Application of Artificial Intelligence to Enhance English Acquisition by Multilingual Students

Ally Daniel Silva; Bárbara Solana Scarlassara

DOI: 10.22167/2675-6528-202602634

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The incorporation of Artificial Intelligence (AI) in English language teaching has emerged as an innovative pedagogical strategy, especially in bilingual educational settings serving Multilingual Learners (MLLs). This study investigated how the pedagogical integration of AI can contribute to the acquisition of English as a second language by MLLs in basic education, analyzing its potential, limitations, and pedagogical implications within the context of a bilingual school guided by the principles of the International Baccalaureate. The research was developed as an exploratory, qualitative, and applied case study in an international school, involving 15 students aged 8 to 10 years. Generative and non-generative AI tools were integrated into English reading, writing, speaking, and listening practices, utilizing classroom observations, field journal entries, written and oral productions, and students’ reflective journals. The results showed consistent progress in students’ linguistic development, with improvements in grammatical accuracy, textual organization, vocabulary, fluency, and communicative confidence. AI provided immediate and tailored feedback, promoting autonomy and reducing language anxiety. It was concluded that AI is a powerful tool for personalizing learning and expanding language use opportunities, provided it is used critically, ethically, and pedagogically, reinforcing the central role of the teacher.

Keywords: Second language acquisition; English teaching; Pedagogical innovation; Learning personalization; Educational technologies.

1. Introduction

The integration of Artificial Intelligence (AI) in education has emerged as one of the central themes in the pursuit of more innovative, personalized, and effective pedagogical practices. In a global scenario, where technology redefines the role of the teacher and the student, understanding how AI can enhance teaching and learning has become essential for the advancement of contemporary educational practices.

AI has the potential to complement human intelligence, expanding personalization capabilities and promoting more meaningful educational experiences, as highlighted by Luckin (2018) and Holmes, Bialik, and Fadel (2022). In the context of language acquisition, especially of English as a second language (L2), AI represents a tool capable of transforming traditional teaching processes, offering immersive experiences, instant feedback, and adaptive instruction approaches that respect the pace and profile of each learner, as reinforced by Poth (2023).

For most students, English constitutes an L2, used both as a medium of instruction and as a vehicle for social and cognitive interaction. In this sense, understanding the role of AI in mediating this linguistic learning process is fundamental to identifying new pedagogical possibilities that dialogue with the educational philosophy of the International Baccalaureate (IB).

There is also a reflection on the intersection between technology, bilingualism, and inclusion, considering the specificities of Multilingual Learners (MLLs) and the need for strategies that contemplate their different linguistic and cultural trajectories. The term MLLs refers to students who develop and use knowledge in two or more languages, even if they present different levels of proficiency in each and use them in distinct contexts. The adoption of this terminology represents a perspective based on the student’s linguistic resources, recognizing multilingualism as part of their repertoire and identity (WIDA, 2020).

Second language learning is a profoundly contextual process, in which affective, cultural, and identity factors directly influence linguistic development, as observed by Krashen (2009) and García and Wei (2014). The incorporation of AI-based tools, such as virtual assistants, writing platforms with automated feedback, intelligent translators, and adaptive content generators, allows not only for the personalization of learning but also for the expansion of student autonomy and the strengthening of communicative competence in real language use contexts (Poth, 2023; Holmes et al., 2022).

The relevance of this study lies in the historical moment in which Brazilian basic education finds itself, marked by the expansion of bilingual education and the increased use of digital technologies in classrooms. In this scenario, AI emerges as a powerful ally for the development of linguistic, cognitive, and socio-emotional skills, especially when guided by solid ethical and pedagogical principles. Therefore, the research objective was to investigate how the pedagogical integration of Artificial Intelligence can contribute to the process of acquiring English as a second language by Multilingual Learners in basic education, analyzing its potential, limitations, and pedagogical implications in the context of a bilingual school guided by the principles of the International Baccalaureate.

2. Material and Methods

This research adopted a qualitative, applied approach, focused on investigating pedagogical practices mediated by technology in bilingual contexts. According to Gil (2019), applied research aims to generate knowledge for solving specific problems, aligning with the analysis of the pedagogical use of Artificial Intelligence in teaching English. The qualitative approach, in turn, favored a deep understanding of phenomena in their natural contexts, considering meanings, perceptions, and processes, as highlighted by Lakatos and Marconi (2017).

