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5USING ARTIFICIAL INTELLIGENCE IN TEACHING ENGLISH VOCABULARY
АУГАМБАЙ ЖАНЕРКЕ МЫРЗАХМЕТКЫЗЫ
Абай атындагы ^азак ултты; педагогикалы; университетшщ «7M01703 - Шет тш» мамандыгыныц 2-курс магистранты, Алматы, ^азакстан
КЕМЕЛБЕКОВА ЗАДА АБЫЛБЕКОВНА
Филология гылымдарыныц докторы, кауымдастырылган профессор Абай атындагы ^азак ултты; педагогикалы; университетшщ шеттiлдер кафедрасы
Abstract. This article is devoted to the application of artificial intelligence (AI) technologies in teaching English vocabulary. In the context of the rapid development of technologies And their use in education is becoming an important tool for improving the learning process. The study analyzes the main methods of using AI, such as adaptive systems, applications and chatbots. The results of an experiment conducted with school-gymnasium #30, 10-11th grade students demonstrating the effectiveness of using AI for learning are also presented. Based on the data analysis, practical recommendations are proposedfor integrating AI into educational processes aimed at improving the development of English vocabulary.
Keywords: artificial intelligence, language learning, vocabulary, adaptive systems, chatbots, machine learning, linguistics, English learning, educational technologies, vocabulary learning, AI technologies.
Language learning is a complicated process with many variables that might result in a wide range of outcomes. The degree of acculturation, the quantity of intelligible information, the attention to second language traits and aspects, and the chances for meaningful negotiation and production all have an impact on language learning. [1, pp. 93] People deliberately work to improve their language skills and knowledge when they pick up new languages. In order to acquire the four fundamental skills—reading, writing, speaking, and listening—as well as associated skill elements, such pronunciation, this process entails comprehending language components, such as syntax and vocabulary. [2, pp. 82-84]
Existing evaluations on AI in language learning indicate that the development of chatbots, virtual reality settings, writing assistants, tutoring systems, and other adaptive learning software and systems has received a lot of attention. [3, pp. 190-205] In order to maximize language learning by boosting autonomy, motivation, engagement, and effectiveness, these technologies have mostly been designed to create individualized and adaptable learning experiences. [4, pp. 16-20] NLP-based tutoring systems, for example, are made to offer resources, suggestions, and comments that are specific to each student. These technologies may now precisely adjust information in real-time to each user's learning rate, preferences, and requirements (such as cognitive, affective, and social) because to the quick growth of AI in recent years. [5, pp. 135-153]
Modern artificial intelligence (AI) technologies are increasingly being used in various fields of human activity, including education. In the field of learning foreign languages, AI is able to offer new teaching methods, especially in terms of vocabulary expansion, which is a key component of language acquisition. Vocabulary is the basis of language competence, which is necessary for successful communication, reading, writing and understanding in a foreign language. Learning new words and their active use often becomes a problem for students, especially when it comes to independent work and lack of practice in a natural language environment.
The object under study concerns the use of AI to improve the process of learning English, in particular, its lexical component. Traditional teaching methods (memorization of words, dictation, translation cards, etc.) often do not take into account the individual needs of students and do not adapt to their level of knowledge and progress. AI offers innovative solutions that can eliminate many of the problems associated with traditional methods.
The purpose of this study is to study the effectiveness of using artificial intelligence technologies to teach English vocabulary among students of Kazakhstani universities, compare various methods and suggest optimal ways to implement them in educational programs.
The use of AI in education has the potential to revolutionize the process of learning foreign languages by offering students more adapted and effective ways to memorize and use new words. In Kazakhstan, where English is becoming more and more in demand, especially in the academic and professional fields, the study of this topic is relevant and shows the importance of the research. Understanding how AI can be used to improve lexical skills is key to improving the quality of education.
The novelty of this study lies in the fact that it focuses on the study of the use of AI in the context of the Kazakh educational system, which has not yet fully integrated modern technologies into the learning process. While in some Western countries the use of AI for educational purposes has already become common practice, in Kazakhstan these technologies are just beginning to be implemented. This study provides a unique opportunity to evaluate how AI can be used to improve the effectiveness of English language teaching in Kazakh universities, and also offers specific recommendations for integrating these technologies into curricula.
