Ethics of Artificial Intelligence in Education: Balancing Automation and Human-Centered Learning
Apr 11, 2025
About this article
Published Online: Apr 11, 2025
Received: Nov 24, 2024
Accepted: Feb 27, 2025
DOI: https://doi.org/10.2478/amns-2025-0843
Keywords
© 2025 Jie Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Computational Efficiency Evaluation
| Model | Inference Time(ms) | Memory Usage(MB) |
|---|---|---|
| Rule-Based | 5.2 | 120 |
| Supervised Learning | 11.8 | 300 |
| RL-Based (Ours) | 8.5 | 220 |
Fairness Improvement in Learning Recommendations
| Method | DIR Before | DIR After |
|---|---|---|
| Baseline AI Model | 0.72 | - |
| Fairness-Aware Embeddings | 0.81 | 0.91 |
| Reweighting Approach | 0.79 | 0.89 |
Evaluation of Ethical AI Components in Education
| Metric | Baseline | NLP Bias Correction | RL Optimization | XAI | Final Framework |
|---|---|---|---|---|---|
| BRS(%) | 52.3 | 68.1 | 74.5 | 78.3 | 85.2 |
| II (0-1) | 0.42 | 0.58 | 0.63 | 0.81 | 0.89 |
| PA (%) | 64.7 | 72.1 | 85.3 | 87.5 | 90.8 |
| SSS (1-10) | 5.8 | 7.1 | 7.9 | 8.6 | 9.2 |
| DPS (%) | 79.5 | 82.2 | 85.0 | 88.7 | 92.3 |
Summary of Experimental Dataset
| Feature | Value | Description |
|---|---|---|
| Number of students | 50,000 | Learners across different subjects |
| Number of interactions | 5.2M | Clicks, session durations, quiz results |
| Number of textual feedback | 100K | Student reflections and teacher comments |
| Number of knowledge graph nodes | 20K | Concepts and relationships in various subjects |
Personalization and Adaptability Evaluation
| Model | PA(%) | ER(%) | SSS(1-10) |
|---|---|---|---|
| Rule-Based | 64.3 | 68.2 | 6.5 |
| Supervised Learning | 78.1 | 75.6 | 7.9 |
| RL-Based(Ours) | 90.4 | 89.1 | 9.1 |