The study was characterized as an exploratory case study, seeking to understand in depth a specific educational reality and allowing for detailed analysis of the practices and perceptions of the subjects involved (Gil, 2019). The research was inserted into the field of Design-Based Research (DBR), a research methodology focused on the creation, application, and refinement of educational interventions in real teaching and learning contexts (Design-Based Research Collective, 2003).

DBR is characterized by the close articulation between theory and practice, seeking to understand complex educational phenomena through iterative cycles of planning, implementation, analysis, and refinement of pedagogical proposals. This approach allowed interventions to be adjusted based on systematic observation of the processes, being especially suitable for investigating pedagogical innovations mediated by technology, as pointed out by Wang and Hannafin (2005).

The research was conducted in a medium-sized private school, located in the Ipiranga neighborhood, in São Paulo. The institution adopts instruction in English and follows the International Baccalaureate (IB) framework, operating through the Primary Years Programme (PYP) and Middle Years Curriculum (MYC) programs, which value the integral development of students (IBO, 2018).

Fifteen elementary school students, aged 8 to 10 years, identified as Multilingual Learners (MLLs), participated in the research. These students were learning English as an additional language and were at proficiency levels 1 to 3, according to the English Language Development Standards Framework (WIDA, 2020). The selection of this group considered linguistic heterogeneity and the need for differentiated pedagogical interventions to support the development of English as an L2.

For data collection, different instruments were used, allowing for information triangulation. Classroom observations and field diary entries by the teacher-researcher were made. Written and oral student productions were also collected, in addition to reflective diaries completed by the participants. Interviews were conducted to complement the understanding of interactions and linguistic performance.

Initially, diagnostic instruments were applied to probe students’ proficiency. A written production task requested the elaboration of a short paragraph (6 to 8 sentences) on a known topic, with evaluation criteria that included textual organization, vocabulary, sentence structure, and grammatical accuracy. A guided reading script, composed of a short text and oral questions of literal and inferential comprehension, was also used to observe reading strategies.

The Artificial Intelligence tools employed in the intervention included generative AI-based writing assistants, such as ChatGPT, and text reformulation tools, such as Grammarly. Reading features with voice synthesis, such as those available in Google Docs, Chat GPT, and Copilot, and voice recognition tools (speech-to-text) were also integrated into the activities, considering their accessibility and potential for immediate feedback.

The research development occurred in four successive stages. The first stage, of initial diagnosis, lasted one week (4 hours/class) and consisted of applying reading activities, written production, and systematic classroom observation, without the use of AI tools, to establish a baseline of the students’ linguistic development.

The second stage, of planning the pedagogical intervention, also lasted one week and focused on the design of the AI-mediated didactic sequence. Linguistic objectives, evaluation criteria, teacher mediation strategies, and types of interaction with AI were defined, selecting digital tools aligned with the principles of the International Baccalaureate, Universal Design for Learning, and ethical technology use guidelines.

The third stage, of implementing the didactic sequence, lasted for four weeks (approximately 16 class hours) and corresponded to the core of the intervention. In this phase, the researcher-teacher conducted activities that integrated AI tools into reading, writing, speaking, and listening practices in English, with systematic monitoring of the classes and recording of student interactions and productions.

The fourth stage, of observation, analysis, and refinement, included the recording and analysis of the collected data. Students’ written and oral productions were evaluated using rubrics aligned with WIDA descriptors and IB evaluation criteria, which covered textual organization, grammar, vocabulary, fluency, pronunciation, and confidence. A linguistic observation form recorded aspects such as oral participation, L1 use, L2 attempts, linguistic anxiety, and spontaneous strategies, allowing for a qualitative analysis of the development of communicative competence.

The data were organized through the systematic recording of students’ initial and revised productions, the researcher-teacher’s field diaries, students’ reflective diaries, and the evaluative rubrics. Data analysis was performed qualitatively, through the triangulation of information obtained by the different instruments, with the objective of understanding the learning processes and the interactions mediated by AI.

