequal access, and epistemic trust as central ethical concerns in educational AI — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
rfi-ai-risks-bias-privacy-reliability |
title |
Respondents identified algorithmic bias (31.9%), data privacy (27.5%), and technical reliability (27.5%) as the primary risks of AI in education |
description |
Respondents identified algorithmic bias (31.9%), data privacy (27.5%), and technical reliability (27.5%) as the primary risks of AI in education |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-09 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — rfi-risks |
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