Enterprise AI Analysis
Generative Inquiry-Based Learning: New Models of AI-empowered Education
This research analyzes how Generative Inquiry-Based Learning (GIL) leverages AI to transform education, reducing cognitive load and improving teaching efficiency. It proposes a teacher-student-machine collaborative framework, based on the 4C/ID model, and demonstrates its application in a 'Python Programming' module. GIL aims to cultivate critical thinking, creativity, and self-motivation, addressing challenges in the Industry 4.0 era while mitigating risks of AI over-dependence.
Executive Impact at a Glance
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Deep Analysis & Enterprise Applications
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The 4C/ID model, efficient in Europe, faces adoption barriers due to complex design and teacher expertise requirements. AI offers a solution by transforming teachers into instructional architects, simplifying task restructuring and refinement. This alleviates workload and enhances teaching effectiveness. The research highlights how AI can prepare courseware more efficiently, offer useful advice, and make evaluation more objective and efficient. For students, AI provides personalized learning solutions, reducing cognitive load and boosting self-motivation.
4C/ID
Model enhanced by AI
This model provides customized learning paths, rich resources, real-time feedback, and dynamic assessment, enhancing critical thinking and self-motivated inquiry. It involves multi-round, AI-guided tasks with teacher-provided resources, moving beyond traditional textbooks to diverse, quick-response resources. AI-enabled real-time feedback ensures continuous progress assessment for both teachers and students, a significant improvement over static exam-based evaluations.
| Aspect | Traditional Inquiry | AI-Assisted Inquiry |
|---|---|---|
| Resources | Solely textbooks, limited diversity |
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| Feedback | Delayed, exam-based |
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| Personalization | Generic learning path |
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| Skill Development | Reliance on teacher guidance |
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The GIL framework integrates the 4C/ID model, 5E inquiry-based learning, and AI to benefit both teachers and students. For teachers, AI reduces complexity, provides tailored exercises, sufficient support, and timely feedback. For students, it alleviates cognitive burden, enhances knowledge absorption, and boosts self-motivation through task breakdown and feedback. Prompt templates from OpenAI and other sources further reduce teacher workload and encourage student questions, feedback, and strategy adjustments.
Generative Inquiry Learning Process
Teacher Defines Objectives
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AI-Assisted Task Design (4C/ID)
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AI-Assisted Student Inquiry (5E)
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Instructional Prompt Templates
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Real-time Feedback & Assessment
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Improved Learning Outcomes
The 'Python Programming' module demonstrates GIL's application. Teaching objectives are divided into knowledge and skills, with four incremental difficulty learning tasks (per Bloom's taxonomy). AI generates multi-level exercises, refined by teachers, addressing diverse student needs. The course execution involves introduction, exploration, reflection, expansion, and evaluation stages, where AI tools aid in problem-solving, code optimization, and fostering critical thinking and team spirit.
Python Programming with GIL
Course Planning & Objectives
- Divides objectives into knowledge-oriented and skills-oriented.
- Establishes four learning tasks with incremental difficulty (Bloom's taxonomy).
- AI generates multi-level specialized exercises, refined by teacher experience.
Course Execution Stages
- Introduction: Teacher provides supportive information, students construct knowledge framework.
- Exploration: Students solve programming problems with AI aid (syntax errors, logical flaws, model optimization).
- Reflection: Students evaluate inquiry results, optimize code for quality control.
- Expansion: Peers review codes, share ideas, develop systematic thinking and team spirit.
- Evaluation: Teachers assess programs' functionality, structures, and maintainability, fostering critical and analytical thinking.
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