Review reports ethical concerns of educational AI including algorithmic bias and exploitation of personal data¶
Claim · All claims
Evidence · 1 study · reviewr2·q2
Currency · newest study 2025 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
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? The C1 ethical concerns theme reports predicted issues such as algorithmic bias and exploitation of personal data, with calls for frameworks ensuring responsible AI design in education. → Adam Palmquist 2025
Evidence¶
Adam Palmquist 2025¶
Adam Palmquist, Helga Dis Isfold Sigurdardottir, Hege Myhre. (2025). Exploring interfaces and implications for integrating social-emotional competencies into AI literacy for education: a narrative review. Journal of Computers in Education. https://doi.org/10.1007/s40692-025-00354-1
q2 · i? · review · r2
The review's C1 'Social and emotional implications' sub-theme, attributing the prediction to Chaudhry and Kazim (2022), who propose frameworks focused on preventing discrimination and safeguarding student rights to disengage from automated systems. No effect sizes are printed.
"Through their survey of AI applications in the educational domain, they predict numerous issues and concerns that need to be addressed, such as algorithmic bias and the exploitation of personal data."
Discussion¶
Related Claims¶
- 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
- Human-AI relationship concerns persist across all CAI generations while academic integrity and data privacy are emerging ethical concerns — related
- Reviewed studies identify data privacy, algorithmic bias, unequal access, and epistemic trust as central ethical concerns in educational AI — possibly the same claim (merge candidate)
- Ethical and governance dimensions of AI were less prominent in teachers' narratives than pedagogical opportunities — related
- Algorithmic bias and data privacy are central ethical risks of AI in adult education — related
- Educators' use of AI in PBL is accompanied by persistent ethical concerns about data privacy, algorithmic bias, and educator autonomy — related
- Privacy protections lag behind reputational concerns: institutions rarely consider implications of capturing audio-visual and behavioral data from private spaces, and AI algorithms are seldom transparent about error rates — related
- Ethical concerns (bias, transparency, privacy, authorship, accountability) emerge as cross-cutting dimensions of GenAI-specific TPK across studies of teacher educators — related
- When students raised ethics in essays, concerns concerned authorship and integrity rather than privacy or data — related
- Respondents proposed testing and monitoring (61.9%) and human involvement (22.7%) as leading mitigations for algorithmic bias — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ethical-concerns-algorithmic-bias-data |
title |
Review reports ethical concerns of educational AI including algorithmic bias and exploitation of personal data |
description |
Review reports ethical concerns of educational AI including algorithmic bias and exploitation of personal data |
status |
review |
evidence_strength |
weak |
newest_evidence |
2025 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — adam-palmquist-2025 |
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