AI 기반 GPT-4-Turbo 과제 채점 및 다중 형식 출력

• •HTTP Webhook 엔드포인트(n8n이 자동으로 생성)
• •OpenAI API Key

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              "value": "=Question 1: Explain Ohm's Law and its applications (10 marks)\nAnswer: Ohm's Law states V=IR where V is voltage, I is current, R is resistance. Applications include circuit design, electrical troubleshooting, power calculations.\n\nQuestion 2: Describe the working principle of a DC motor (15 marks)\nAnswer: DC motor converts electrical energy to mechanical energy using electromagnetic induction. Current through armature creates magnetic field that interacts with stator field causing rotation.\n\nQuestion 3: Calculate stress in a beam under load (20 marks)\nAnswer: Stress = Force/Area. For bending stress: σ = My/I where M is moment, y is distance from neutral axis, I is moment of inertia.\n\nQuestion 4: Explain thermodynamic cycles (15 marks)\nAnswer: Common cycles include Carnot, Otto, Diesel, Rankine. Each involves heat addition, expansion, heat rejection, compression stages for energy conversion.\n\nQuestion 5: Discuss Boolean algebra and logic gates (10 marks)\nAnswer: Boolean algebra uses AND, OR, NOT operations. Logic gates implement these: AND gate outputs 1 only when all inputs are 1, OR gate outputs 1 when any input is 1."
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        "jsCode": "const gradingResult = $input.first().json;\nconst studentName = $('Prepare Assignment Data').first().json.studentName;\nconst assignmentTitle = $('Prepare Assignment Data').first().json.assignmentTitle;\n\n// Create HTML table\nlet htmlTable = `\n<h2>Grading Report: ${assignmentTitle}</h2>\n<h3>Student: ${studentName}</h3>\n<table border=\"1\" cellpadding=\"10\" cellspacing=\"0\" style=\"border-collapse: collapse; width: 100%;\">\n  <thead>\n    <tr style=\"background-color: #4CAF50; color: white;\">\n      <th>Question</th>\n      <th>Max Marks</th>\n      <th>Marks Obtained</th>\n      <th>Feedback</th>\n    </tr>\n  </thead>\n  <tbody>\n`;\n\ngradingResult.questions.forEach(q => {\n  htmlTable += `\n    <tr>\n      <td>Question ${q.questionNumber}</td>\n      <td>${q.maxMarks}</td>\n      <td>${q.marksObtained}</td>\n      <td>${q.feedback}</td>\n    </tr>\n  `;\n});\n\nhtmlTable += `\n  </tbody>\n  <tfoot>\n    <tr style=\"background-color: #f2f2f2; font-weight: bold;\">\n      <td>TOTAL</td>\n      <td>${gradingResult.totalMarks}</td>\n      <td>${gradingResult.totalObtained}</td>\n      <td>Grade: ${gradingResult.grade} (${gradingResult.percentage.toFixed(2)}%)</td>\n    </tr>\n  </tfoot>\n</table>\n<p><strong>Overall Feedback:</strong> ${gradingResult.overallFeedback}</p>\n`;\n\n// Create CSV data\nlet csvData = \"Question,Max Marks,Marks Obtained,Feedback\\n\";\ngradingResult.questions.forEach(q => {\n  csvData += `\"Question ${q.questionNumber}\",${q.maxMarks},${q.marksObtained},\"${q.feedback.replace(/\"/g, '\"\"')}\"\\n`;\n});\ncsvData += `\"TOTAL\",${gradingResult.totalMarks},${gradingResult.totalObtained},\"Grade: ${gradingResult.grade} (${gradingResult.percentage.toFixed(2)}%)\"\\n`;\n\nreturn {\n  studentName,\n  assignmentTitle,\n  htmlTable,\n  csvData,\n  gradingResult,\n  summary: `${studentName} scored ${gradingResult.totalObtained}/${gradingResult.totalMarks} (${gradingResult.percentage.toFixed(2)}%) - Grade: ${gradingResult.grade}`\n};"
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        "content": "## Introduction\nAutomates AI-driven assignment grading with HTML and CSV output. Designed for educators evaluating submissions with consistent criteria and exportable results.\n## How It Works\nWebhook receives papers, extracts text, prepares data, loads answers, AI grades submissions, generates results table, converts to HTML/CSV, returns response.\n## Workflow Template\nWebhook → Extract Text → Prepare Data → Load Answer Script → AI Grade (OpenAI + Output Parser) → Generate Results Table → Convert to HTML + CSV → Format Response → Respond to Webhook\n## Workflow Steps\n1. Input & Preparation: Webhook receives paper, extracts text, prepares data, loads answer script.\n2. AI Grading: OpenAI evaluates against answer key, Output Parser formats scores and feedback.\n3. Output & Response: Generates results table, converts to HTML/CSV, returns multi-format response.\n## Setup Instructions\n1. Trigger & Processing: Configure webhook URL, set text extraction parameters.\n2. AI Configuration: Add OpenAI API key, customize grading prompts, define Output Parser JSON schema.\n"
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        "content": "## Prerequisites\n- OpenAI API key\n- Webhook platform\n- n8n instance\n## Use Cases\n- University exam grading\n- Corporate training assessments\n## Customization\n- Modify rubrics and criteria\n- Add PDF output\n- Integrate LMS (Canvas, Blackboard)\n## Benefits\n- Consistent AI grading\n- Multi-format exports\n- Reduces grading time by 90%"
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