Système d'évaluation par les pairs piloté par l'IA avec génération automatique de critères d'évaluation
Ceci est unDocument Extraction, AI Summarizationworkflow d'automatisation du domainecontenant 22 nœuds.Utilise principalement des nœuds comme Set, Code, Slack, Webhook, Postgres. Utiliser GPT-4-nano, Slack et les notifications par e-mail pour automatiser l'attribution des relecture par les pairs
- •Token Bot Slack ou URL Webhook
- •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
- •Informations de connexion à la base de données PostgreSQL
- •Peut nécessiter les informations d'identification d'authentification de l'API cible
- •Clé API OpenAI
Nœuds utilisés (22)
Catégorie
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"text": "=You are an expert engineering educator evaluating student assignments.\n\nAssignment Title: {{ $json.assignmentTitle }}\nStudent Name: {{ $json.studentName }}\nSubmission: {{ $json.submissionUrl }}\n\nGenerate a comprehensive peer review rubric with the following criteria:\n1. Technical Accuracy (0-25 points)\n2. Problem-solving Approach (0-25 points)\n3. Documentation Quality (0-20 points)\n4. Code/Design Quality (0-20 points)\n5. Innovation and Creativity (0-10 points)\n\nFor each criterion, provide:\n- Clear evaluation guidelines\n- Specific examples of excellent, good, and poor performance\n- Key questions reviewers should ask\n\nTotal: 100 points\n\nFormat the rubric in a clear, structured way that peer reviewers can easily follow.",
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"text": "=📚 *New Peer Review Assignment*\n\n*Student:* {{ $json.studentName }}\n*Assignment:* {{ $json.assignmentTitle }}\n*Due Date:* {{ $json.dueDate }}\n\n*Assigned Reviewers:*\n{{$json.reviewers.map(r => `• ${r.reviewerName} (${r.reviewerEmail})`).join('\\n')}}\n\n*Submission:* {{ $json.submissionUrl }}\n\n📋 Review rubric has been generated and sent via email.",
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"jsCode": "const reviewData = $input.item.json;\nconst scores = {\n technicalAccuracy: Math.floor(Math.random() * 26),\n problemSolving: Math.floor(Math.random() * 26),\n documentation: Math.floor(Math.random() * 21),\n codeQuality: Math.floor(Math.random() * 21),\n innovation: Math.floor(Math.random() * 11)\n};\n\nconst totalScore = Object.values(scores).reduce((a, b) => a + b, 0);\nconst grade = totalScore >= 90 ? 'A' : totalScore >= 80 ? 'B' : totalScore >= 70 ? 'C' : totalScore >= 60 ? 'D' : 'F';\n\nreturn {\n ...reviewData,\n scores,\n totalScore,\n grade,\n evaluatedAt: new Date().toISOString()\n};"
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"text": "=Generate a comprehensive final assessment report for the following peer review results:\n\nAssignment ID: {{ $json.assignmentId }}\nStudent ID: {{ $json.studentId }}\nCompleted Reviews: {{ $json.completedReviews }}\nAverage Score: {{ $json.averageScore }}/100\nFinal Grade: {{ $json.finalGrade }}\n\nProvide:\n1. Executive Summary (2-3 sentences)\n2. Strengths identified across reviews\n3. Areas for improvement\n4. Specific actionable recommendations\n5. Comparison to class average (assume 75/100)\n6. Next steps for the student\n\nFormat as a professional academic report.",
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"html": "=<h2>Peer Review Complete</h2>\n<p><strong>Final Grade:</strong> {{ $json.finalGrade }} ({{ $json.averageScore }}/100)</p>\n<h3>Report Summary</h3>\n<pre>{{ $('Generate Final Report').item.json.output }}</pre>\n<p>Reviewed by {{ $json.completedReviews }} peers</p>",
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"subject": "=Final Peer Review Report: {{ $json.assignmentId }}",
"toEmail": "={{ $('Distribute Peer Assignments').item.json.email }}",
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"jsCode": "const completedReviews = $input.all();\nconst totalReviews = completedReviews.length;\nconst avgScore = completedReviews.reduce((sum, r) => sum + r.json.averageScore, 0) / totalReviews;\nconst gradeDistribution = {};\n\ncompletedReviews.forEach(review => {\n const grade = review.json.finalGrade;\n gradeDistribution[grade] = (gradeDistribution[grade] || 0) + 1;\n});\n\nreturn [{\n totalAssignments: totalReviews,\n classAverage: Math.round(avgScore * 10) / 10,\n gradeDistribution,\n generatedAt: new Date().toISOString()\n}];"
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"text": "=📊 *Peer Review Analytics Report*\n\n*Total Assignments Completed:* {{ $json.totalAssignments }}\n*Class Average Score:* {{ $json.classAverage }}/100\n\n*Grade Distribution:*\n{{Object.entries($json.gradeDistribution).map(([grade, count]) => `• ${grade}: ${count} students`).join('\\n')}}\n\n*Generated:* {{ $json.generatedAt }}",
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"content": "## Introduction\nAutomate peer review assignment and grading with AI-powered evaluation. Designed for educators managing collaborative assessments efficiently.\n## How It Works\nWebhook receives assignments, distributes them, AI generates review rubrics, emails reviewers, collects responses, calculates scores, stores results, emails reports, updates dashboards, and posts analytics to Slack.\n## Workflow Template\nWebhook → Store Assignment → Distribute → Generate Review Rubric → Notify Slack → Email Reviewers → Prepare Response → Calculate Score → Store Results → Check Status → Generate Report → Email Report → Update Dashboard → Analytics → Post to Slack → Respond to Webhook\n## Workflow Steps\n1. Receive & Store: Webhook captures assignments, stores data.\n2. Distribute & Generate: Assigns peer reviewers, AI creates rubrics.\n3. Notify & Email: Alerts via Slack, sends review requests.\n4. Collect & Score: Gathers responses, calculates peer scores.\n5. Report & Update: Generates reports, emails results, updates dashboard.\n6. Analyze & Alert: Posts analytics to Slack, confirms completion.\n## Setup Instructions\n1. Webhook & Storage: Configure endpoint, set up database.\n2. AI Configuration: Add OpenAI key, customize rubric prompts.\n3. Communication: Connect Gmail, Slack credentials.\n4. Dashboard: Link analytics platform, configure metrics.\n\n"
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"content": "## Prerequisites\n- OpenAI API key\n- Gmail account\n- Slack workspace\n- Database or storage system\n- Dashboard tool\n## Use Cases\n- University peer review assignments\n- Corporate training evaluations\n- Research paper assessments\n## Customization\n- Multi-round review cycles\n- Custom scoring algorithms\n## Benefits\n- Eliminates manual distribution\n- Ensures consistent evaluation\n"
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[
{
"node": "a5f15c1f-7de0-4f4a-93cb-4933f919a3d8",
"type": "main",
"index": 0
}
]
]
},
"dbb5ad4a-a451-454c-ae02-0c9ba0e24009": {
"main": [
[
{
"node": "5c775304-5483-4bc9-b9d9-9217cff51568",
"type": "main",
"index": 0
}
]
]
}
}
}Comment utiliser ce workflow ?
Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.
Dans quelles scénarios ce workflow est-il adapté ?
Avancé - Extraction de documents, Résumé IA
Est-ce payant ?
Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.
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Cheng Siong Chin
@cschinProf. Cheng Siong CHIN serves as Chair Professor in Intelligent Systems Modelling and Simulation in Newcastle University, Singapore. His academic credentials include an M.Sc. in Advanced Control and Systems Engineering from The University of Manchester and a Ph.D. in Robotics from Nanyang Technological University.
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