Educational Technology Evaluation
Analytic Hierarchy Process on the Application of AI in College Students' English Learning
This study utilized the Analytic Hierarchy Process (AHP) to assess AI applications in college students' English learning, revealing that learning effectiveness (0.32) and personalization (0.27) are the most critical factors. Adaptive learning systems (0.38) and oral practice platforms (0.27) emerged as top solutions, providing a scientific basis for optimizing AI integration in English education.
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0.32
Learning Effectiveness Weight
0.27
Personalization Weight
0.38
Adaptive Learning Systems (C3) Overall Weight
Deep Analysis & Enterprise Applications
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0.32
Learning Effectiveness Weight
Enterprise Process Flow
Literature Review & Expert Interviews
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Construct Hierarchical Model
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Design Pairwise Comparison Matrices
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Data Collection & Weight Calculation
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Consistency Check & Analysis
| Tool | Strengths | Weaknesses |
|---|---|---|
| Duolingo |
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| ELSA Speak |
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| ChatGPT (for ELL) |
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0.27
Personalization Weight
Impact of Adaptive Learning Systems
Adaptive learning systems (C3) were found to be the most effective overall, with a total weight of 0.38. They excel in adjusting to individual student paces and providing customized feedback, directly addressing the core needs of personalized English language acquisition for college students. This leads to higher engagement and improved learning outcomes compared to traditional methods.
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