Grant Proposal Review PRO V2.0
Grant proposals: 12-step, 9D QC, dual-core
Description
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
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View all
HE Document Write&Review v3.0
🎯 Got your application rejected? Can't find the highlight for your proposal? Don't know where to start with review comments? Three real scenarios: 🔸 Scenario 1: Application season anxiety—Reviewer feedback: "Insufficient theoretical support, vague policy basis." Unsure which documents to cite or which theoretical framework to use. 🔸 Scenario 2: Proposal writing dilemma—In charge of course construction plan: objectives, tasks, pathways, evaluation… each part needs writing, but you feel the logic is not rigorous enough and worry about being questioned on "feasibility" during review. 🔸 Scenario 3: Review dilemma—Need to write peer review comments: must point out issues while maintaining professionalism, be well-founded but not too harsh. How to strike the balance? 💡 What can this system do for you? Not just give advice—it writes, revises, and reviews for you directly. 📝 Writing Mode: From topic to final document Enter your topic and existing materials. The system automatically identifies the document type (application/proposal/report/review). Automatically matches authoritative policy documents and theoretical support. Generates content chapter by chapter, each with evidence, logic, and facts. Key promise: Never fabricates data; clearly tells you what's missing. 🔍 Review Mode: Expert-level diagnosis Upload your text. Professional scoring across 7 dimensions (value, alignment, completeness, innovation, feasibility, support, expression quality). Precisely identifies problem areas. Provides specific revision suggestions + example rewrites. Not general advice, but paragraph-level specific guidance. ✏️ Revision Optimization Mode: Precision enhancement Strengthens arguments based on existing text. Optimizes expression, eliminates empty talk and clichés. Standardizes terminology and logic. Improves overall competitiveness. ⚡ Three Core Mechanisms (Unique) 🛡️ Firewall Mechanism Built-in "fact boundary": User-provided real data is never fabricated; policy basis must have sources; theoretical support cannot be misapplied. Every sentence you see can be traced back to its source. 🔄 Multi-core Adversarial Engine One core writes, another specifically checks for errors. Like having a strict auditor watching, ensuring no "unsubstantiated facts," "logic gaps," or "policy mismatches" occur. 📊 Stepwise Guidance Doesn't ask you 20 questions at once—identifies the most critical gaps and asks only the 3–5 most necessary questions. After each stage, clearly tells you "what's done," "what's missing," and "what to do next." 🎯 Scope of Application (All Higher Education Scenarios) ✅ Teaching achievement award applications (institutional/provincial/national) ✅ Quality engineering project applications (top courses/teaching teams/textbooks, etc.) ✅ Course construction plans, major construction plans ✅ Major self-assessment reports, course acceptance reports ✅ Expert review comments, peer reviews ✅ Education reform project applications, closing reports 🚀 User Experience Writing an application from scratch: Provide the topic and basic materials → System identifies document type, takes inventory, matches policies and theories → Generates outline → Writes chapter by chapter → Consolidates → Get a draft in 1 hour. Reviewing existing text: Upload document → System automatically scores → Lists main issues → Provides revision suggestions and example rewrites → Get review report in 20 minutes. Optimizing existing plan: Provide existing text and optimization direction → System diagnoses weaknesses → Strengthens arguments, optimizes expression → Get optimized version in 30 minutes. 👉 Try it now—make higher education document writing no longer a burden. This is not just a writing assistant; it's an intelligent engine that understands higher education rules, review standards, and professional expression.
Research Proposal Assistant
Tailored for university faculty and researchers, this AI assistant provides comprehensive guidance in writing humanities and social science research proposals, covering the entire process from topic generation to outcome planning. Whether you need to brainstorm a topic from scratch or optimize a specific section of your proposal, this tool offers expert-level guidance and support. This assistant combines expert experience with a strategy of 'example guidance + writing theory integration' to help you efficiently produce high-quality proposals. You receive targeted analysis of research hotspots in your discipline, topic refinement suggestions, and—based on your research direction and project type—generate a rigorous background, in-depth literature review, and insightful research value statement. In the research content design phase, the assistant helps you define the research subject, build a logically clear research framework, and recommend innovative research ideas and methods. It also helps you distill key points and difficulties, set clear research objectives, and plan a detailed research schedule and feasibility analysis, ensuring your proposal is rigorous and well-structured. Additionally, you can use this tool to deeply explore the innovative aspects of your topic in terms of academic ideas, viewpoints, and research methods, and systematically plan multiple forms of expected outcomes, their applications, and social benefits. Finally, all content is integrated with one click to generate a complete, logically rigorous proposal that meets submission standards, helping you increase your success rate.