Moreover, the study considers the use of AI not only as an auxiliary tool, but also as a means for a radical transformation of the educational process. AI systems can offer new approaches to learning that make the learning process more interactive and effective, which opens up new prospects for teaching foreign languages in general.
One of the main problems in learning English vocabulary is the inefficiency of traditional teaching methods. Students often face difficulties in memorizing and using new words, especially in situations where they do not have the opportunity to apply them in a natural language environment. This leads to "passive" word knowledge: students can recognize words on paper, but cannot actively use them in speech or writing. Traditional methods also often do not take into account the individual differences of students, such as their level of training, pace of learning and motivation.
The solution to this problem is the use of AI technologies that can adapt curricula to each student. AI systems can track students' progress and automatically suggest tasks that match their current level of knowledge. For example, adaptive machine learning systems can analyze which words a student remembers faster and which require more repetition. This allows you to create a more effective and personalized learning program that not only improves the memorization of words, but also promotes their active use.
The study questions, our methodology, and the guidelines for the systematic review are all covered in the following section. After that, we give the results of our analysis of the pertinent literature. We wrap up by talking about our goals for more study.
Methods
This study aims to spot patterns in the creation of AI-based language learning resources and provide detailed information on tools that can help in enhancing vocabulary. From a theoretical area, artificial intelligence (AI) has developed into a game-changing technology that is changing a number of sectors, including education. Enhancing learning outcomes, automating administrative duties, and providing individualized learning experiences are the main goals of AI integration in education. [6, pp. 112-116] In language learning, where data-driven teaching tactics, adaptive learning paths, and individualized feedback are critical to achieving language competency, the application of AI-driven solutions has had a particularly significant influence.
The use of AI for individualized learning has been one notable development. Artificial intelligence (AI) systems in education can evaluate students' prior knowledge, learning style, and particular learning difficulties, as explained by Zawacki-Richter et al. [7, pp. 62-68] By adapting instructional materials appropriately, these tools provide a personalized learning experience for every student. AI can identify mistake patterns in language acquisition and recommend specific workouts to improve weaker areas.
A branch of artificial intelligence called natural language processing (NLP) studies how computers and human languages interact. It enables meaningful and practical computer processing and comprehension of human language. [8, pp. 6-8] NLP has made it possible to develop systems for teaching vocabulary that mimic real-world language usage, offer context-specific feedback, and involve students in practical applications.
Duolingo, which use AI-powered algorithms to teach vocabulary in context, is a well-known example of NLP in vocabulary instruction. [9, pp. 113-120] Based on the learner's learning history and competency level, Duolingo's natural language processing technology can determine the best vocabulary for them. Furthermore, it reinforces meaning with context hints, allowing students to pick up terminology in more natural language contexts. For example, the platform helps learners understand word meanings in context by introducing vocabulary within conversations or dialogues rather than presenting terms separately.
Applications based on natural language processing also make use of speech synthesis and recognition technology. Through conversational interactions with digital texts, such as Google's "Talk to Books" tool, students can mimic normal speech patterns and improve their vocabulary retention. For students who might not have access to native English speakers, these resources are extremely helpful since they let them practice and improve their vocabulary in authentic conversational contexts. For example, Babbel adapts its vocabulary lessons according to how well students understand specific terms using a combination of natural language processing (NLP) and machine learning. It monitors the frequency of accurate word and phrase recall by users, modifying the frequency of repetition for words that learners find difficult. [10, pp. 21-29]
Another significant use of AI in education is Intelligent Tutoring Systems (ITS). By imitating the techniques and strategies used by human tutors, ITS are intended to deliver individualized teaching. [11, pp. 212-215] These tools provide customized feedback, track student achievement, and adjust to individual learning requirements. When it comes to vocabulary learning, ITS can pinpoint problem regions and offer extra practice, clarifications, or different teaching methods.
RoboTutor, a platform created by Carnegie Mellon University, is an example of an effective use of ITS in vocabulary learning. [12, pp. 65-73] RoboTutor has been very successful in classrooms with limited resources. It tracks the learner's progress using AI algorithms and provides scaffolding that adapts in real time as the learner's vocabulary abilities advance or deteriorate. Additionally, the system offers instantaneous remedial feedback, which is essential for assisting students in internalizing and promptly fixing their errors.
