TRIZ-Academic Problem Discovery v2.0
14 TRIZ Core Tools · 1+4 Matrix Architecture
Description
The core dilemma researchers face when selecting a topic is often not 'having no problems,' but 'not seeing the real problem.' TRIZ-Academic Problem Discovery v2.0 uses the AFP 3.1 three-layer architecture, integrating 14 core TRIZ tools to discover scientifically rigorous problems with theoretical tension from ambiguous dilemmas. Core Capabilities: 1. 1+4 Matrix Architecture — 1 router (8-signal diagnosis) + 4 execution paths (perspective expansion, conflict resolution, solution construction, thought diagnosis) 2. Discipline Adaptation — Automatically adapts TRIZ tool emphasis across three modes: STEM (technical contradictions), humanities and social sciences (social contradictions), and management (organizational contradictions). 3. In-depth Operation of 14 TRIZ Tools — Nine-screen method, laws of evolution, ideal final result, ARIZ, separation principles, STC operator, resource analysis, functional analysis, etc., each with an operational template. 4. B-Core Nine-Dimension Quantitative Audit — Structure, logic, tool accuracy, discipline fit, innovativeness, conflict resolution, anti-compromise, executability, theoretical tension; a score of ≥80 is required for delivery. 5. Three-Level Degradation Strategy — Automatically degrades to general innovation methodology when TRIZ tools are not suitable. Suitable for: PhD and master's students, early-career researchers, interdisciplinary researchers, and supervisors. Supports both Chinese and English.
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TRIZ Research Innovation Coach
TRIZ Research Innovation Coach is a system for discovering innovative research problems and generating research topics for academic work. Centered on TRIZ contradiction resolution, the nine-screen method, laws of evolution, resource analysis, and thinking-trap diagnosis, it uses a rigorous AFP-style step-by-step process to transform vague research challenges into clear scientific questions, actionable research plans, and theoretically compelling topic directions. The system also includes dual review by a thinking coach and an academic editor, assessing research subjects, theoretical mechanisms, evidence pathways, methodological fit, and degree of innovation. It helps users overcome common challenges such as “I can’t find an innovation,” “I can only compromise,” and “my methods are limiting me,” while building transferable and reusable innovative thinking skills.
STEM PhD Topic Diagnostician
Already have a PhD dissertation topic but unsure whether it is rigorous, innovative, and feasible? This Skill is designed to diagnose existing topics—not randomly generate topics from scratch or merely polish the wording. It breaks down the research subject, core variables, mechanisms, outcome measures, and research boundaries in your topic. Based on the Chinese and English literature, experimental foundation, samples, methods, equipment, timeline, and budget you provide, it assesses the topic’s rigor, novelty, and feasibility, while identifying risks such as “innovation” based only on changing materials, confounded variables, insufficient evidence for the proposed mechanism, an uncontrolled sample matrix, and chapters that cannot build progressively. The final output is a STEM PhD Topic Health Check Report, which clearly recommends whether to: retain the topic with minor adjustments; retain the research subject while strengthening its dimensions; retain the research problem and reconstruct the subject; or stop patching and redesign the topic. It is intended for STEM PhD students in food science, chemistry, materials science, biology, basic medical sciences, environmental science, agriculture, energy, and engineering. It is especially useful for those preparing a proposal defense, revising a topic at an advisor’s request, who have already defended their proposal but lack a clear central thread, or who have existing experiments but are unsure whether they can support an entire PhD dissertation.
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.
TRIZ-Academic Problem Discovery v2.0
14 TRIZ Core Tools · 1+4 Matrix Architecture
Description
The core dilemma researchers face when selecting a topic is often not 'having no problems,' but 'not seeing the real problem.' TRIZ-Academic Problem Discovery v2.0 uses the AFP 3.1 three-layer architecture, integrating 14 core TRIZ tools to discover scientifically rigorous problems with theoretical tension from ambiguous dilemmas. Core Capabilities: 1. 1+4 Matrix Architecture — 1 router (8-signal diagnosis) + 4 execution paths (perspective expansion, conflict resolution, solution construction, thought diagnosis) 2. Discipline Adaptation — Automatically adapts TRIZ tool emphasis across three modes: STEM (technical contradictions), humanities and social sciences (social contradictions), and management (organizational contradictions). 3. In-depth Operation of 14 TRIZ Tools — Nine-screen method, laws of evolution, ideal final result, ARIZ, separation principles, STC operator, resource analysis, functional analysis, etc., each with an operational template. 4. B-Core Nine-Dimension Quantitative Audit — Structure, logic, tool accuracy, discipline fit, innovativeness, conflict resolution, anti-compromise, executability, theoretical tension; a score of ≥80 is required for delivery. 5. Three-Level Degradation Strategy — Automatically degrades to general innovation methodology when TRIZ tools are not suitable. Suitable for: PhD and master's students, early-career researchers, interdisciplinary researchers, and supervisors. Supports both Chinese and English.
Related Skills
View allTRIZ Research Innovation Coach
TRIZ Research Innovation Coach is a system for discovering innovative research problems and generating research topics for academic work. Centered on TRIZ contradiction resolution, the nine-screen method, laws of evolution, resource analysis, and thinking-trap diagnosis, it uses a rigorous AFP-style step-by-step process to transform vague research challenges into clear scientific questions, actionable research plans, and theoretically compelling topic directions. The system also includes dual review by a thinking coach and an academic editor, assessing research subjects, theoretical mechanisms, evidence pathways, methodological fit, and degree of innovation. It helps users overcome common challenges such as “I can’t find an innovation,” “I can only compromise,” and “my methods are limiting me,” while building transferable and reusable innovative thinking skills.
STEM PhD Topic Diagnostician
Already have a PhD dissertation topic but unsure whether it is rigorous, innovative, and feasible? This Skill is designed to diagnose existing topics—not randomly generate topics from scratch or merely polish the wording. It breaks down the research subject, core variables, mechanisms, outcome measures, and research boundaries in your topic. Based on the Chinese and English literature, experimental foundation, samples, methods, equipment, timeline, and budget you provide, it assesses the topic’s rigor, novelty, and feasibility, while identifying risks such as “innovation” based only on changing materials, confounded variables, insufficient evidence for the proposed mechanism, an uncontrolled sample matrix, and chapters that cannot build progressively. The final output is a STEM PhD Topic Health Check Report, which clearly recommends whether to: retain the topic with minor adjustments; retain the research subject while strengthening its dimensions; retain the research problem and reconstruct the subject; or stop patching and redesign the topic. It is intended for STEM PhD students in food science, chemistry, materials science, biology, basic medical sciences, environmental science, agriculture, energy, and engineering. It is especially useful for those preparing a proposal defense, revising a topic at an advisor’s request, who have already defended their proposal but lack a clear central thread, or who have existing experiments but are unsure whether they can support an entire PhD dissertation.
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.
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