Teaching staff distrusted third parties in LA on privacy and ethics, centring on data sharing and ownership¶
Claim · All claims
Evidence · 1 study · qualitativer3·q1
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
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q1 i? Interviews identified data sharing and data ownership as the two concerns underlying teaching staff distrust of third parties handling LA, including worries about personal data reaching advertising companies and data being sent overseas. → Asma Shannan Alzahrani 2023
Evidence¶
Asma Shannan Alzahrani 2023¶
Asma Shannan Alzahrani, Yi-Shan Tsai, Naif Aljohani, Emma Whitelock-wainwright, Dragan Gasevic. (2023). Do teaching staff trust stakeholders and tools in learning analytics? A mixed methods study. Education Tech Research Dev. https://doi.org/10.1007/s11423-023-10229-w
q1 · i? · qualitative · r3
Thematic analysis of 24 interviews. Participants consented to sharing educational data but not personally identifiable data; one participant worried student data "with names, mobile numbers and emails" could be sold to advertising companies, and another about "sending educational data out of Saudi Arabia".
"In terms of trust in third parties, two concerns were identified in teaching staff interviews, including data sharing and ownership."
Discussion¶
Related Claims¶
- Teaching staff distrusted LA tools' data accuracy and prediction precision despite trusting their usefulness — related
- Community adults entering an AI education session held broad, generalized concerns about AI, including distrust of self-regulation and privacy worries — related
- Adult learners raised data-practice concerns that did not map onto the Community of Inquiry framework, including not knowing what data is collected and where it goes — related
- Faculty members and students identify both user-related and algorithm-related ethical risks in AI use in education — related
- Universities converge on privacy-oriented data guardrails, warning against entering sensitive data into consumer AI tools and encouraging institutionally approved services — related
- Senior manager buy-in, teaching staff buy-in, legal framework, privacy protection, and ethics guidelines were rated critically important to achieving LA potential — related
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by process:wiki-ingest on 2026-10-28 |
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