HE Document Write&Review v3.0
Fact-checking, multi-core AI, step-by-step
Description
🎯 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.
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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.
Course Design Assistant v7.0
💡 What can this assistant do for you? An expert with years of experience in instructional design and lesson plan writing, guiding you step by step to create professional-level instructional designs. (Offline training valued at least ¥3000 per session.) 📋 13-step standardized process, scientific and rigorous, no omissions From course information collection → learning situation analysis → pain point diagnosis → key/difficult point confirmation → objective writing (draft + refinement) → teaching model selection → AI tool integration → process design → evaluation design → board design → complete plan → multi-format delivery Each step includes quality checks to ensure no skipped steps, no omissions, and no vague content. 🎯 Automatic ABCD principle check, no more headaches in objective writing The system automatically checks each objective against the ABCD principle and tags Bloom's taxonomy levels (L1-L6). 📊 Dedicated "Advancedity, Innovation, Challenge" check, aligned with first-class course standards No more worrying about reviewers saying "insufficient challenge." The system quantifies the check for you. 🧠 Intelligent Bloom's taxonomy matching, automatically achieving higher-order thinking The system includes a Bloom's verb classification table (L1-L6) and automatically identifies: Each objective is automatically tagged with its level, ensuring the proportion of higher-order objectives meets standards. 🎨 Natural integration of curriculum-based ideological and political education, no more forced labeling The system provides 5 integration strategies: - Situational embedding: Introduce through real cases/historical stories (e.g., "China's high-speed rail control system from import to independent innovation") - Behavioral concretization: Replace abstract concepts with observable behaviors (e.g., "Identify violations in engineering accidents" rather than "Improve professional ethics") - Value conflict: Set ethical dilemmas to guide judgment (e.g., "Economic benefits vs. environmental protection") - Cultural inheritance: Introduce through traditional culture/institutional advantages (e.g., "The artisan spirit of ancient astronomical instruments") - Comprehensive literacy goals: Covering scientific literacy, engineering literacy, innovation literacy, digital literacy, values, humanistic literacy, and global perspective ⏱️ Time-driven design to ensure the teaching process is implementable Automatically calculate the total time for each segment Compare with the total time set by the user If the error exceeds ±5 minutes, automatically alert and provide adjustment suggestions No more awkward situations where "the design looks great but there isn't enough class time" 🤖 Deep integration of AI tools, supporting higher-order thinking rather than just efficiency improvement Three-tier architecture: - L1 Tool layer: Visualization, real-time feedback, virtual simulation - L2 Teaching layer: Personalized paths, layered tasks, collaborative support - L3 Innovation layer: AI as debate opponent, decision simulation, design assistance Only recommends domestic AI tools (Wenxin Yiyan, Tongyi Qianwen, Doubao, etc.), supports integration of teacher-built intelligent agents 📐 Comprehensive evaluation design, perfect objective-activity-assessment alignment - Diagnostic evaluation: Pre-class assessment - Formative evaluation: Process monitoring (classroom questioning, group discussion, lab reports) - Summative evaluation: Outcome validation (exams, project presentations) For L4-L6 higher-order objectives, automatically generate evaluation rubrics including four dimensions: "Depth of analysis, Innovation, Argument quality, Critical thinking" 🎭 Two working modes to suit different needs - Mode A (Quick output): Provide basic information → System automatically completes steps 2-11 → Outputs complete plan directly → Suitable for time-constrained users who trust AI judgment - Mode B (Step-by-step interaction): Gradually confirm each step → User can modify at any time → Suitable for fine-tuning and deep involvement Regardless of mode, 4 delivery formats are available: Detailed version, concise version, visual web page, teaching process framework diagram ⚡ Six core competitive advantages (unique features) 🔬 1. Scientific process, not based on intuition 13-step standardized process strictly executed in order, no skipping. Each step has clear output and quality check to ensure the design aligns with educational principles. 📊 2. Quantitative check of "Advancedity, Innovation, Challenge" Automatically detect advancedity (proportion of higher-order objectives ≥50%), innovation (real situations + innovative modes), and challenge (open-ended questions + interdisciplinary integration), aligned with first-class course standards. 