Med Education

Education News Reporting

This skill specializes med-news-reporter for the education beat. Read med-news-reporter first for the general 6-step workflow (type selection, material audit, fact-check, balance, ethics, literacy). This file adds education-beat-specific discipline on top: research evidence auditing, demographic integrity, effect-size verification, and education-law red lines (兒少法, teacher privacy, curriculum interpretation).

Overview

Distilled from education-journalism curricula at Spencer Foundation, Education Writers Association (EWA), Columbia Journalism School (Education Track), and Taiwan educational institutions (師大新聞系 education track, NTU journalism education reporting). Covers four main education-news sub-types: policy reform / research findings / campus events / student data.

IRON LAW: Effect Size + Population, Not Just "Research Shows"

Education research is widely sensationalized into "study finds X improves Y by Z%".
The LLM tendency is to lead with the headline effect and skip the methodology footer.
Instead: always report (a) effect size (Cohen's d, NNT, % point change), (b) sample size
and demographic (N=500 Taiwan Grade 4, etc.), (c) replication status (single study vs
meta-analysis vs unpublished), (d) source funding (ministry, private foundation, etc.).

This is not optional. A study with d=0.08 is "statistically significant" but educationally
meaningless; a study of 35 suburban Grade 5 students cannot generalize to national policy.
Readers must have this context to judge whether the news is real improvement or noise.

Default LLM failure mode: "A new study shows bilingual education boosts test scores by 12%"
(leading effect, no Cohen's d, no sample demographic, no replication context).

Correct: "A 2024 study of 240 Grade 4 students in Taipei bilingual programs found a 0.6
standard-deviation improvement in reading (Cohen's d=0.6), sustained in a follow-up cohort
but not replicated in rural schools. The National Taiwan University research was funded by
the Language Ministry. Previous international meta-analyses show effect sizes ranging d=0.2
to d=0.5 depending on classroom intensity."

Why this is non-obvious: the headline % is true, the study is real, the writing flows naturally — but the reader cannot judge whether the news is a meaningful education breakthrough or a statistically-significant artifact of a small, unrepresentative sample. This is how education policy gets made on bad evidence.

Rationalization Table — these justifications DO NOT override the Iron Law:

When to Use

Trigger conditions:

• User supplies education material — 教育部新聞、校園事件、教育研究論文摘要、課程改革公告、升學統計、教師訪談、學生表現數據 — and asks for a news piece.
• User asks for "教育新聞" / "校園報導" / "教育研究新聞" / "education policy story" / "campus event coverage" / "student achievement news".
• User paraphrases: "寫一篇教育政策新聞", "整理校園事件成報導", "幫我解讀這份 PISA 排名", "draft an article on this research finding".

Input signals:

• Named schools, students (or deidentified cohorts), educators, education institutions, student data, curriculum changes, test scores, research findings.
• Education-specific terminology: 教育部、國教院、頂大、全教總、會考、學測、108 課綱、升學率、GPA、PISA、effect size.

When NOT to use:

• School / education institution press release in the institution's own voice → use pr-press-release.
• Teacher training / pedagogy guidance / school-internal communication → use tech-teaching or domain-specific skill.
• Student recruitment / marketing ("discover our innovative bilingual program") → use mkt-*.
• Pure curriculum design or lesson-planning → out of scope for journalism.

Methodology

Step 0: Defer general workflow to med-news-reporter

Read or have already loaded med-news-reporter for: material audit, fact-checking, source-strength tagging, balance principle, media-ethics check, media-literacy self-check. Do not re-implement those steps here. This file specializes Steps 1–3, adds education-specific Step 3.5 (Research Evidence Audit), and modifies Step 4 (ethics) to include education-specific red lines.

Step 1: Classify the education-story sub-type

If ambiguous, ask the user — do not guess.

Step 2: Source vetting & demographic tagging

Every education claim involving data or outcomes must carry demographic context at first mention:

• ❌ Vague: 「研究顯示,雙語教育提升閱讀成績。」
• ✅ Specific: 「一項由台灣大學 2024 年進行的研究,針對 240 位台北市雙語班四年級學生,發現閱讀成績提升 0.6 個標準差(Cohen's d=0.6)。」
• ✅ With limitation: 「該研究樣本侷限於市區雙語班;在農村地區試行時效果未再現。」

Education source tier tagging (extends med-news-reporter):

Step 3: Education-specific risk check

Beyond med-news-reporter's general ethics check, add:

1. 兒少法 §69 — do NOT disclose name + school + identifying details of minors:

   • ❌ "12 歲的王小華就讀範例國中"
   • ✅ "一位 12 歲女學生" / "示範國中一名四年級男童"
   • Even with parental consent, err toward deidentification.

2. 升學率 / 錄取率口徑混淆:

   • "升學率"(進入高等教育比例) ≠ "錄取率"(申請者中被錄取比例) ≠ "申請成功率"(報考人數 ÷ 錄取人數)
   • Always cite official source (聯招中心、教育部) and definition. If ambiguous in source, flag as [待查證: 升學率定義].

3. 研究結果過度延伸(Goodhart's Law):

   • "One school improved writing with Method X" does not support "Method X should be national policy".
   • "PISA ranking rose 1 position" does not support "reform worked" (within error margin; trends matter more than rank).
   • Mark speculative cause-effect as conditional: "如果..." / "可能" / "若要推廣至全國,須進一步驗證".

4. 教師受訪許可:

   • Teachers employed by schools usually need school approval to speak publicly.
   • 「受訪時曾言及」need confirmation: did the school grant permission? Mark risk if unclear.

5. 校園隱私與實驗倫理:

   • Classroom data (test scores, attendance, behavioral notes) may contain PII.
   • Educational research on students requires IRB approval or school consent + parental consent.
   • If data source unclear, ask before citing.

Step 3.5: Research Evidence Audit (education-specific addition)

For every research-based claim, extract and verify:

1. Effect size: Convert headline claim to Cohen's d, odds ratio, percentage point change, or NNT (number needed to treat).

   • If not in abstract, read Methods + Results. If absent, flag as [待查證: 效果量統計值].
   • Benchmark: education interventions with d < 0.2 are weak; d > 0.5 are strong.

2. Sample size and demographic:

   • Who? (Grade level, school type, region, SES if known, language background for language research).
   • How many? (N = total sample size).
   • If N < 50 or sample is non-representative (private urban school, bilingual cohort only), note as "preliminary evidence" or "context-specific".

3. Replication and consistency:

   • Is this a single published study, a pre-print, a meta-analysis, or a series of replications?
   • If single study: "a study found" not "studies show".
   • If meta-analysis: report heterogeneity (I²), range of effect sizes across studies, and which studies drove the mean.

4. Funding and conflict of interest:

   • Who paid for the research? (Ministry, private foundation, school, textbook company?).
   • Relevant conflict (e.g., a bilingual-program-company-funded study of bilingual-program effectiveness has incentive skew).
   • Disclose in piece: "The research was funded by [source]" or include in demographic tag.

5. Publication status:

   • Peer-reviewed journal > preprint > press release > blog.
   • If data is unpublished or under review, note explicitly: "preliminary findings" / "not yet peer-reviewed".