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TRUVACE RECORD VERSION
record: TRV-2026-0401
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T10:28:31.267821Z
status: published
lens: p_space
sector: education
headline: Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners
dek: Abstract One trending theme within research on learning and teaching is an emphasis on artificial intelligence (AI). While AI offers opportunities in the educational arena, blindly replacing human involvement is not the answer. Instead, current research suggests that the key lies in harnessing the strengths of both humans and AI to create a more effective and beneficial learning and teaching experience. Thus, the importance of ‘humans in the loop’ is becoming a central tenet of educational AI. As AI technology a…
gain_title: (none)
problem_title: Deploying AI in education faces key challenges of ensuring privacy and ethical use, trustworthy algorithms, and equity and fairness.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Deploying AI in education faces key challenges of ensuring privacy and ethical use, trustworthy algorithms, and equity and fairness.
problem_evidence: privacy and ethical use of AI | importance of trustworthy algorithms | equity and fairness
quick_read: Published June 4, 2024, this peer-reviewed paper examined AI in education through a Delphi study of 33 international professionals plus follow-up face-to-face discussions with international researchers. It found that effective use depends on keeping humans in the loop rather than blindly replacing human involvement.

The work matters because it reframes AI adoption in schools around governance risks that affect students and teachers directly, not just technical performance. What remains uncertain is how the identified priorities of privacy, algorithmic trustworthiness, and equity translate into enforceable policies and measurable classroom outcomes across diverse systems.
limitation: Findings rest on a small Delphi panel of 33 international professionals and subsequent face-to-face discussions, limiting generalizability.
tag: Evidence-backed problem
key_points: Delphi study surveyed N = 33 international professionals followed by in-depth face-to-face discussions with international researchers. | Three most important trends identified were privacy and ethical use of AI, importance of trustworthy algorithms, and equity and fairness. | Same three items were also identified as the three key challenges for deploying AI in education. | Paper outlines policy recommendations and a research agenda based on findings.
rundown: The authors conducted a Delphi study with a survey of 33 international professionals and then held in-depth face-to-face discussions with a panel of international researchers to surface trends and challenges.

Results converged on three priorities that were both most important and most challenging: privacy and ethical use, trustworthy algorithms, and equity and fairness, leading to policy recommendations and a proposed research agenda.
sources:
- peer_reviewed | Technology, Knowledge and Learning | https://doi.org/10.1007/s10758-024-09747-0 | 2024-06-04
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