🎯 3. Automatic ABCD principle verification Each objective must include "Condition-Behavior-Standard," with observable and measurable action verbs. The system automatically checks and tags Bloom's levels. 🧠 4. Intelligent Bloom's taxonomy matching Built-in L1-L6 verb classification table, automatically identifies objective levels, ensures higher-order thinking standards are met, no more worrying about reviewers saying "objective level is too low." ⏱️ 5. Time-driven design Automatically calculate segment durations, compare with total duration, alert if error exceeds ±5 minutes, ensuring the teaching design is implementable and within time limits. 📐 6. Perfect objective-activity-assessment alignment Constructive Alignment principle ensures consistency among "teaching objectives, teaching activities, and assessment methods." Each objective has corresponding activities and evaluations. 🎯 Applicable scenarios (covering all higher education) ✅ Teaching competitions: Teaching innovation competitions, young teacher teaching competitions, classroom teaching contests ✅ Course construction: First-class course applications, curriculum-based ideological and political demonstration courses, blended course design ✅ Daily teaching: New lesson preparation, teaching reform, teaching model innovation ✅ Teaching evaluation: Peer review, supervisory observation, teaching quality assessment ✅ Teacher development: New teacher training, teaching ability enhancement, teaching research 🚀 User experience Quick mode (Mode A): Provide course name, chapter, major, class hours → Select Mode A → System automatically completes 13-step process → Get complete teaching design in 30 minutes → Choose delivery format (detailed/concise/visual web page/framework diagram) Detailed mode (Mode B): Provide basic information → Select Mode B → Gradually confirm learning situation analysis, pain point diagnosis, key/difficult points, teaching objectives, teaching model, AI tools, teaching process, evaluation design, board design → Modify at each step → Complete high-quality teaching design in 1-2 hours Review mode: Upload existing teaching design → System evaluates step by step against all elements → Summarize three strengths and three weaknesses → Provide specific and actionable optimization suggestions → Generate a standardized and well-formatted evaluation report document
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)
HE Document Write&Review v3.0
Fact-checking, multi-core AI, step-by-step
Description
🎯 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.
Related Skills
View allGrant 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.
Course Design Assistant v7.0
💡 What can this assistant do for you? An expert with years of experience in instructional design and lesson plan writing, guiding you step by step to create professional-level instructional designs. (Offline training valued at least ¥3000 per session.) 📋 13-step standardized process, scientific and rigorous, no omissions From course information collection → learning situation analysis → pain point diagnosis → key/difficult point confirmation → objective writing (draft + refinement) → teaching model selection → AI tool integration → process design → evaluation design → board design → complete plan → multi-format delivery Each step includes quality checks to ensure no skipped steps, no omissions, and no vague content. 🎯 Automatic ABCD principle check, no more headaches in objective writing The system automatically checks each objective against the ABCD principle and tags Bloom's taxonomy levels (L1-L6). 📊 Dedicated "Advancedity, Innovation, Challenge" check, aligned with first-class course standards No more worrying about reviewers saying "insufficient challenge." The system quantifies the check for you. 🧠 Intelligent Bloom's taxonomy matching, automatically achieving higher-order thinking The system includes a Bloom's verb classification table (L1-L6) and automatically identifies: Each objective is automatically tagged with its level, ensuring the proportion of higher-order objectives meets standards. 🎨 Natural integration of curriculum-based ideological and political education, no more forced labeling The system provides 5 integration strategies: - Situational embedding: Introduce through real cases/historical stories (e.g., "China's high-speed rail control system from import to independent innovation") - Behavioral concretization: Replace abstract concepts with observable behaviors (e.g., "Identify violations in engineering accidents" rather than "Improve professional ethics") - Value conflict: Set ethical dilemmas to guide judgment (e.g., "Economic benefits vs. environmental protection") - Cultural inheritance: Introduce through traditional culture/institutional advantages (e.g., "The artisan spirit of ancient astronomical instruments") - Comprehensive literacy goals: Covering scientific literacy, engineering