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Ethics of Artificial Intelligence in Education: Balancing Automation and Human-Centered Learning

Jie Zhang

Jie Zhang

Xi'an International UniversityShaanxi, China
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Zhang, Jie
  
11 avr. 2025
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Publié en ligne: 11 avr. 2025
Reçu: 24 nov. 2024
Accepté: 27 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0843
Mots clés
© 2025 Jie Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Proposed Multimodal Decision Framework integrating NLP, RL, and XAI for ethical AI in education.
Proposed Multimodal Decision Framework integrating NLP, RL, and XAI for ethical AI in education.

Figure 2.

Bias reduction score improvement with different AI components.
Bias reduction score improvement with different AI components.

Figure 3.

Fairness improvement using different bias mitigation techniques.
Fairness improvement using different bias mitigation techniques.

Figure 4.

Comparison of personalization metrics across different models.
Comparison of personalization metrics across different models.

Figure 5.

Inference time comparison for real-time deployment.
Inference time comparison for real-time deployment.

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
Langue:
Anglais
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Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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