Additionally, ITS can use gamified components to inspire students. AI-powered tutoring systems can boost learner motivation - a crucial component of vocabulary retention by integrating features like badges, prizes, and progress tracking. As reported by Luckin [13, pp. 120-123], students using ITS for vocabulary learning often outperform their peers in terms of engagement and retention.
By deliberately lengthening the time between review sessions, spaced repetition an evidence-based learning strategy - improves long-term memory retention. [14, pp. 71-85] This strategy has been successfully applied by AI-driven programs like Memrise and Anki, which use algorithms to identify the best times for students to review vocabulary items. Artificial intelligence (AI) systems evaluate student performance and modify the repetition schedule according to the vocabulary's level of difficulty and the learner's recall success rate.
Example in Action: Anki's algorithm for spaced repetition adapts its flashcard reviews according to user input. The algorithm lengthens the review period for a word if a student successfully remembers it on a regular basis. On the other hand, a term that the student finds difficult will reappear more frequently. [15, pp. 44-51] By strengthening challenging terms and lessening the cognitive strain on previously learned vocabulary, this approach maximizes the learning process.
According to research by Kang (2016), students who employed spaced repetition systems outperformed those who employed conventional memory strategies in terms of retention rates. The extra advantage of these AI-powered systems is that they can customize the intervals for each student, increasing the effectiveness and sustainability of vocabulary acquisition. [16, pp. 998-1005]
Another area in which AI has advanced significantly is gamification, or the application of game aspects outside of gaming environments. AI is used by programs like Duolingo (Appendix 1) and Quizlet (Appendix 2) to modify gamified components, such as points, levels, and prizes, according to each learner's progress and degree of engagement. [17, pp. 75-82] By presenting tasks that are suitably matched to their ability levels, these systems maintain learners' motivation.
Duolingo has succeeded as much as possible, both with the development and recruitment of users, and in the development of gamification within the application. I think Duolingo can be considered an example of the most gamified learning application. The main idea behind all mechanics is the idea of flow. While practicing in Duolingo, the user should feel the flow, that each of his actions leads to a noticeable and useful result (obviously not to a result in learning, it is not so easy to feel it). Each user's action is justified in some way, he has both long-term goals in terms of training and short-term goals - in achieving a level and in competition with other users, in earning bonuses, etc. All this creates the feeling that, on the one hand, I'm doing pretty routine things - learning words, going through mini-exercises, but on the other hand, it's like I'm playing a game. In this game, I have my own parallel goals and rewards. See Picture 1 for app interface.
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Picture 1. Duolingo interface
On the Quizlet platform, users can explore areas of interest to them and check how they have learned the material in various interactive ways:
• To train with the help of digital cards;
• Check what you have learned in class through competitions in Quizlet live and in the "Check" mode (individually or in teams);
• Test yourself independently through quizzes with various formats;
• Learn the material through mini-games and other platform modes that allow the student to train their knowledge in different formats. (Picture 2)
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Picture 2. Quizlet interface
It has been demonstrated that AI-powered gamification in vocabulary learning increases learner perseverance and enjoyment, two crucial components for long-term retention, by offering real-time performance metrics and individualized feedback. [18, pp. 146-173]
The benefits of AI in teaching go beyond vocabulary acquisition. For example, AI systems provide real-time grammar and syntax feedback in writing and comprehension activities, which is essential for non-native English speakers. As AI systems get better at comprehending natural language input, they can provide students with immediate remedial feedback.
In conclusion, the research demonstrates a multitude of AI applications in the study of English vocabulary, ranging from ITS like RoboTutor that provide individualized, adaptive learning experiences to NLP-powered platforms like Duolingo that contextualize language. While AI's connection with gamified platforms boosts motivation, its ability to apply spaced repetition techniques improves retention. But even with the obvious advantages, problems like unequal access to technology and the requirement for teacher training still exist and require more research. [19, pp. 32-33]
Design of Research
This study's research methodology, sometimes referred to as a mixed-methods design, blends qualitative and quantitative techniques. This methodology was used in order to obtain a thorough grasp of how artificial intelligence (AI) affects the acquisition of English vocabulary from both an objective (measurable results) and subjective (experiential) standpoint. The conclusions reached are guaranteed to be based on both theoretical research and practical applications thanks to a combination of empirical data collecting and literature evaluation.