literacy, innovation literacy, digital literacy, values, humanistic literacy, and global perspective ⏱️ Time-driven design to ensure the teaching process is implementable Automatically calculate the total time for each segment Compare with the total time set by the user If the error exceeds ±5 minutes, automatically alert and provide adjustment suggestions No more awkward situations where "the design looks great but there isn't enough class time" 🤖 Deep integration of AI tools, supporting higher-order thinking rather than just efficiency improvement Three-tier architecture: - L1 Tool layer: Visualization, real-time feedback, virtual simulation - L2 Teaching layer: Personalized paths, layered tasks, collaborative support - L3 Innovation layer: AI as debate opponent, decision simulation, design assistance Only recommends domestic AI tools (Wenxin Yiyan, Tongyi Qianwen, Doubao, etc.), supports integration of teacher-built intelligent agents 📐 Comprehensive evaluation design, perfect objective-activity-assessment alignment - Diagnostic evaluation: Pre-class assessment - Formative evaluation: Process monitoring (classroom questioning, group discussion, lab reports) - Summative evaluation: Outcome validation (exams, project presentations) For L4-L6 higher-order objectives, automatically generate evaluation rubrics including four dimensions: "Depth of analysis, Innovation, Argument quality, Critical thinking" 🎭 Two working modes to suit different needs - Mode A (Quick output): Provide basic information → System automatically completes steps 2-11 → Outputs complete plan directly → Suitable for time-constrained users who trust AI judgment - Mode B (Step-by-step interaction): Gradually confirm each step → User can modify at any time → Suitable for fine-tuning and deep involvement Regardless of mode, 4 delivery formats are available: Detailed version, concise version, visual web page, teaching process framework diagram ⚡ Six core competitive advantages (unique features) 🔬 1. Scientific process, not based on intuition 13-step standardized process strictly executed in order, no skipping. Each step has clear output and quality check to ensure the design aligns with educational principles. 📊 2. Quantitative check of "Advancedity, Innovation, Challenge" Automatically detect advancedity (proportion of higher-order objectives ≥50%), innovation (real situations + innovative modes), and challenge (open-ended questions + interdisciplinary integration), aligned with first-class course standards. 🎯 3. Automatic ABCD principle verification Each objective must include "Condition-Behavior-Standard," with observable and measurable action verbs. The system automatically checks and tags Bloom's levels. 🧠 4. Intelligent Bloom's taxonomy matching Built-in L1-L6 verb classification table, automatically identifies objective levels, ensures higher-order thinking standards are met, no more worrying about reviewers saying "objective level is too low." ⏱️ 5. Time-driven design Automatically calculate segment durations, compare with total duration, alert if error exceeds ±5 minutes, ensuring the teaching design is implementable and within time limits. 📐 6. Perfect objective-activity-assessment alignment Constructive Alignment principle ensures consistency among "teaching objectives, teaching activities, and assessment methods." Each objective has corresponding activities and evaluations. 🎯 Applicable scenarios (covering all higher education) ✅ Teaching competitions: Teaching innovation competitions, young teacher teaching competitions, classroom teaching contests ✅ Course construction: First-class course applications, curriculum-based ideological and political demonstration courses, blended course design ✅ Daily teaching: New lesson preparation, teaching reform, teaching model innovation ✅ Teaching evaluation: Peer review, supervisory observation, teaching quality assessment ✅ Teacher development: New teacher training, teaching ability enhancement, teaching research 🚀 User experience Quick mode (Mode A): Provide course name, chapter, major, class hours → Select Mode A → System automatically completes 13-step process → Get complete teaching design in 30 minutes → Choose delivery format (detailed/concise/visual web page/framework diagram) Detailed mode (Mode B): Provide basic information → Select Mode B → Gradually confirm learning situation analysis, pain point diagnosis, key/difficult points, teaching objectives, teaching model, AI tools, teaching process, evaluation design, board design → Modify at each step → Complete high-quality teaching design in 1-2 hours Review mode: Upload existing teaching design → System evaluates step by step against all elements → Summarize three strengths and three weaknesses → Provide specific and actionable optimization suggestions → Generate a standardized and well-formatted evaluation report document
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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