AI Analysis Report
The Mode and Practice Path of Generative Artificial Intelligence Empowerment Classroom Teaching – Based on Grounded Theory
Author: Xiaoqing Zhang, Shipian Xu, Yuhui Jing | Date: March 14-16, 2025
The integration of Generative Artificial Intelligence (GAI) in education has garnered increasing attention due to its transformative potential in classroom teaching. Using grounded theory, this study analyzes relevant policy texts and teaching case studies to construct a comprehensive framework for GAI applications in educational settings. Findings indicate that GAI can enhance personalized learning, foster improved teacher-student interactions, and streamline educational processes, providing novel pathways for educational innovation.
Executive Impact & Key Findings
This report synthesizes key findings from 65 classroom teaching cases on GAI integration, highlighting its transformative impact on education. Our analysis reveals that GAI is crucial for personalized learning, enhancing teacher-student dynamics, and streamlining educational workflows. The study introduces a comprehensive framework and practical pathways for GAI-enabled classroom teaching, bridging the gap between macro-theoretical perspectives and practical implementation. It underscores GAI's role in driving educational innovation, particularly for China's "Artificial Intelligence +" strategy, by redefining roles, optimizing instructional design, and adapting to diverse educational contexts.
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Teaching Cases Analyzed
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Teaching Modes Innovated
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Implementation Readiness Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction to GAI in Education
Generative Artificial Intelligence (GAI) is rapidly transforming global education, with policy documents from the UK, UNESCO, and China highlighting its potential. GAI is seen as a key driver for personalized learning, improved educational quality, and equity. Despite its promise, practical integration in classroom settings, especially in China, remains underdeveloped. This study aims to fill this gap by exploring GAI-based teaching models through grounded theory and offering practical recommendations.
Research Methodology & Results
The study analyzed 65 classroom teaching cases, including 31 from the Ministry of Education's AI+ higher education applications and 34 from web searches. Through open coding, 48 initial concepts were abstracted and categorized into 11 themes. Axial coding consolidated these into core components: learning space, instructional design, and role relationships. Selective coding identified instructional design as central, optimizing content and methods through GAI, leading to a theoretical model for GAI-based classroom teaching.
Theoretical Model of GAI-Enabled Teaching
The proposed theoretical model focuses on three dimensions: people (role relationships), things (intelligent aids, resources, environment), and objects (instructional design). It emphasizes a 'preconditions - implementation process - effect presentation' pattern. GAI reconfigures learning space with virtual environments and intelligent tools, optimizes pedagogical design for dynamic adaptation, and reshapes teacher/student roles from transmitters/recipients to guides/explorers, fostering a symbiotic 'tool empowerment' and 'subject development'.
Practical Paths for GAI Implementation
The GAI-enabled classroom teaching model optimizes learning through intelligent tools and data analysis across pre-course, in-course, and post-course phases. In the pre-course, GAI recommends personalized materials and aids course design. In-course, it facilitates intelligent Q&A, interactive learning, and real-time process assessment. Post-course, GAI analyzes data for effectiveness and offers resources for independent study, creating a continuous feedback loop.
Adaptability and Challenges of GAI
GAI application requires adaptation to disciplinary knowledge, student cognitive levels, and regional resources. While effective in programming (GitHub Copilot) and medical simulations (Peking University's Virtual Simulation Lab), challenges exist in artistic creation, metaphorical understanding, and cross-cultural sensitivity. Resource disparities (urban vs. rural) and algorithmic bias need careful consideration to ensure equitable and effective implementation, alongside continuous teacher/student training in technological literacy.
65
Classroom Teaching Cases Analyzed
Enterprise Process Flow
Open Coding (48 Initial Concepts)
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Axial Coding (3 Core Components)
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Selective Coding (Instructional Design as Central)
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Theoretical Model Construction
| Aspect | Traditional Teaching | GAI-Enabled Teaching |
|---|---|---|
| Teacher Role | Knowledge Transmitter |
|
| Student Role | Passive Recipient |
|
| Learning Space | Physical Classroom (Limited) |
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| Instructional Design | One-size-fits-all |
|
Case Study: Peking University's Virtual Simulation Intelligence Laboratory
Context: Higher education, medical training.
Challenge: Training higher-order clinical skills with precision and realism.
Solution: Utilized mixed reality technology with force feedback and multimodal interactions.
Result: Transformed medical operations into quantitative training, deepening professional competence, and optimizing instrumental attributes with contextual immersion.
Calculate Your Potential ROI with AI
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Your AI Implementation Roadmap
A structured approach to integrating GAI into your organization's learning and development strategy, ensuring a smooth transition and maximum impact.
Phase 1: Pilot Programs & Data Collection
Initiate GAI pilot programs in diverse educational settings, focusing on collecting baseline data on student engagement, learning outcomes, and teacher feedback. Establish technical infrastructure and initial training for educators.
Phase 2: Iterative Model Refinement
Analyze pilot data to refine GAI algorithms and instructional design models. Incorporate teacher-student feedback to enhance personalization features and address any ethical concerns. Develop specialized GAI tools for specific disciplinary needs.
Phase 3: Scalable Deployment & Training
Expand GAI integration to broader educational contexts, including differentiated approaches for urban and rural areas. Implement comprehensive training programs for all educators on GAI literacy, ethical usage, and adaptive teaching methodologies.
Phase 4: Continuous Evaluation & Innovation
Establish a continuous evaluation framework to monitor long-term impact on learning outcomes and educational equity. Foster research and development for new GAI functionalities, ensuring adaptability to evolving educational needs and technological advancements.
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