Interviews with English language teachers and students utilizing AI-powered resources in their vocabulary teaching and learning processes comprised the qualitative component. In the meantime, the quantitative component entailed examining the performance data from these AI-powered platforms, particularly calculating the rate of engagement and vocabulary retention over a predetermined time frame.
Selection criteria for tools: popularity, the availability of usage data, and the existence of AI-driven features were taken into consideration when choosing the tools. These platforms were evaluated in light of:
• Vocabulary retention effectiveness: measured using each platform's built-in statistics (e.g., the review intervals in Babbel or Anki).
• User engagement metrics: Tracked using platform data to assess how frequently learners engage with the vocabulary lessons.
There were two main parts to the data collection process:
1. Survey of AI resources for teaching English vocabulary - a thorough examination of the current AI-based resources frequently used to teach vocabulary was carried out. These included programs that employ natural language processing (NLP) and machine learning to customize vocabulary education, such as Babbel, Memrise, Anki, and Duolingo. Every platform was analyzed according to its features, degree of customization, feedback frequency, and incorporation of spaced repetition strategies.
2. Qualitative interviews - ten English language teachers and fifteen students who have been actively using AI technologies for vocabulary teaching and learning for at least six months participated in semi-structured interviews. The following were the main topics of the interview questions:
• Teachers' opinions on how well AI tools work to increase students' engagement and language retention.
• The students' perceptions of feedback, personalization, and the frequency of spaced repetition in their use of AI-powered platforms.
The sample questions of the survey given in Table 1 below.
1. What differences have you observed in vocabulary retention after introducing AI tools into your learning/teaching?
2. How do you perceive the level of feedback and personalization provided by the AI tools compared to traditional methods?
3. What AI tools you found the most effective in vocabulary learning?
4. What obstacles did you face while working with AI tools?
Table 1
Sample questions of the survey
The study focused on educational institutions that had already incorporated AI-powered vocabulary-teaching tools into their curricula.
Teachers: ten English language teachers were selected, each with an average of eight years of classroom experience. For at least six months, all had been using AI-based vocabulary teaching resources in their classes.
Learners: from a variety of skill levels (A1 to C1 on the CEFR scale), 15 learners were chosen. For over six months, each student had been using AI tools to increase their vocabulary.
Results
Quantitative Analysis: AI Tool Performance Data
Learner engagement for 8-week period and vocabulary retention rates were the two main indicators that were the focus of the quantitative investigation. Three popular AI-powered platforms - Duolingo, Memrise, and Anki were the sources of the data. The outcomes are shown below in Table 2.
Using these AI technologies, we monitored the vocabulary retention rates of fifty students. The percentage of accurately recalled words following repeated exposure during learning sessions served as a proxy for the retention rate.
Week Duolingo (%) Memrise (%) Anki (%)
1 62 60 58
2 67 64 61
3 73 70 67
4 78 75 72
5 82 79 76
6 85 83 80
7 88 85 83
8 89 86 84
Table 2
Vocabulary Retention Rates for 8 Weeks
The retention rates of all three platforms increased steadily, but Anki's beginning rates were marginally lower than those of Duolingo and Memrise.
By the end of the 8 weeks, Duolingo had the highest retention rates (89%), closely followed by Memrise (86%) and Anki (84%) in that order. Learner engagement was tracked by the number of lessons completed per week and average time spent on the platform per session. The results are shown in Diagram 1.
12345678
■ Duolingo ■ Memrise Anki
Diagram 1. Lessons completed per week
All platforms had a steady increase in engagement, but by week 8, Duolingo had the most courses completed and the longest session duration. In terms of engagement, Anki initially behind the other platforms but demonstrated steady improvement.
Qualitative Analysis: Thematic Insights from Interviews
Based on participant replies, thematic analysis was used to identify important themes in the qualitative data. The following were the most recurring themes found in the interviews:
1. Better retention of vocabulary: when utilizing AI-based technologies instead of conventional techniques, both students and teachers reported observable increases in vocabulary retention. According to learners, AI systems' use of spaced repetition strengthened new vocabulary and made it simpler to remember words after a few days.
