标准解读
Human oversight in automated education decisions
Explains the applicable evidential and assurance requirements in relation to human oversight in automated education decisions, covering scope, evidence, decision authority.
新闻与出版物
浏览国际教育质量认证委员会的新闻、标准更新和研究。
显示 12 条,共 1061 条结果
已发布 1061 篇文章
标准解读
Explains the applicable evidential and assurance requirements in relation to human oversight in automated education decisions, covering scope, evidence, decision authority.
数据与研究分析
Examines automated decision oversight, addressing access, participation and outcomes, source definitions, coverage, comparability, uncertainty and limits on inference.
质量改进方法
Sets out a structured improvement method as an evidence-led approach to micro-credential quality, covering responsibility, outcome evidence and sustained effect.
质量改进方法
Sets out a controlled approach to improving ownership and follow-through for data minimisation, covering diagnosis, responsible action, outcome evidence and sustained effect.
政策与监管分析
Examines automated decision oversight through institutional responsibility, clarifying legal effect, institutional responsibility, learner safeguards and public-interest risk.
数据与研究分析
Examines implementation of AI literacy obligations, addressing timeliness and data completeness and the evidential limits relevant to responsible interpretation.
数据与研究分析
Examines data minimisation, addressing longitudinal analysis, source definitions, coverage, comparability, uncertainty and limits on inference.
质量改进方法
Sets out a method for using internal evidence to strengthen teacher supply and retention, covering diagnosis, responsible action, outcome evidence.
标准解读
Explains the applicable evidential and assurance requirements in relation to micro-credential quality, covering scope, evidence, decision authority.
标准解读
Explains transparency requirements in relation to learner agency, covering scope, evidence, decision authority, material exceptions and continuing assurance.
政策与监管分析
Examines AI literacy obligations through expectations for education providers, clarifying legal effect, institutional responsibility, learner safeguards and public-interest risk.
数据与研究分析
Examines institutional AI policies, addressing trend analysis, source definitions, coverage, comparability, uncertainty and limits on inference.