Prov Ed Reform: Mortise-Mirror
Prov. teaching reform grant writing guide
Description
“Mortise-Mirror Method · Education Reform Edition” is a full-process assistant tool for provincial education reform projects, covering topic selection diagnosis, key issues, innovation points, research objectives, research content, methods, implementation plans, outcome design, and application drafting. It draws on experience from multiple successful project approvals and stands out for its distinctive approach: every step in topic selection is supported by evidence. It first locks onto the two core competitive advantages — “problems to be solved + innovation points” — then works backwards to define research objectives and content. Its original “Mortise-Mirror” consistency check ensures logical coherence. It also embeds reviewer-preferred expression styles and sentence structures. Compared with the first version, the second version mainly offers six improvements: - Upgrades from “one-to-one correspondence between problems and innovations” to supporting one-to-many and many-to-one logical connections. - Shifts from “step-by-step confirmation” to confirmation at key milestones, reducing unnecessary pauses. - Adds a “concept—evidence—conclusion” check to prevent insufficient evidence and exaggerated conclusions. - Distinguishes research methods, data analysis, technical support, and rigor measures. - Plans expected outcomes in advance, using the outcomes to retroactively validate objectives and content. - Adds project ledger, traceability matrix, ethical risk, and Word chart formatting standards. The new version more strongly highlights the core thread of education reform projects: real teaching problems—reform mechanisms—classroom implementation—evidence of effectiveness—outcome promotion.
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Provincial Proposal Assistant
This Skill is also known as 'Mortise-and-Tenon Mirror Method: Education Planning Edition.' It draws on the proposal experience of several successfully funded projects. It is not a simple text ghostwriter but a guide that helps college teachers turn vague ideas into logically rigorous, methodologically feasible, and evidence-rich provincial education science planning projects. The Skill covers topic selection diagnosis, literature search and research gap identification, research question formation, concept definition and theoretical framework construction, innovation point extraction, goal and content design, research methods and sample planning, evidence and conclusion boundary review, expected outcomes and implementation plan design, proposal rationale writing, full-text review and optimization, and proposal chart creation. Through a mortise-and-tenon-style check of 'problem—theory—goal—content—method—evidence—contribution—outcome,' it proactively identifies theory mislabeling, method mismatches, insufficient evidence, and exaggerated conclusions, ensuring the proposal is not only formally complete but also truly sound in research design and review logic.
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.
ResearchTeaching Award App Auto Expert
Teaching Achievement Award Application · Full-Process Automation Advisor System 🎯 Adaptive Recognition of Coverage Levels: Education Type: Basic Education / Vocational Education / Higher Education → Automatically matches the application system Award Level: National / Provincial / School Level → Automatically identifies difficulty and focus of competition 📊 Four Delivery Phases (Staged Closed Loop): | Phase | Output | Core Value | |-------|--------|------------| | 1. Diagnosis & Profiling | 7-8 question smart survey + achievement positioning report | Identify the right application level to avoid over or under applying | | 2. Topic Selection | Topic direction matrix + 3-5 similar successful cases | Know which direction to adjust to be most visible | | 3. Title Incubation | 5-8 alternative titles (SCPAR naming) | If the title is right, half the application is done | | 4. Body Writing | Complete version of the application with strict word count | How many words for innovation, results, and dissemination value — precise to the paragraph | | 5. Diagnostic Scoring | Three-dimensional innovation assessment + expert checklist | Reviewing it yourself after editing is like having a professional review | 🔧 Built-in Standardized Tool Library: ✓ SCPAR Naming Rule — The invisible scoring table for teaching achievement titles ✓ Word Count Hard Constraint Check — Automatically enforces 5000-12000 word range ✓ Three-Dimensional Innovation Assessment Model — Full reproduction of the scoring logic used by award judges ✓ Title Template Library — Reference templates for national/provincial/school level achievements ✓ Expert Checklist — Item-by-item self-check for the finished application 📈 Expected Results: Application success rate from random 30% to precise 70%+ Application preparation time from 2-3 months to 2-3 weeks Higher first-submission hit rate (more accurate topic selection)