Ethical considerations were rigorously observed. The use of AI tools occurred on institutional accounts, following the school’s internal policies and IB guidelines on data protection and digital security. No personal data was entered into open AI systems, and all interactions were supervised by the research professor. The commitment to the responsible and ethical use of AI was based on guidelines from UNESCO (2023), the International Society for Technology in Education (ISTE, 2023), and the U.S. Department of Education (2023).

3. Results and Discussion

The present investigation into the integration of Artificial Intelligence (AI) in English language teaching for Multilingual Learners (MLLs) revealed a promising and complex panorama, aligned with educational theories that advocate for the student’s active participation in the learning process, as proposed by Dewey (1980). The findings demonstrate that AI can be a significant resource for promoting more interactive, personalized, and effective learning experiences, provided its use is conscious and intentional. The study sought to understand how this technology can foster student autonomy and the development of communicative competence, respecting the linguistic, cultural, and socio-emotional dimensions of the learners, while also reflecting on teaching practices that promote the ethical and responsible use of technology in language teaching.

The analysis of the data, based on the comparison between students’ initial and final linguistic productions, the teacher-researcher’s field diary records, and the students’ reflective diaries, showed consistent progress. For example, an initial written production by a student, which expressed “I go to park and play with friend”, demonstrated a lack of past tense marking, omission of definite and indefinite articles, and absence of plural marking. These characteristics are consistent with the initial stages of L2 acquisition, where the learner still relies on simplified structures and L1 transfer, as described by Krashen (1982).

After the pedagogical intervention mediated by AI, the same student produced the sentence “I went to the park and played with my friends”. This evolution demonstrated the adequate use of the regular past tense, the correct insertion of articles, and the marking of plurals, indicating the internalization of linguistic patterns. Such progress was facilitated by the immediate and contextualized feedback provided by AI tools, a pattern of development observed recurrently among the study participants. The data collected throughout the intervention confirmed significant advances in the students’ linguistic development, particularly in written production.

A progressive improvement in textual organization was observed, which manifested in the transition from isolated phrasal structures to coherent sequences of ideas. There was also a notable increase in grammatical accuracy, especially in the use of verb tenses and basic connectors. AI mediation was crucial, as it allowed students to receive immediate feedback adapted to their proficiency level, encouraging them to revise and rewrite texts more autonomously and consciously. These results corroborate Krashen’s (1982) comprehensible input hypothesis, which posits that learning is most effective when linguistic input is adjusted to the learner’s level.

Unlike traditional pedagogical approaches, where feedback is often deferred and generic, the intervention with Artificial Intelligence provided a continuous cycle of trial, error, and revision. This more responsive learning environment not only favored error correction but also promoted the development of metalinguistic awareness, allowing students to understand the “why” behind corrections, rather than merely reproducing them. These findings also align with Swain and Lapkin’s (1998) output hypothesis, which emphasizes how the active production of language, combined with immediate feedback, contributes to the consolidation of linguistic knowledge and the internal restructuring of the language.

The pedagogical intervention with the students was structured in four stages, aiming to test hypotheses and apply theory in practice. The First Stage, the Initial Diagnosis, aimed to map the students’ recurring difficulties regarding textual organization, the use of verb tenses, and communicative confidence in English. To this end, initial diagnostic instruments were applied, including a written production task and a guided reading script, without the use of Artificial Intelligence tools, establishing a baseline of the students’ linguistic development.

The diagnostic written production proposal aimed to identify the initial level of written proficiency of the MLLs, considering textual organization, vocabulary, sentence structure, and grammatical accuracy. The instruction requested a short paragraph (6-8 sentences) on a known topic, focusing on clear organization of ideas. The observed criteria included clarity of ideas, use of simple and compound sentences, coherence and logical sequence, use of basic vocabulary, and recurring errors, such as verb tense and agreement. The guided reading script consisted of reading a short English text, suitable for the students’ level, followed by oral questions of literal and inferential comprehension, in addition to observing the reading strategies used, such as the use of cognates and inference.

