INTEGRATING AI DIGITAL TOOLS INTO EFL COURSES: IMPACT ON LEARNER ENGAGEMENT ACROSS AGE GROUPS

ABSTRACT

This article explores how Artificial Intelligence (AI)-supported applications can be used in English as a Foreign Language (EFL) lessons with learners of different age groups. The purpose of the study is to group selected tools according to the main language skill or classroom function they support and to examine their practical value in school teaching. The analysis focuses on seven applications: Suno AI, ELSA Speak, Gliglish, AI Text Leveler, Grammarly, Quizlet, and Online Test Pad. These tools are considered in relation to listening, pronunciation, speaking, reading, writing, vocabulary work, and formative assessment. The study is based on descriptive analysis, classroom observation, and questionnaire responses collected during EFL lessons. The findings show that AI-supported tools are most effective when they are linked to a specific lesson objective, provide prompt feedback, and correspond to the age and proficiency level of learners. The article argues that such applications should be viewed not as universal solutions, but as focused pedagogical instruments for more meaningful and engaging language learning.

АННОТАЦИЯ

В статье рассматривается, как приложения с поддержкой искусственного интеллекта (ИИ) могут использоваться на уроках английского языка как иностранного с учащимися разных возрастных групп. Цель исследования — сгруппировать выбранные инструменты в соответствии с основным языковым навыком или учебной функцией, которую они поддерживают, и определить их практическую ценность в школьном обучении. Анализ сосредоточен на семи приложениях: Suno AI, ELSA Speak, Gliglish, AI Text Leveler, Grammarly, Quizlet и Online Test Pad. Эти инструменты рассматриваются в связи с аудированием, произношением, говорением, чтением, письмом, работой над словарным запасом и формативным оцениванием. Исследование основано на описательном анализе, наблюдении за уроками и ответах анкетирования, собранных в ходе занятий по английскому языку. Результаты показывают, что инструменты с поддержкой ИИ наиболее эффективны тогда, когда они связаны с конкретной целью урока, обеспечивают быструю обратную связь и соответствуют возрасту и уровню владения языком учащихся. В статье обосновывается, что такие приложения следует рассматривать не как универсальные решения, а как целенаправленные педагогические инструменты для более осмысленного и вовлекающего изучения языка.

Keywords: Artificial Intelligence, EFL lessons, AI-supported applications, language skills, classroom observation, learner engagement, formative assessment.

Ключевые слова: Искусственный Интеллект, уроки английского языка, приложения с поддержкой ИИ, языковые навыки, наблюдение за уроком, вовлечённость учащихся, формативное оценивание.

Introduction

The spread of Artificial Intelligence in education has changed not only the range of digital tools available to teachers, but also the way language practice can be organised in class. In EFL teaching, AI-supported applications now allow teachers to vary input, adapt tasks more quickly, and offer learners a more responsive type of practice. This is especially noticeable while working with listening, pronunciation, speaking, reading, writing, vocabulary revision, and short assessment tasks. At the same time, the key issue is no longer the simple presence of technology in the lesson. A more important question is how a particular tool serves the language aim of the activity and whether it genuinely supports learning rather than distracts from it [1; 3; 5].

The relevance of this topic is also connected with the learning habits of today’s school students. Many of them are used to fast feedback, visual prompts, short interactive tasks, and digital environments in which they can act immediately. Because of this, AI-based applications are often accepted by learners quite naturally during EFL lessons. However, interest alone does not guarantee educational value. Digital tools only become pedagogically meaningful when they help learners stay focused on the target language and take part in purposeful classroom interaction [1; 13].

Domestic and international studies point to the same general tendency: Artificial Intelligence can support language teaching, but its usefulness depends on the teacher’s decisions. Kuvshinova notes that such technologies can reduce routine work and diversify lesson tasks, while adaptation to Domestic contexts still remains uneven for some tools [13]. Holmes, Luckin, and Godwin-Jones similarly emphasise that AI has the strongest effect when it is connected to clear pedagogical goals, learner autonomy, and carefully planned classroom use [3; 4; 5].

Although interest in artificial intelligence has grown rapidly, teachers still lack sufficiently concrete classroom-oriented descriptions of which tools work best for which skill. In practice, applications that all fall under the label of AI may perform very different functions: one may support listening repetition, another may improve pronunciation, while the third one may help with text adaptation or quick assessment. For that reason, a skill-based and function-based classification is useful not only for description, but also for lesson planning. It helps teachers choose a tool for a reason instead of using it simply because it is new or popular.

The purpose of this study is to analyse the use of AI-supported applications in EFL lessons with learners of different age groups and to classify these applications according to the main language skill or pedagogical function they support. The study examines seven tools: Suno AI, ELSA Speak, Gliglish, AI Text Leveler, Grammarly, Quizlet, and Online Test Pad. Attention is given to the classroom role of each tool, its possible advantages for learner engagement, and its limitations in real teaching practice.

