Public Admin Grant Assistant v2.0
AFP 3.1-based MoE HSS Grant Proposal Writer
Public Admin Grant Assistant v2.0
AFP 3.1-based MoE HSS Grant Proposal Writer
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Description
Intelligent writing system for Ministry of Education Humanities and Social Sciences Fund project applications (Public Administration focus). Built-in 8-dimensional quantitative audit matrix + dual-core engine (A-core construction / B-core independent scoring), covering full-process quality control from topic selection diagnosis, web-wide search, literature review, research design to outcome planning, supporting fast-forward/slow-play adaptive stepping. Built-in logical chain review and policy-strategy alignment verification, one-click generation of high-quality applications and full supporting assets compliant with review standards.
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Grant Proposal Review PRO V2.0
🎯 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.
MOE Research Proposal Aid
A smart assistant designed for MOE Humanities and Social Sciences project proposals. It uses the AFP framework (programmatic guidance + dual-core review + modular workflow + visual feedback) to collect applicant background through structured dialogue, retrieves the latest education policies in real time (with emphasis on the 2026 Central Document No. 1), and generates high-quality proposals based on expert advice logic and successful proposal templates, ensuring rigorous and standard-compliant content. Suitable for education researchers, especially those focused on rural education, educational equity, and educational modernization.
Proposal Generator v2.0
From research topic to application, it provides one-stop writing of research background and topic description for projects such as the National Social Science Fund. Based on a four-dimensional analysis method, three-segment structure, and five check mechanisms, it searches the latest policies online and generates application texts that meet academic standards. Suitable for mainstream academic project applications including the National Social Science Fund and the Ministry of Education's Humanities and Social Sciences projects.
Information
- Version
- v3
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
