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Artificial intelligence now grades essays, flags plagiarism, and predicts course completion. Its speed and consistency tempt schools to outsource judgment. Yet the very act of measurement in education is value‑laden: what counts as “good writing,” “originality,” or “mastery” is contested and context‑dependent. When models optimize for a fixed metric, they risk mistaking the map for the territory and shifting focus toward what is easily scored rather than what is meaningfully learned.
Fairness is the first fault line. Training data may encode historical inequities—whose voices got published, which dialects were praised—and models inherit these skews. Systems often rely on proxies for constructs that are hard to measure (for example, “argument quality” via lexical richness), and Goodhart’s law warns that once a proxy becomes a target, it stops being a good proxy. Calibrating fairness is not straightforward: demographic parity can reduce disparate outcomes but may undermine validity; equalized odds balances error rates across groups but can lower overall accuracy. These trade‑offs must be acknowledged, not hidden behind glossy dashboards.
Transparency is the second challenge. Rubrics can be codified, but large models remain partly opaque. Students and teachers deserve contestability: a clear pathway to question scores, see the evidence the model used, and request human review. Audit trails, error bounds, and model cards that disclose training sources and known limitations help restore trust. A human‑in‑the‑loop—someone empowered to override outputs—should be the default for high‑stakes decisions.
Privacy concerns run alongside performance. Tools that log keystrokes or analyze speech may create a chilling effect, discouraging risk‑taking and authentic voice. Minimization (collect only what is necessary), on‑device inference or federated learning, strict retention limits, and opt‑outs for sensitive uses should be standard. Students should know what data is collected, how it is used, and how to delete it.
Finally, values must drive design. AI assessment shines when supporting formative feedback—timely, actionable guidance that helps revision—rather than replacing summative judgment. If systems narrow outcomes to what machines detect, curricula will drift toward easily measured skills and away from creativity, collaboration, and ethical reasoning. Schools should treat AI as a co‑pilot that scaffolds learning, not a judge that defines it. This requires investing in teacher capacity, engaging students and families in governance, and regularly auditing impacts against educational goals.
The central question is not whether AI can grade, but under what conditions it ought to. Ethical assessment is a balancing act: validity and fairness, transparency and privacy, efficiency and human dignity. Designing for contestability, restraint, and alignment with pedagogical aims keeps technology in service of education, rather than the other way around.
理解问题
词汇理解题
- In the article, what does the word “proxy” most nearly mean in context?
- What is meant by “opaque” when describing large models?
- What does “contestability” imply in the context of AI assessment?
细节理解题
- Which two fairness approaches are explicitly mentioned, and what trade‑offs do they involve?
- According to the text, what safeguards are recommended to enhance transparency and trust?
- What privacy measures are suggested to mitigate a chilling effect on students?
推理判断题
- Based on the passage, why does the author prefer AI for formative feedback rather than summative judgment?
- What can be inferred about the likely impact on curricula if AI metrics dominate assessment design?
主旨归纳题
- Which statement best captures the main idea of the article?
A. AI assessment will inevitably replace teachers due to its efficiency.
B. Ethical AI in education requires balancing validity, fairness, transparency, and privacy, with human oversight and pedagogical alignment.
C. Bias in AI is unsolvable, so schools should avoid AI entirely.
D. The primary benefit of AI lies in detecting plagiarism more accurately than humans.
答案解析
词汇理解题
- 正确答案: A proxy is a substitute measure standing in for a complex construct.
- 思路: Identify the sentence defining usage: “Systems often rely on proxies for constructs that are hard to measure...”
- 原文定位: “Systems often rely on proxies for constructs that are hard to measure (for example, ‘argument quality’ via lexical richness).”
- 易错点: 不要将 proxy 理解为“代表人”或“中介人”；此处强调“替代指标”。
- 正确答案: Opaque means not fully transparent or understandable in how it reaches conclusions.
- 思路: Look for context about model explainability.