H&SS Topic Selection AFP
Humanities and Social Sciences Topic Selection Engineering System 🎯 Adaptive Coverage System: | Topic Type | System Fit | Difficulty Level | |------------|------------|------------------| | National Social Science Fund | ✅ Core Application | ★★★★★ | | Ministry of Education Humanities and Social Sciences Project | ✅ Fully Compatible | ★★★★☆ | | National Education Science Planning Key Projects | ✅ Fully Compatible | ★★★★☆ | | Provincial Social Science Planning | ✅ Fully Compatible | ★★★☆☆ | | Vocational Education/Teaching Reform Projects | ✅ Fully Compatible | ★★★☆☆ | 📊 Six-Stage Delivery Process (P0-P5 Mandatory Steps): | Stage | Phase | Core Output | Key Action | |-------|-------|-------------|------------| | P0 | Anchor Your Base | Personal Resources & Position Assessment | Take stock of your academic accumulation, existing achievements, and team resources | | P1 | Policy Decoding & Real Problem Extraction | Policy Analysis Table + Problem List | Scan the latest policy documents to uncover issues that truly matter to the review committee | | P2 | Atomic-Level Object Decomposition | Minimum Granularity Object Table | Break down grand propositions into core research objects | | P3 | Nine-Grid Topic Explosion | 9 Topic Direction Matrix | One object, 9 angles, select the optimal direction | | P4 | Critical Review & Finalization | Revision Suggestions + Risk Checklist | Two-role confrontation: applicant vs. blind reviewer, iterative refinement | | P5 | Asset Packaging | Topic Core Asset Memorandum | Final topic + reasoning logic + ready for proposal writing | 🔧 Built-in Standard Tool Library: ✓ Two-Role Confrontation System — Topic Planner ↔ Blind Review Expert (self vs. self PK) ✓ Naming Red Line Check — No colons/dashes/subtitles allowed, must end with "research" ✓ Trinity Rule — Qualifier + Object + Problem (invisible scoring sheet) ✓ Real-Time Policy Update — Synchronized with latest guidelines from NSSF, MOE, and Education Science Planning ✓ Nine-Grid Topic Matrix — Systematically spread 9 angles, visualize innovation comparison ✓ Blind Review Risk Assessment — Predict topic weaknesses in review 📈 Expected Results: 📌 Grant Approval Rate — From average 20-30% to 60-70%+ (depending on prior accumulation) ⏱️ Topic Selection Cycle — From 3-6 months of polishing to 2-3 weeks of systematic finalization 🎯 Hit Accuracy — From "broadcasting" to precisely targeting the review logic of the target committee 📦 Continuity — The Topic Memorandum can directly interface with "Grant Proposal Writing AFP" for a complete closed loop
Grant Proposal Review PRO V2.0
Grant proposals: 12-step, 9D QC, dual-core
Description
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
Related Skills
View allHE Document Write&Review v3.0
🎯 Got your application rejected? Can't find the highlight for your proposal? Don't know where to start with review comments? Three real scenarios: 🔸 Scenario 1: Application season anxiety—Reviewer feedback: "Insufficient theoretical support, vague policy basis." Unsure which documents to cite or which theoretical framework to use. 🔸 Scenario 2: Proposal writing dilemma—In charge of course construction plan: objectives, tasks, pathways, evaluation… each part needs writing, but you feel the logic is not rigorous enough and worry about being questioned on "feasibility" during review. 🔸 Scenario 3: Review dilemma—Need to write peer review comments: must point out issues while maintaining professionalism, be well-founded but not too harsh. How to strike the balance? 💡 What can this system do for you? Not just give advice—it writes, revises, and reviews for you directly. 📝 Writing Mode: From topic to final document Enter your topic and existing materials. The system automatically identifies the document type (application/proposal/report/review). Automatically matches authoritative policy documents and theoretical support. Generates content chapter by chapter, each with evidence, logic, and facts. Key promise: Never fabricates data; clearly tells you what's missing. 🔍 Review Mode: Expert-level diagnosis Upload your text. Professional scoring across 7 dimensions (value, alignment, completeness, innovation, feasibility, support, expression quality). Precisely identifies problem areas. Provides specific revision suggestions + example rewrites. Not general advice, but paragraph-level specific guidance. ✏️ Revision Optimization Mode: Precision enhancement Strengthens arguments based on existing text. Optimizes expression, eliminates empty talk and clichés. Standardizes terminology and logic. Improves overall competitiveness. ⚡ Three Core Mechanisms (Unique) 🛡️ Firewall Mechanism Built-in "fact boundary": User-provided real data is never fabricated; policy basis must have sources; theoretical support cannot be misapplied. Every sentence you see can be traced back to its source. 🔄 Multi-core Adversarial Engine One core writes, another specifically checks for errors. Like having a strict auditor watching, ensuring no "unsubstantiated facts," "logic gaps," or "policy mismatches" occur. 📊 Stepwise Guidance Doesn't ask you 20 questions at once—identifies the most critical gaps and asks only the 3–5 most necessary questions. After each stage, clearly tells you "what's done," "what's missing," and "what to do next." 🎯 Scope of Application (All Higher Education Scenarios) ✅ Teaching achievement award applications (institutional/provincial/national) ✅ Quality engineering project applications (top courses/teaching teams/textbooks, etc.) ✅ Course construction plans, major construction plans ✅ Major self-assessment reports, course acceptance reports ✅ Expert review comments, peer reviews ✅ Education reform project applications, closing reports 🚀 User Experience Writing an application from scratch: Provide the topic and basic materials → System identifies document type, takes inventory, matches policies and theories → Generates outline → Writes chapter by chapter → Consolidates → Get a draft in 1 hour. Reviewing existing text: Upload document → System automatically scores → Lists main issues → Provides revision suggestions and example rewrites → Get review report in 20 minutes. Optimizing existing plan: Provide existing text and optimization direction → System diagnoses weaknesses → Strengthens arguments, optimizes expression → Get optimized version in 30 minutes. 👉 Try it now—make higher education document writing no longer a burden. This is not just a writing assistant; it's an intelligent engine that understands higher education rules, review standards, and professional expression.