Materials and methods

The study was conducted at a private language centre where children, teenagers, and young adults study English in small groups. The participants were learners aged 7–20 working at A2–B2 levels according to the CEFR. Group size usually ranged from 6 to 7 students. The tools were observed in both face-to-face and online lessons, which made it possible to compare learner reactions in different formats. Participation was voluntary, and all questionnaire responses and observation notes were used in anonymised form.

Most of the tools selected for the study had an English-language interface. This was important for the classroom context because students encountered instructions, menu items, and prompts in the target language while completing tasks. In that sense, the interface itself became part of the learning environment. Online Test Pad differed from the other tools because it could be used in both Russian and English, which made it more flexible for instructions and quick checking tasks.

The seven selected applications were used for different purposes during lessons. Suno AI was chosen for listening activities because it can turn a short prompt, vocabulary set, or mini dialogue into a song that students can hear several times in a new format [6]. ELSA Speak was included in pronunciation work because it gives learners instant feedback on how clearly, they produce English sounds and words [7]. Gliglish was used for short speaking exchanges and role-play tasks, where students needed to react rather than simply repeat [8]. AI Text Leveler (Diffit) helped adapt reading material to learner level and therefore made texts more manageable for mixed groups [9]. Grammarly was used at the drafting and revision stage of writing tasks [10]. Quizlet supported vocabulary recycling through repeated practice and visible progress tracking [11]. Online Test Pad was used for quick quizzes, comprehension checks, and lesson revision [12].

To collect data, the study combined two sources: classroom observation and a short learner questionnaire. During observation, attention was paid to how actively students joined the task, how long they remained focused, whether they volunteered answers more readily, and how confidently they completed oral or written work. The questionnaire included short statements about interest, usefulness, clarity, motivation, and willingness to use similar tools again. Learners responded on a five-point Likert scale and also had an opportunity to add brief comments.

Results and discussion

The data were interpreted through descriptive analysis. In the context of this study, AI is understood as digital systems that can process language input, generate content, recognise patterns, and provide automated feedback in response to learner actions. First, the selected tools were grouped according to the language skill or classroom function they mainly supported. This created a practical framework for discussing the results shown in Table 1. (see Table 1) After that, questionnaire responses and lesson observations were compared in order to see whether student’s stated preferences were reflected in their actual classroom behavior. Particular attention was given to differences between tools that offered quick interaction and feedback and tools that required more self-regulation.

As shown in Table 1, the classification of tools reveals that AI in EFL is not a single method but a set of instruments with different pedagogical functions. Some tools primarily support language input, such as Suno AI for listening or Diffit for reading adaptation. Others support production and interaction, such as ELSA Speak and Gliglish for speaking, Grammarly for writing development, Quizlet for vocabulary learning, and Online Test Pad for formative assessment. This classification is important because learner engagement depends not simply on the novelty of technology, but on whether the selected tool matches a clear language objective and classroom procedure.

Table 1.

AI-supported digital tools used in EFL lessons

After establishing the classification of tools, the next step was to examine how students perceived different applications and how these perceptions were reflected in questionnaire data. Table 2 (see Table 2) summarises students’ evaluation of several tools used in practice.

Table 2 shows that students responded most positively to Suno AI and Quizlet, which were associated with repetition, memorability and visible progress. ELSA Speak also received a high level of approval because learners appreciated immediate pronunciation feedback. At the same time, reading tools and writing assistants were evaluated more cautiously. This suggests that students prefer AI tools that provide direct interaction, quick response and clear visible outcomes, whereas tools requiring more self-regulation may be perceived as useful but less engaging.

Table 2.

Results of the student questionnaire

A broader picture of student’s attitudes is presented in Table 3, (see Table 3) which summarises the mean scores and the percentage of agreement across key questionnaire statements.

Table 3.

 Students’ attitudes to AI-supported digital tools

As shown in Table 3, students demonstrated generally positive attitudes toward AI-supported activities. The strongest agreement concerned increased interest in lessons (4.6), vocabulary learning (4.5), listening practice (4.4) and willingness to continue using such tools in future lessons (4.7). The writing item received the lowest mean score (3.8), which corresponds with the more cautious attitudes reported in Table 2. This pattern suggests that students most strongly value AI when it supports immediate, visible and interactive practice rather than more complex writing processes.

The observation data provide further detail on how these attitudes were expressed in actual classroom behaviour. According to the classroom observation notes, listening tools based on AI-generated songs were especially effective with younger groups. When Suno AI was used to transform a vocabulary list or a short dialogue into a song, students listened several times without resistance, repeated phrases more willingly and noticed new lexical items in context. Compared to traditional listening tasks from the coursebook, these lessons produced more movement, more spontaneous repetition and a noticeably higher level of emotional involvement [6].

Speaking-related tools produced different but equally important effects. ELSA Speak helped learners concentrate on pronunciation accuracy and individual correction, which was especially useful for students who were shy about reading aloud in front of peers [7]. Gliglish, by contrast, supported more spontaneous speech because it simulated real conversational turns and encouraged learners to respond quickly rather than simply repeat [8]. In this sense, the combination of ELSA Speak and Gliglish worked productively: the first tool strengthened phonetic awareness, while the second moved students toward freer oral interaction.

