Top-Prize Teaching Deck Expert
Top-Prize Teaching Deck Expert
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Description
For faculty in STEM and applied universities, this all-in-one expert skill starts with learning-survey data and teaching pain points, then guides you step by step through top-level redesign of teaching innovation initiatives for the Teaching Innovation Competition, page-by-page PPT visual design, and high-fidelity generation and assembly of Slides presentations.
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Slides Design Expert (Teaching)
This skill is designed to generate high-quality, pedagogically rigorous, and visually stunning presentation outlines for educators, trainers, and content creators. It goes beyond the mediocrity of traditional AI-generated content, focusing on creating learning materials with design taste, teaching depth, and visual impact. Users simply provide their teaching content, and the skill systematically analyzes knowledge points, builds a logically rigorous knowledge system, and intelligently infers the most suitable design language and visual style based on the subject characteristics, target audience, and teaching goals. It ensures each slide has a unique layout and clear visual hierarchy, avoiding repetition and templatization. The core capabilities of this skill include in-depth teaching content analysis, personalized design language inference, and slide outline generation strictly following the 'anti-mediocrity' principle. It can convert abstract concepts into concrete visual representations and provide detailed narrative goals, key content, visual descriptions, and layout structure suggestions for each slide. The final outline not only contains complete teaching logic but also incorporates professional design guidance, ensuring that the presentation effectively conveys knowledge while providing an excellent visual experience.
ResearchFull-Process TIC One-Stop v.20
Strictly aligned with the real competition preparation requirements of the National College Teacher Teaching Innovation Competition, it generates all competition materials in one go. Built-in pain point diagnosis framework, 10 methodology toolkits, nine-track differentiation strategies (fully upgraded in 2026). Supports multi-path workflow expansion. Includes judge's perspective deep diagnosis, intelligent questionnaire for track selection, and track recommendation decision tree.
ResearchTeaching Innovation Prep Guide
Whether you are a university teacher participating for the first time, or you want to enhance the competitiveness of your existing courses, this preparation guide can help you stand out in the Teaching Innovation Competition. It will serve as your personal coaching partner, providing full-process support from analyzing the underlying logic of the competition to on-site presentation delivery. This guide is deeply anchored in the evaluation criteria and core methodology of the Teaching Innovation Competition, helping you correct common misunderstandings and understand that the essence of the competition is teaching academic research. You will get a personalized preparation path, whether you need systematic full-process guidance, want to optimize based on existing courses, or seek breakthroughs in a specific area, you can find the most suitable plan. From sorting out course foundations and identifying teaching pain points, to designing innovative measures and refining teaching effectiveness, the guide will lead you step by step. It not only helps you write high-quality course descriptions, teaching objectives, and innovation reports, but also provides practical guidance on key aspects such as classroom recording and PPT creation. Every critical decision is supported by rigorous evidence and professional advice, ensuring your materials are logical and persuasive. Finally, through consistency verification of the three materials and alignment check with grading criteria, it ensures that all your competition materials meet high standards, effectively avoiding empty statements, and presenting your teaching achievements in the most professional and impactful way.
Information
- Version
- v3
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