Prov Ed Reform: Mortise-Mirror
Prov. teaching reform grant writing guide
Description
“Mortise-Mirror Method · Education Reform Edition” is a full-process assistant tool for provincial education reform projects, covering topic selection diagnosis, key issues, innovation points, research objectives, research content, methods, implementation plans, outcome design, and application drafting. It draws on experience from multiple successful project approvals and stands out for its distinctive approach: every step in topic selection is supported by evidence. It first locks onto the two core competitive advantages — “problems to be solved + innovation points” — then works backwards to define research objectives and content. Its original “Mortise-Mirror” consistency check ensures logical coherence. It also embeds reviewer-preferred expression styles and sentence structures. Compared with the first version, the second version mainly offers six improvements: - Upgrades from “one-to-one correspondence between problems and innovations” to supporting one-to-many and many-to-one logical connections. - Shifts from “step-by-step confirmation” to confirmation at key milestones, reducing unnecessary pauses. - Adds a “concept—evidence—conclusion” check to prevent insufficient evidence and exaggerated conclusions. - Distinguishes research methods, data analysis, technical support, and rigor measures. - Plans expected outcomes in advance, using the outcomes to retroactively validate objectives and content. - Adds project ledger, traceability matrix, ethical risk, and Word chart formatting standards. The new version more strongly highlights the core thread of education reform projects: real teaching problems—reform mechanisms—classroom implementation—evidence of effectiveness—outcome promotion.
Related Skills
View allProvincial Proposal Assistant
This Skill is also known as 'Mortise-and-Tenon Mirror Method: Education Planning Edition.' It draws on the proposal experience of several successfully funded projects. It is not a simple text ghostwriter but a guide that helps college teachers turn vague ideas into logically rigorous, methodologically feasible, and evidence-rich provincial education science planning projects. The Skill covers topic selection diagnosis, literature search and research gap identification, research question formation, concept definition and theoretical framework construction, innovation point extraction, goal and content design, research methods and sample planning, evidence and conclusion boundary review, expected outcomes and implementation plan design, proposal rationale writing, full-text review and optimization, and proposal chart creation. Through a mortise-and-tenon-style check of 'problem—theory—goal—content—method—evidence—contribution—outcome,' it proactively identifies theory mislabeling, method mismatches, insufficient evidence, and exaggerated conclusions, ensuring the proposal is not only formally complete but also truly sound in research design and review logic.
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.
ResearchTeaching Award App Auto Expert
Teaching Achievement Award Application · Full-Process Automation Advisor System 🎯 Adaptive Recognition of Coverage Levels: Education Type: Basic Education / Vocational Education / Higher Education → Automatically matches the application system Award Level: National / Provincial / School Level → Automatically identifies difficulty and focus of competition 📊 Four Delivery Phases (Staged Closed Loop): | Phase | Output | Core Value | |-------|--------|------------| | 1. Diagnosis & Profiling | 7-8 question smart survey + achievement positioning report | Identify the right application level to avoid over or under applying | | 2. Topic Selection | Topic direction matrix + 3-5 similar successful cases | Know which direction to adjust to be most visible | | 3. Title Incubation | 5-8 alternative titles (SCPAR naming) | If the title is right, half the application is done | | 4. Body Writing | Complete version of the application with strict word count | How many words for innovation, results, and dissemination value — precise to the paragraph | | 5. Diagnostic Scoring | Three-dimensional innovation assessment + expert checklist | Reviewing it yourself after editing is like having a professional review | 🔧 Built-in Standardized Tool Library: ✓ SCPAR Naming Rule — The invisible scoring table for teaching achievement titles ✓ Word Count Hard Constraint Check — Automatically enforces 5000-12000 word range ✓ Three-Dimensional Innovation Assessment Model — Full reproduction of the scoring logic used by award judges ✓ Title Template Library — Reference templates for national/provincial/school level achievements ✓ Expert Checklist — Item-by-item self-check for the finished application 📈 Expected Results: Application success rate from random 30% to precise 70%+ Application preparation time from 2-3 months to 2-3 weeks Higher first-submission hit rate (more accurate topic selection)
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