Diffit and Quizlet supported reading and vocabulary engagement in a different way. Diffit made it easier to adapt texts to learner level, which helped students approach reading tasks with less anxiety and better comprehension support [9]. Quizlet, on the other hand, increased motivation during vocabulary practice because students could repeat items, track progress and return to difficult words several times [11]. In classroom practice, both tools worked best when they were integrated into a wider lesson sequence rather than used as isolated digital activities.

Grammarly was perceived ambivalently both in the questionnaire and in observation notes. Many learners appreciated the ability to generate ideas, reformulate sentences and correct grammar, especially at the drafting and revision stage [10]. However, some also showed a tendency to rely too heavily on automated suggestions. For this reason, classroom practice demonstrated that such tools are most effective when they are framed by clear rules: students should produce an initial draft independently and use AI primarily for revision rather than substitution.

Online Test Pad was particularly useful for formative assessment and revision. In classroom practice it helped maintain pace, allowed the teacher to check comprehension quickly and gave learners immediate feedback after listening or reading tasks [12]. At the same time, observation showed that quiz-based tools are most effective when followed by communicative follow-up tasks. Without such follow-up, some students may focus more on completion speed than on language processing itself.

Taken together, the questionnaire and observation data indicate that learner engagement increased most clearly when AI tools were integrated into a pedagogically transparent sequence: a clear language aim, a brief explanation of why the tool was used, a focused classroom task, and a communicative or reflective follow-up stage. This means that the positive effect of AI in EFL does not arise from novelty alone. Rather, it depends on how effectively the teacher connects each tool to meaningful language practice and classroom interaction.

Conclusion

Overall, the study shows that AI-supported digital tools can strengthen learner engagement across different age groups when they are built into a clear lesson structure and connected to a specific language aim.

A skill-based classification of tools proved useful for analysis and for teaching practice. In the lessons observed, Suno AI was most effective for listening repetition, ELSA Speak for pronunciation work, Gliglish for spoken interaction, AI Text Leveler for reading adaptation, Grammarly for revision in writing, Quizlet for vocabulary recycling, and Online Test Pad for quick formative assessment.

 Questionnaire responses and classroom observation led to a similar conclusion: students reacted most positively to activities in which the tool offered immediate response, visible progress, and an obvious connection to the task they were asked to complete.

The results also confirm that the teacher remains the central figure in organising AI-based work. The tool itself does not guarantee a stronger lesson. Its value depends on selection, timing, instructions, and the quality of the follow-up interaction built around it.

References:

1. British Council. AI in education: how to use AI tools in your classroom next term [Electronic resource]. – URL: https://internationalschools.britishcouncil.org/blog/ai-in-education-how-to-use-ai-tools-in-your-classroom-next-term (accessed: 21.03.2026). [in English].
2. Ross E. M. Embracing artificial intelligence in the classroom [Electronic resource] // Harvard Graduate School of Education. – 2023. – URL: https://www.gse.harvard.edu/ideas/usable-knowledge/23/07/embracing-artificial-intelligence-classroom  (accessed: 21.03.2026). [in English].
3. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education. – Boston: Center for Curriculum Redesign, 2019. – 257 p. [in English].
4. Luckin R. Machine Learning and Human Intelligence: The Future of Education for the 21st Century. – London: UCL Institute of Education Press, 2018. – 232 p. [in English].
5. Godwin-Jones R. Distributed agency in second language learning and teaching through generative AI // Language Learning & Technology. – 2024. – Vol. 28. – No. 2. – P. 5–30. – DOI: 10.64152/10125/73570. [in English].
6. Suno. AI music generator [Electronic resource]. – URL: https://suno.com (accessed: 21.03.2026). [in English].
7. ELSA Speak. AI-powered English speaking coach [Electronic resource]. – URL: https://elsaspeak.com  (accessed: 21.03.2026). [in English].
8. Gliglish. Learn languages by speaking with AI [Electronic resource]. – URL: https://gliglish.com  (accessed: 21.03.2026). [in English].
9. Diffit. Create and adapt instructional materials for diverse classrooms [Electronic resource]. – URL: https://web.diffit.me  (accessed: 21.03.2026). [in English].
10. Grammarly. AI writing assistant [Electronic resource]. – URL: https://www.grammarly.com (accessed: 21.03.2026). [in English].
11. Quizlet. AI-powered study tools and flashcards [Electronic resource]. – URL: https://quizlet.com/features/ai-study-tools  (accessed: 21.03.2026). [in English].
12. Online Test Pad. Platform for creating educational tests and quizzes [Electronic resource]. – URL: https://onlinetestpad.com/en  (accessed: 21.03.2026). [resource interface: Russian, English].
13. Kuvshinova E. E. Primenenie iskusstvennogo intellekta v obuchenii inostrannomu yazyku [Application of artificial intelligence in teaching a foreign language] // Gumanitariy Yuga Rossii. – 2024. – Vol. 13. – No. 2 (66). – P. 75–84. – DOI: 10.18522/2227-8656.2024.2.7. [in Russian].