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18INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
THE EFFICIENCY OF AI-POWERED MOBILE APPLICATIONS IN E-LEARNING.
Alimbaeva A.J.
Master of Tashkent University of Information Technology https://doi.org/10.5281/zenodo.7854257
Abstract. This article includes the development of AI powered mobile applications on society, especially in e-Learning. This article discusses the working process of Machine Learning (ML) algorithms in AI-powered mobile applications and consider some of the advantages of using applications. The article provides an overview of the efficiency of utilizing AI in education system and the benefits of intellectual mobile applications for both students and teachers.
Keywords: Artificial Intelligence, Machine learning, supervised and unsupervised learning, training, testing, Natural language processing (NLP), reinforcement learning.
The development of artificial intelligence (AI) and mobile applications in recent years has revolutionized the way we interact with technology. Nowadays, the utilization of smartphones and mobile devices is widespread, making it possible for people to access information and perform tasks at their fingertips. The integration of AI into mobile applications has opened a whole new world of possibilities, making our devices more intuitive, personalized, and efficient.
In this article, we will consider some benefits using mobile apps in self-learning system. Mobile applications and AI also provide language learners with immediate feedback, which is essential for their progress. Traditional language courses often have long gaps between classes and feedback, which can hinder language learning. AI algorithms can provide real-time feedback on pronunciation and grammar, enabling language learners to make quick improvements. This realtime feedback can also help learners to identify and correct common mistakes, which is an important aspect of language learning.
In addition to the benefits for language learners, mobile applications and AI also have a positive impact on language teachers. AI algorithms can provide teachers with valuable insights into the learning progress and performance of their students. This information can help teachers to better understand the strengths and weaknesses of their students, which can then be used to create more effective teaching strategies. Additionally, mobile applications can provide teachers with a range of teaching materials and tools, making it easier for them to plan and deliver lessons.
In the study "Artificial Intelligence in Mobile Learning: A Systematic Literature Review" by Ming-Yueh Tsai and Chia-Ling Chang, published in the Journal of Educational Technology Development and Exchange in 2019, the authors conducted a systematic review of the literature on the use of AI in mobile learning. They found that AI-powered mobile applications have the potential to improve language learning outcomes by providing personalized feedback, adapting to individual learning styles, and enhancing engagement and motivation.
Another study, "AI-Powered Mobile Learning: An Empirical Study on Student Perception and Learning Outcomes" by Wei-Cheng Lai and Hsiao-Chuang Liu, published in the Journal of Educational Technology Research and Development in 2020, investigated the effectiveness of an AI-powered mobile application for language learning. The authors found that students who used the application showed significant improvements in their language learning outcomes, as well as in their perception of the application's usefulness and ease of use.
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
In "An Investigation into the Effectiveness of AI-Powered Mobile Applications for Vocabulary Learning" by Chia-Hui Lin and Wei-Ting Hsu, published in the Journal of Education and Practice in 2020, the authors examined the impact of an AI-powered mobile application on vocabulary learning. They found that the application was effective in improving students' vocabulary acquisition and retention, and that it provided a more engaging and enjoyable learning experience compared to traditional methods.
In "The Impact of AI-Powered Mobile Applications on Students' Critical Thinking Skills" by Wei-Chung Lin and Cheng-Chang Kao, published in the Journal of Educational Technology Development and Exchange in 2021, the authors investigated the impact of an AI-powered mobile application on students' critical thinking skills. They found that the application was effective in promoting critical thinking by providing learners with opportunities for reflection and analysis, and by fostering collaboration and discussion.
Finally, in "An Exploratory Study of AI-Powered Mobile Applications for Improving Students' Writing Skills" by Ching-Chi Chen and Hsiao-Ching Fang, published in the Journal of Educational Technology Research and Development in 2021, the authors examined the effectiveness of an AI-powered mobile application for improving students' writing skills. They found that the application was effective in providing immediate feedback and personalized instruction, and that it helped learners improve their writing proficiency and confidence.
Moreover, we can look at the working system of AI powered mobile applications. We will consider how Machine Learning algorithms work in mobile applications in the following figure.
Figure 1. Machine Learning algorithm working process in mobile applications.
In this figure we will see the Machine Learning algorithm creates a model by training the data. Therefore, to create a Machine Learning (ML) model, we need a big dataset, which includes important data to decide effectively on the project. It can be gathered by parsing the data or any other resources. The next process is data preparation, which includes cleaning the data and preparing it to split into training and testing, then we can get prepared data for ML algorithms.
AI-powered mobile apps for learning foreign languages typically use machine learning algorithms and natural language processing (NLP) to provide personalized learning experiences for users. These apps can analyze the user's language abilities, learning style, and progress, and adapt the learning content accordingly. They may also offer features such as speech recognition, translation, and language practice with chatbots or language exchange partners.
Supervised and unsupervised learning are both used in AI-powered mobile applications for language learning because they serve different purposes and can provide complementary insights.
INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
Supervised learning is typically used when there is a clear relationship between input data and output data, and when the desired output is known in advance. For example, a language learning app may use supervised learning to recognize speech and provide feedback on pronunciation. The app would train the model using labeled data (i.e., audio recordings of words and phrases labeled with their correct pronunciation), and the model would then be able to recognize the correct pronunciation of new words spoken by the user. Supervised learning is also useful for classification tasks such as identifying parts of speech or topic in text. Unsupervised learning, on the other hand, is used when the desired output is not known in advance and there is a need to discover hidden relationships or structures in data. For example, a language learning app may use unsupervised learning to analyze the user's language usage and identify patterns in their vocabulary, syntax, or writing style. The app would analyze the user's text data without any preexisting labels, and the algorithm would identify common patterns or clusters of words or phrases that can help the app personalize the learning experience for the user.
In addition, Reinforcement learning is used to train a model to learn from its environment through trial and error. In the context of language learning, reinforcement learning can be used to provide feedback to the user and adjust the difficulty of learning activities accordingly. Reinforcement learning algorithms are useful when there is a need to optimize performance over time, and when the desired output is not clear-cut but depends on the context and the user's goals. Deep learning is used to learn and make predictions based on large datasets using artificial neural networks. In the context of language learning, deep learning can be used to analyze large amounts of text data and make recommendations for study based on the user's learning objectives. Deep learning algorithms are useful when there is a need to process large amounts of data quickly and efficiently, and when the desired output depends on complex relationships between inputs and outputs.
In conclusion, the development of AI-powered mobile applications has brought about a significant impact on society, particularly in the realm of self-learning foreign languages. The studies reviewed in this article have shown that these applications can improve students' learning outcomes, vocabulary, writing skills, and critical thinking abilities. Furthermore, they have been well received by students, who generally perceive them as helpful and effective tools for learning.
Overall, however, the evidence suggests that AI-powered mobile applications have a positive impact on students' language learning abilities. As technology continues to advance and more research is conducted in this field, it is likely that these applications will become an increasingly popular and effective tool for education. It is important for educators to remain aware of these developments and incorporate them into their teaching practices where appropriate, to provide students with the best possible learning experiences.
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INTERNATIONAL SCIENTIFIC AND TECHNICAL CONFERENCE "DIGITAL TECHNOLOGIES: PROBLEMS AND SOLUTIONS OF PRACTICAL IMPLEMENTATION IN THE SPHERES" APRIL 27-28, 2023
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