Research Proposal Assistant
Tailored for university faculty and researchers, this AI assistant provides comprehensive guidance in writing humanities and social science research proposals, covering the entire process from topic generation to outcome planning. Whether you need to brainstorm a topic from scratch or optimize a specific section of your proposal, this tool offers expert-level guidance and support. This assistant combines expert experience with a strategy of 'example guidance + writing theory integration' to help you efficiently produce high-quality proposals. You receive targeted analysis of research hotspots in your discipline, topic refinement suggestions, and—based on your research direction and project type—generate a rigorous background, in-depth literature review, and insightful research value statement. In the research content design phase, the assistant helps you define the research subject, build a logically clear research framework, and recommend innovative research ideas and methods. It also helps you distill key points and difficulties, set clear research objectives, and plan a detailed research schedule and feasibility analysis, ensuring your proposal is rigorous and well-structured. Additionally, you can use this tool to deeply explore the innovative aspects of your topic in terms of academic ideas, viewpoints, and research methods, and systematically plan multiple forms of expected outcomes, their applications, and social benefits. Finally, all content is integrated with one click to generate a complete, logically rigorous proposal that meets submission standards, helping you increase your success rate.
H&SS Topic Selection AFP
Humanities and Social Sciences Topic Selection Engineering System 🎯 Adaptive Coverage System: | Topic Type | System Fit | Difficulty Level | |------------|------------|------------------| | National Social Science Fund | ✅ Core Application | ★★★★★ | | Ministry of Education Humanities and Social Sciences Project | ✅ Fully Compatible | ★★★★☆ | | National Education Science Planning Key Projects | ✅ Fully Compatible | ★★★★☆ | | Provincial Social Science Planning | ✅ Fully Compatible | ★★★☆☆ | | Vocational Education/Teaching Reform Projects | ✅ Fully Compatible | ★★★☆☆ | 📊 Six-Stage Delivery Process (P0-P5 Mandatory Steps): | Stage | Phase | Core Output | Key Action | |-------|-------|-------------|------------| | P0 | Anchor Your Base | Personal Resources & Position Assessment | Take stock of your academic accumulation, existing achievements, and team resources | | P1 | Policy Decoding & Real Problem Extraction | Policy Analysis Table + Problem List | Scan the latest policy documents to uncover issues that truly matter to the review committee | | P2 | Atomic-Level Object Decomposition | Minimum Granularity Object Table | Break down grand propositions into core research objects | | P3 | Nine-Grid Topic Explosion | 9 Topic Direction Matrix | One object, 9 angles, select the optimal direction | | P4 | Critical Review & Finalization | Revision Suggestions + Risk Checklist | Two-role confrontation: applicant vs. blind reviewer, iterative refinement | | P5 | Asset Packaging | Topic Core Asset Memorandum | Final topic + reasoning logic + ready for proposal writing | 🔧 Built-in Standard Tool Library: ✓ Two-Role Confrontation System — Topic Planner ↔ Blind Review Expert (self vs. self PK) ✓ Naming Red Line Check — No colons/dashes/subtitles allowed, must end with "research" ✓ Trinity Rule — Qualifier + Object + Problem (invisible scoring sheet) ✓ Real-Time Policy Update — Synchronized with latest guidelines from NSSF, MOE, and Education Science Planning ✓ Nine-Grid Topic Matrix — Systematically spread 9 angles, visualize innovation comparison ✓ Blind Review Risk Assessment — Predict topic weaknesses in review 📈 Expected Results: 📌 Grant Approval Rate — From average 20-30% to 60-70%+ (depending on prior accumulation) ⏱️ Topic Selection Cycle — From 3-6 months of polishing to 2-3 weeks of systematic finalization 🎯 Hit Accuracy — From "broadcasting" to precisely targeting the review logic of the target committee 📦 Continuity — The Topic Memorandum can directly interface with "Grant Proposal Writing AFP" for a complete closed loop
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