These initial results were crucial for guiding the pedagogical intervention planning, indicating the need for strategies that would broaden linguistic input and support the development of comprehension at more complex levels, justifying the subsequent integration of Artificial Intelligence tools. The Second Stage, Pedagogical Intervention Planning, focused on the design of the AI-mediated didactic sequence. An example of the diagnostic instrument for reading comprehension used was the “Diagnostic Reading Test”, which presented a short text followed by questions of literal comprehension (who, what, where, when) and inferential comprehension (why, how, critical reflection).

The analysis of the responses to this instrument allowed us to identify the students’ proficiency level, revealing vocabulary limitations and difficulties in making inferences in English. This stage, also lasting one week, included the selection of digital tools aligned with the principles of the International Baccalaureate, Universal Design for Learning, and the guidelines for ethical technology use. The linguistic objectives, evaluation criteria, teacher mediation strategies, and types of interaction with AI were defined, such as writing assistants, text readers with voice synthesis, and linguistic reformulation tools.

A relevant aspect observed was the use of the mother tongue (Portuguese) as a support strategy for learning in the initial stages. Students resorted to Portuguese to understand instructions and organize ideas, but this use gradually decreased as they became more confident in English. This movement highlights “translanguaging” practices, where the student’s linguistic repertoire is used in an integrated way in the learning process (García; Wei, 2014), and reinforces the linguistic interdependence proposed by Cummins (2000).

The Third Stage, the Implementation of the Didactic Sequence, corresponded to the core of the intervention, developed over four weeks, totaling approximately 16 class hours. In this phase, the teacher-researcher implemented the planned didactic sequence, promoting the development of reading, writing, and speaking skills in English through the pedagogical and ethical use of Artificial Intelligence tools. The teacher’s performance was fundamental, offering gradual support to students and adapting linguistic input, according to Krashen’s (1985) notion of comprehensible input, in addition to promoting meaningful interactions aligned with the sociocultural perspective of language learning.

During this stage, students used AI tools in a guided manner to review texts, expand vocabulary, practice oral skills, and receive immediate feedback. The prompts used were previously planned by the researcher-teacher with clear pedagogical purposes, such as explained grammatical review, simplified text rewriting, contextualized lexical suggestions, and pronunciation practice. The students’ initial and revised productions were systematically recorded for qualitative comparison, allowing continuous monitoring of linguistic progress.

In the field of orality, the results pointed to a significant increase in fluency and communicative confidence. The use of tools based on voice recognition and synthesis provided a safe environment for language practice, which reduced linguistic anxiety and encouraged active student participation. This aspect reinforces the importance of the affective filter in the language acquisition process, as described by Krashen (1982), and the role of output in consolidating learning, according to Swain and Lapkin (1998).

The field diary records indicated changes in students’ linguistic behavior, who initially showed hesitation and frequent use of their mother tongue. After AI-mediated practices, students began to produce more complete and fluent utterances, even if still with errors, evidencing growing communicative confidence. The Artificial Intelligence tools used included writing assistants with grammatical feedback, simplified rewriting tools, voice synthesis (text-to-speech), and voice recognition (speech-to-text).

The prompt examples used, such as “Check this text for grammar mistakes and explain them using simple English” for grammar review, “Rewrite this paragraph using short sentences and simple vocabulary” for rewriting, “Suggest simple synonyms for these words in a school context” for vocabulary, and “Read this text slowly and clearly so I can practice pronunciation” for oral practice, demonstrate the pedagogical intentionality in the use of AI. These activities, from the perspective of the output hypothesis (Swain, 1985), favored students’ linguistic production, allowing them to identify gaps in their knowledge and develop greater control over the language.

In parallel, in light of multimedia learning theory (Mayer, 2009), the use of resources such as audio, text, and digital interaction contributed to the construction of meanings by integrating multiple cognitive processing channels. These elements indicate that technology was employed as structured support for the language acquisition process, aligning with the needs of MLL students and reinforcing the role of the teacher-researcher as a mediator of the pedagogical use of AI, enhancing the personalization of teaching and expanding opportunities for meaningful interaction with the English language.