- 原文定位: “Rubrics can be codified, but large models remain partly opaque.”
- 易错点: 误解为“低质量”或“无效”；这里强调“难以解释”。
- 正确答案: Contestability refers to the ability and process to challenge, review, and overturn AI decisions.
- 思路: Note paired elements: pathway to question scores, evidence, human review.
- 原文定位: “Students and teachers deserve contestability: a clear pathway to question scores, see the evidence the model used, and request human review.”
- 易错点: 不要将其等同于“竞争性”；这里是“可申诉性”。
细节理解题
- 正确答案: Demographic parity and equalized odds; parity may undermine validity, equalized odds can lower overall accuracy.
- 思路: Scan fairness paragraph for named criteria and trade‑offs.
- 原文定位: “Demographic parity... may undermine validity; equalized odds... can lower overall accuracy.”
- 易错点: 混淆两者目标：一个关注结果分布，一个关注错误率。
- 正确答案: Audit trails, error bounds, model cards disclosing training sources and limitations, and human‑in‑the‑loop review.
- 思路: List transparency tools mentioned.
- 原文定位: “Audit trails, error bounds, and model cards... A human‑in‑the‑loop...”
- 易错点: 将“rubrics”误认为透明度保障；它们可编码但非充分保障。
- 正确答案: Data minimization, on‑device inference or federated learning, strict retention limits, and opt‑outs for sensitive uses; clear notices and deletion rights.
- 思路: Identify privacy practices enumerated.
- 原文定位: “Minimization... on‑device inference or federated learning, strict retention limits, and opt‑outs... Students should know what data is collected... and how to delete it.”
- 易错点: 只关注技术手段而忽略权利（知情、删除）。
推理判断题
- 正确答案: Formative feedback aligns with learning support without conferring high‑stakes judgments; AI can provide timely guidance while humans retain final evaluation authority.
- 思路: Synthesize statements about formative vs summative roles.
- 原文定位: “AI assessment shines when supporting formative feedback... rather than replacing summative judgment.”
- 易错点: 认为作者反对任何 AI 评分；作者强调“条件与用途”。
- 正确答案: Curricula would likely narrow to skills that are easily machine‑measurable, reducing emphasis on creativity, collaboration, and ethical reasoning.
- 思路: Note causal link between metrics dominance and curriculum drift.
- 原文定位: “If systems narrow outcomes to what machines detect, curricula will drift toward easily measured skills and away from creativity, collaboration, and ethical reasoning.”
- 易错点: 只看到效率提升，忽视价值偏移。
主旨归纳题
- 正确答案: B
- 思路: Weigh each option against article’s emphasis on balance, oversight, and alignment with educational aims.
- 原文定位: 尾段总括: “Ethical assessment is a balancing act... Designing for contestability, restraint, and alignment with pedagogical aims...”
- 易错点: A 和 D 过于片面；C 过度否定。
阅读技巧总结
- 识别价值词与技术词的并置：遇到 fairness、validity、privacy 等词时，关注作者如何呈现权衡与取舍。
- 处理抽象概念的策略：遇到 proxy、contestability、opaque 等抽象词，先在原文中定位定义性句或例子，再归纳含义。
- 细节定位方法：使用段落主题句和并列结构（如列举措施：audit trails, error bounds, model cards）快速提取答案。
- 推理题方法：从因果线索和“风险—建议”框架中推断作者立场与潜在影响。
- 主旨归纳：优先选择能覆盖全文多个维度（公平、透明、隐私、教学目标）的选项，而非单点观点。
训练效果评估与后续建议
- 本次表现若能准确定位列举项与权衡关系，说明细节把握能力较好；若在词义题上出现混淆（如将 contestability 理解为竞争性），需加强语境释义训练。
- 建议后续练习增加对学术报告或政策指南的阅读，强化对术语与框架（如不同公平指标）之间差异的理解。
- 可进一步训练：用三句话总结每段的“问题—风险—对策”，提升主旨整合与推理能力。
