القائمة
Search 2M+ education research records via the ERIC database API
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill eric-education-apiيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
استنادا إلى تصنيف SOC المهني
| name | eric-education-api |
| description | Search 2M+ education research records via the ERIC database API |
| metadata | {"openclaw":{"emoji":"🎓","category":"literature","subcategory":"search","keywords":["ERIC","education research","IES","pedagogy","teaching","educational technology"],"source":"https://eric.ed.gov/"}} |
ERIC is the world's largest digital library of education research, sponsored by the U.S. Institute of Education Sciences (IES). It indexes 2M+ records including journal articles, reports, conference papers, and dissertations covering all aspects of education. The API provides free, unauthenticated access to metadata and links to full text where available.
https://api.ies.ed.gov/eric/
# Basic keyword search
curl "https://api.ies.ed.gov/eric/?search=online+learning&format=json&rows=20"
# Search in specific fields
curl "https://api.ies.ed.gov/eric/?search=title:\"blended learning\"&format=json"
# Filter by publication date
curl "https://api.ies.ed.gov/eric/?search=STEM+education&start=0&rows=25&\
publicationdatestart=2023-01-01&publicationdateend=2026-12-31&format=json"
# Filter by publication type
curl "https://api.ies.ed.gov/eric/?search=formative+assessment&\
publicationtype=Journal+Articles&format=json"
# Filter by descriptor (ERIC thesaurus term)
curl
curl
| Parameter | Description | Example |
|---|---|---|
search | Free-text or field search | search=adaptive+learning |
format | Response format | json or xml |
rows | Results per page (max 200) | rows=50 |
start | Pagination offset | start=50 |
publicationtype | Document type | Journal Articles, Reports, Dissertations/Theses |
publicationdatestart | From date | 2024-01-01 |
publicationdateend | To date | 2026-12-31 |
peerreviewed | Peer-reviewed filter | true or false |
descriptor | ERIC thesaurus term | descriptor:"Distance Education" |
educationlevel | Education level | Higher Education, Elementary Education |
subject | Subject area | subject:"Mathematics Education" |
| Field | Description |
|---|---|
title | Article title |
author | Author name |
descriptor | ERIC controlled vocabulary term |
source | Journal/source name |
abstract | Abstract text |
id | ERIC document ID (e.g., EJ1234567) |
| Type | Description |
|---|---|
Journal Articles | Peer-reviewed journal articles |
Reports - Research | Research reports |
Reports - Descriptive | Descriptive reports |
Reports - Evaluative | Program evaluations |
Dissertations/Theses | Graduate research |
Speeches/Meeting Papers | Conference presentations |
Books | Books and book chapters |
{
"response": {
"numFound": 8450,
"start": 0,
"docs": [
{
"id": "EJ1389012",
"title": "Effects of AI Tutoring on Student Learning Outcomes",
"author": ["Smith, John", "Chen, Wei"],
"source": "Journal of Educational Technology",
"publicationdateyear": 2024,
"description": "This study examines the impact of AI-powered tutoring...",
"descriptor": ["Artificial Intelligence", "Tutoring", "Academic Achievement"],
"educationlevel": ["Higher Education"],
"peerreviewed": "T",
"url": "https://eric.ed.gov/?id=EJ1389012",
"publicationtype": "Journal Articles",
"issn": "1234-5678"
}
]
}
}
import requests
BASE_URL = "https://api.ies.ed.gov/eric/"
def search_eric(query: str, rows: int = 25,
peer_reviewed: bool = True,
pub_type: str = None,
from_year: int = None) -> list:
"""Search the ERIC education research database."""
params = {
"search": query,
"format": "json",
"rows": rows,
}
if peer_reviewed:
params["peerreviewed"] = "true"
if pub_type:
params["publicationtype"] = pub_type
if from_year:
params["publicationdatestart"] = f"{from_year}-01-01"
resp = requests.get(BASE_URL, params=params)
resp.raise_for_status()
data = resp.json()
results = []
for doc in data.get("response", {}).get("docs", []):
results.append({
"id": doc.get("id"),
"title": doc.get("title"),
"authors": doc.get("author", []),
"source": doc.get("source"),
"year": doc.get("publicationdateyear"),
"abstract": doc.get("description", "")[:300],
"descriptors": doc.get("descriptor", []),
"level": doc.get("educationlevel", []),
"url": doc.get("url"),
})
return results
def search_by_descriptor(descriptor: str, rows: int = 50) -> list:
"""Search using ERIC thesaurus controlled vocabulary."""
return search_eric(f'descriptor:"{descriptor}"', rows=rows)
# Example: find recent AI in education research
papers = search_eric("artificial intelligence classroom",
from_year=2023, rows=10)
for p in papers:
print(f"[{p['year']}] {p['title']}")
print(f" Descriptors: {', '.join(p['descriptors'][:5])}")
# Example: search by ERIC descriptor
papers = search_by_descriptor("Gamification")
for p in papers:
print(f"{p['id']}: {p['title']} — {p['source']}")
ERIC uses a controlled vocabulary of 12,000+ descriptors for consistent indexing. Key descriptors include:
| Descriptor | Coverage |
|---|---|
Distance Education | Online/remote learning |
Educational Technology | EdTech tools and methods |
Higher Education | University-level education |
STEM Education | Science, technology, engineering, math |
Teacher Education | Teacher training and development |
Assessment | Testing and evaluation |
Curriculum Development | Curriculum design |
Special Education | Inclusive education |