To measure the development of writing and speaking skills, assessment rubrics were used. The writing rubric included criteria such as textual organization, grammar, and vocabulary, with performance levels ranging from “beginner” (disconnected ideas, many errors, very limited vocabulary) to “proficient” (well-structured text, few errors, varied and appropriate vocabulary). The speaking rubric, in turn, addressed fluency, pronunciation, and confidence, with levels ranging from “beginner” (frequent hesitations, difficulty understanding, avoids speaking) to “proficient” (consistent fluency, clear and intelligible, participates spontaneously).

The application of these rubrics enabled a systematic formative assessment, allowing the teacher-researcher to identify specific progress and difficulties, adjusting pedagogical strategies. A gradual evolution of students towards higher levels of performance was observed, especially in textual organization, vocabulary expansion, and greater fluency in oral communication. From a theoretical point of view, the adoption of criteria that value production and communicative effectiveness aligns with the perspective of communicative competence and the output hypothesis (Swain, 1985), contributing to more structured and conscious learning.

The Fourth Stage, of Observation, Analysis and Refinement, involved the analysis of written and oral productions, of the teacher’s field diary records, of the evaluation rubrics, and of the students’ reflective diaries. This stage allowed for the identification of advances, challenges, and necessary adjustments in the pedagogical proposal. An example of a student’s written production, before the intervention, was “I like play soccer. I play with my friends. We is happy.”, which after the AI’s mediation was revised to “I like playing soccer. I play with my friends after school. We are happy when we play together.”

This comparison demonstrates improvements in grammatical accuracy, sentence structure, and semantic expansion of ideas, without distorting the student’s authorship. AI acted as support in the revision process, facilitating the identification and correction of recurring errors and text reorganization, which contributed to the development of linguistic awareness and autonomy in English writing. The field diary records of the teacher-researcher, in turn, documented in real-time the students’ reactions, engagement levels, and behavioral changes.

An excerpt from the field diary revealed: “During the text revision activity with AI, I observed greater student engagement. Many reread their texts more than once and verbalized doubts about grammatical choices. There was a visible reduction in anxiety, especially among students who normally avoid writing in English.” These records demonstrate the reduction of linguistic anxiety and the increase in participation, reinforcing the role of teacher mediation and immediate feedback in the learning process. The teacher-researcher observed that her pedagogical mediation was fundamental to guiding the use of AI tools, especially in interpreting suggestions and maintaining focus on tasks.

The students’ reflective journals offered a valuable student perspective on AI-mediated learning. The responses revealed advances in metacognitive development, as students recognized their difficulties, identified strategies used, and projected future actions to improve their performance. The reports indicated a strengthening of self-confidence and the perception of AI as a learning support tool, contributing to the construction of student “agency” and to the reduction of the affective filter in the use of the English language.

Beyond linguistic advancements, positive impacts were identified in cognitive and metacognitive aspects. Students demonstrated a greater ability to reflect on their own learning process, identify difficulties, and adopt strategies to overcome them, aligning with the principles of meaningful learning (Ausubel, 1982) and active learning (Mayer, 2020). Evidence of strengthened executive functions, such as attention and cognitive flexibility, often associated with bilingualism (Bialystok, 2001; 2012), was also observed. One student noted: “Now I can write better because I check my mistakes,” indicating the development of metalinguistic awareness and autonomy in the learning process.

In terms of motivation, an expressive increase in engagement was observed, especially in students who previously demonstrated low participation. This suggests that the personalization and interactive nature of AI tools acted as relevant motivational factors in the context of L2 learning. The interactive and personalized nature of the tools contributed to making the learning process more meaningful and accessible, promoting greater participation and interest. These results align with the principles of the International Baccalaureate (IBO, 2018), which value student “agency” and the development of competencies such as autonomy, reflection, and critical thinking.

The linguistic observation form, used by the professor-researcher, systematically recorded linguistic, affective, and strategic aspects manifested by the students. A progressive increase in oral participation was observed, with students who initially responded briefly or avoided speaking, now participating more frequently and showing a greater willingness to express themselves in English. The use of the mother tongue (L1) occurred mainly in the initial stages for comprehension and thought organization, but there was a gradual reduction, with more controlled alternation between L1 and L2, characterizing functional and conscious translanguaging practices.