2. A rise in motivation and engagement: participants underlined that learner motivation was raised by the gamification features of websites such as Memrise and Duolingo. Badges, levels, and challenges were used to make learning more fun and to motivate students to continue studying consistently.
3. Issues with Usability and Access: the usability of some AI technologies and availability to technology were mentioned as problems by some students and teachers. For example, not all students had reliable access to cellphones or fast internet, which hindered their capacity to make good use of AI tools.
Recommendations for Students and Teachers
To maximize the benefits of AI-powered tools for English vocabulary acquisition, tailored strategies for both students and teachers can enhance engagement, retention, and overall learning outcomes. Suggestions for students and teachers you can see on Diagram 2 and Diagram 3 below.
1. Leverage spaced repetition for long-term retention
• Students should prioritize tools that incorporate spaced repetition systems (SRS), like Anki and Memrise, which are proven to reinforce vocabulary over time by strategically scheduling reviews.
2. Utilize gamification features to build habits
• Tools like Duolingo and Memrise use game-based elements that increase motivation. Students are encouraged to take advantage of these features - daily streaks, achievement badges, and challenges - to develop consistent study habits, which are essential for vocabulary retention.
3. Combine AI tools with contextual learning
• While AI tools are excellent for building foundational vocabulary, students should also try to incorporate new words into real-life contexts. Writing sentences, participating in conversations, and watching English media can complement AI-driven learning and deepen word understanding.
4. Set personalized learning goals
• Setting clear, measurable goals within AI platforms allows students to track their progress and stay motivated. These goals should be realistic and based on individual learning speeds, which AI platforms like Babbel can support through personalized feedback and adaptive lesson pacing.
5. Review performance analytics regularly
• Most AI vocabulary tools provide analytics on user progress and areas that need improvement. Reviewing these insights weekly can help students identify weaker areas and adjust their study strategies for better outcomes.
Diagram 2. Recommendations for students
1. Integrate AI tools into classroom activities
• Teachers can use AI vocabulary tools as part of classroom activities, incorporating these digital tools into daily exercises or homework assignments. This will help students apply vocabulary they learn in-app to real-life scenarios, enhancing practical understanding.
2. Encourage students to set personal learning plans
• Teachers should guide students in setting realistic goals for vocabulary acquisition and learning frequency within the AI platforms. Setting targets helps students stay focused and can make vocabulary acquisition feel achievable and measurable.
3. Use performance data to tailor instruction
• Many AI platforms provide insights into student progress and areas for improvement. Teachers can use this data to identify common vocabulary challenges and adapt lessons to address these gaps, ensuring that students receive focused support.
4. Provide structured feedback and reinforcement
• While AI tools often give instant feedback, teachers can deepen this by discussing vocabulary challenges in class and providing contextual examples. Structured feedback on AI-driven exercises reinforces students' understanding and provides additional context that AI platforms may not cover.
5. Foster collaborative learning opportunities
• AI tools can sometimes make learning feel isolated. Teachers should consider creating group activities where students use new vocabulary in discussions or group exercises. Collaborative activities can help students apply vocabulary in more interactive, conversational settings, enhancing both engagement and retention.
6. Encourage responsible use of technology
• While AI tools are beneficial, over-reliance on them may limit students' exposure to other vocabulary-building strategies. Teachers should encourage a balanced approach, suggesting a mix of AI-based and traditional learning techniques to foster a more well-rounded language skill set.
Diagram 3. Recommendations for teachers Conclusion
The study emphasizes how artificial intelligence (AI) techniques have a great deal of promise to improve vocabulary instruction in English. It is clear from the examination of quantitative data and qualitative insights that AI-powered learning platforms such as Duolingo, Memrise, and Anki have a quantifiable positive effect on learner engagement and vocabulary retention. With Duolingo showing the highest retention rates, the use of spaced repetition and tailored feedback has proven very successful in helping learners retain vocabulary over time.
Additionally, the gamification elements incorporated into these platforms boost student engagement and motivation, promoting regular use and a greater level of immersion in the language-learning process. Nonetheless, the study also highlights the difficulties with usability and accessibility, since certain students encounter obstacles related to technology and reliable internet connections.
In conclusion, even if AI tools are effective tools for learning vocabulary, optimizing their use in language instruction will need resolving concerns of fair access and offering assistance to students in a variety of settings.
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