Application of Artificial Intelligence to IELTS Learning †
Abstract
:1. Introduction
2. AI in Language Learning and IELTS Preparation
2.1. Introduction
2.2. Personalized Learning and Motivation
2.3. Supporting Autonomous Learning
2.4. AI-Driven Feedback Mechanisms
3. Educational Psychology in AI-Enhanced IELTS Preparation
3.1. Autonomy and Learner Control
3.2. Psychological Impact of AI-Generated Feedback
4. Challenges in AI-Enhanced Language Learning
4.1. Reduced Human Interaction and Emotional Support
4.2. Cultural Relevance in AI Learning
4.3. Over-Reliance on AI and Reduced Interpersonal Skills
5. Opportunities for Future Development in AI-Enhanced IELTS Preparation
5.1. Enhancing Feedback
5.2. Integration of Emotional AI
5.3. Cultural Adaptation in Content and Interaction
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Feature | Description |
---|---|
Free Exploration | AI platforms enable learners to explore content at their own pace, choosing topics and areas of focus according to their interests. This approach promotes engagement, curiosity, and allows learners to connect with the material in a more meaningful way, fostering a deeper understanding and increasing motivation. |
Gesture Interaction | Through AI and gesture-recognition technology, learners interact with virtual study environments by moving, selecting, or manipulating objects. This technology supports more immersive learning experiences, allowing students to interact with study materials in innovative ways that enhance understanding and engagement. |
Physical Interaction | AI tools offer sensor-equipped interfaces that allow learners to engage in a physical manner, such as typing, speaking, or using digital pens. This interactive approach aids in kinesthetic learning, helping students to better absorb information by involving physical actions, thus enhancing retention and comprehension. |
Multi-Person Collaboration | AI-facilitated virtual environments support collaborative learning, allowing multiple users to work together on shared tasks or exchange ideas. This setup encourages teamwork and peer learning, providing students with the social interaction that reinforces motivation and helps build communication skills necessary for collaborative work. |
Visual Feedback | AI systems provide immediate visual feedback through animations, charts, and highlights, helping students understand their performance in real-time. This visual reinforcement is especially useful in language learning, where it helps learners identify areas for improvement and encourages iterative progress without needing to wait for teacher feedback. |
Supporting Autonomous Learning | AI platforms empower learners with autonomy, allowing them to manage study schedules, select content, and focus on specific weaknesses. Tools like vocabulary apps adapt to individual performance, helping students take control of their learning journey, aligning with educational psychology principles that value self-motivation and independence. |
Feature | Benefits | Suggestions for Improvement |
---|---|---|
Immediate Feedback | Enables quick correction and iterative learning. | Add context-based suggestions to enhance feedback depth. |
Real-Time Responses | Promotes immediate learning and positive reinforcement. | Offer pacing options for feedback to suit student preferences. |
Error Correction | Focuses on specific errors for targeted improvement. | Provide detailed explanations with examples for better understanding. |
Empathy in Feedback | Encourages motivation and resilience. | Simulate empathetic responses to support student efforts. |
Feedback Interpretation | Builds self-reliance in advanced learners. | Add guided interpretations for clearer understanding. |
Skill Development | Supports frequent practice and self-assessment. | Balance with occasional human feedback for comprehensive learning. |
Psychological Support | Enhances motivation and reduces test anxiety. | Include motivational components and adaptive learning paths. |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hou, Y.; Assim, M.I.S.A.; Taasim, S.I. Application of Artificial Intelligence to IELTS Learning. Eng. Proc. 2025, 89, 20. https://doi.org/10.3390/engproc2025089020
Hou Y, Assim MISA, Taasim SI. Application of Artificial Intelligence to IELTS Learning. Engineering Proceedings. 2025; 89(1):20. https://doi.org/10.3390/engproc2025089020
Chicago/Turabian StyleHou, Yu, Mohamad Ibrani Shahrimin Adam Assim, and Shairil Izwan Taasim. 2025. "Application of Artificial Intelligence to IELTS Learning" Engineering Proceedings 89, no. 1: 20. https://doi.org/10.3390/engproc2025089020
APA StyleHou, Y., Assim, M. I. S. A., & Taasim, S. I. (2025). Application of Artificial Intelligence to IELTS Learning. Engineering Proceedings, 89(1), 20. https://doi.org/10.3390/engproc2025089020