Students demonstrated increasing initiative in producing English utterances, even with incomplete or simplified structures, using keywords, short phrases, and successive reformulations, indicating greater confidence and willingness to experiment with the language. Initial signs of insecurity and hesitation, such as prolonged silence and low voice, were identified, but with the advancement of the intervention and immediate AI feedback, a significant reduction in linguistic anxiety and an increase in communicative self-confidence were observed. Spontaneous strategies, such as gestures, self-correction, word repetition, use of cognates, and requests for support from the AI or the teacher, became more frequent and sophisticated, evidencing metacognitive development.

The Fifth Stage, Reflective Evaluation of Results, allowed for a critical analysis of the identified challenges, despite the positive outcomes. At times, a tendency was observed for students to excessively rely on answers generated by AI tools, especially in written production activities. This behavior indicates the need to develop skills related to critical digital literacy, empowering students to evaluate, question, and reformulate the responses provided by technology, rather than using them passively, which aligns with international guidelines for the ethical use of AI in education (UNESCO, 2023).

Another relevant point was the cognitive overload identified at certain moments of the intervention. The simultaneous presence of multiple stimuli, such as text, audio, automatic suggestions, and instant feedback, may have hindered information processing by some students, especially those at initial levels of linguistic proficiency. This phenomenon can be understood in light of Cognitive Load Theory (Sweller, 1998) and Multimedia Learning Theory (Mayer, 2020), which warn that excess information can compromise learning when there is no adequate control of task complexity and stimulus organization.

Additionally, it was observed that the effectiveness of AI use was directly related to the level of teacher mediation. In moments when there was explicit guidance, modeling of tool use, and clear definition of linguistic objectives, students showed greater engagement and better quality in their productions. On the other hand, in situations with less direction, superficial or inadequate use of resources was verified, reinforcing the central role of the teacher as a mediator of the learning process, as discussed by Moran (2007).

In this sense, the integration of Artificial Intelligence into English language teaching has shown to be promising, but conditioned by fundamental pedagogical factors, such as didactic intentionality, structured planning, and continuous monitoring. Just as with active methodologies, technology, by itself, does not guarantee learning; it needs to be articulated with consistent and contextualized pedagogical practices. The reflective evaluation of the results allows us to affirm that AI can act as a powerful tool for personalizing learning and expanding opportunities for language use, provided it is used critically, ethically, and pedagogically oriented.

Thus, the importance of teacher training for the use of educational technologies is reaffirmed, as well as the construction of practices that promote the balance between technological innovation and the cognitive and linguistic development of students. In summary, the research findings demonstrate that AI, when integrated in a planned and teacher-mediated manner, contributes significantly to the linguistic, metacognitive, and motivational development of MLLs, promoting a more personalized, interactive, and safe learning environment that favors autonomy and communicative confidence.

4. Conclusion

This study investigated how the pedagogical integration of Artificial Intelligence can contribute to the acquisition of English as a second language by Multilingual Learners in basic education, analyzing its potential, limitations, and pedagogical implications in the context of a bilingual school guided by the principles of the International Baccalaureate. It was found that AI provided consistent advances in students’ linguistic development, with improvements in grammatical accuracy, textual organization, vocabulary, fluency, and communicative confidence. It was observed that immediate and adapted AI feedback promoted autonomy and reduced language anxiety, in addition to positively impacting cognitive and metacognitive aspects, such as the ability to reflect on one’s own learning and the strengthening of executive functions. Student engagement also increased significantly. The main contribution lies in demonstrating that AI is a powerful tool for personalizing learning and expanding language use opportunities, provided it is used critically, ethically, and pedagogically, reinforcing the central role of the teacher as a mediator.

However, it was identified that the intervention presented limitations, such as students’ tendency towards excessive dependence on AI-generated responses and the occurrence of cognitive overload during moments of multiple stimuli, especially for those at initial proficiency levels. The effectiveness of AI use was directly related to the level of teacher mediation, with less guidance resulting in superficial use of resources. Faced with these challenges, the need to develop critical digital literacy skills in students and to invest in continuous teacher training for the use of educational technologies is suggested. The construction of practices that promote a balance between technological innovation and the cognitive and linguistic development of students is recommended, providing subsidies for other institutions interested in integrating AI in a pedagogical and humanized way into bilingual education.

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