Artificial Intelligence and Personalized Learning in ESL: A Systematic Review of Adaptive Material Design and Ethical Considerations.

Artificial Intelligence and Personalized Learning in ESL: A Systematic Review of Adaptive Material Design and Ethical Considerations.

Prialoshini Naterkumar, Harwati Hashim

Universiti Kebangsaan Malaysia (UKM)

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000343

Received: 05 July 2025; Accepted: 12 July 2025; Published: 16 August 2025

ABSTRACT

The growing global demand for English proficiency has exposed limitations in traditional English as a Second Language (ESL) instruction, particularly in addressing diverse learner needs. This systematic literature review (SLR) investigates how Artificial Intelligence (AI)-personalized learning platforms enhance ESL learning by adapting materials to learners’ proficiency levels, learning styles, and individual needs. It also explores the ethical considerations involved in implementing AI in ESL contexts. Twenty peer-reviewed publications from major academic databases published between 2014 and 2025 were examined using the PRISMA methodology. In contrast to conventional one-size-fits-all methods, research shows that AI-driven platforms greatly increase student autonomy, engagement, and total language competency by providing personalised learning routes, real-time feedback, and multimodal material. However, challenges such as algorithmic bias, data privacy, equitable access, and the irreplaceable role of educators must be addressed to ensure ethical and inclusive AI integration. This review underscores the transformative potential of AI in ESL education while emphasizing the importance of responsible and equitable implementation.

Keywords: Artificial Intelligence, Personalized Learning, ESL Instruction, Adaptive Learning, Language Proficiency, Ethical Challenges

INTRODUCTION

The globalization of communication and the increasing demand for English proficiency have propelled English as a Second Language (ESL) education to the forefront of educational discourse. However, traditional ESL teaching methodologies often struggle to cater to the diverse needs of learners, characterized by varying proficiency levels, learning styles, and individual learning goals. This heterogeneity necessitates a shift towards more personalized and adaptive learning approaches. In recent years, Artificial Intelligence (AI) has emerged as a promising tool to address these challenges, offering the potential to revolutionize ESL education by providing tailored learning experiences. This Systematic Literature Review (SLR) aims at how does the use of AI-personalized learning platforms that adapt materials based on proficiency, learning styles, and needs compare to traditional ESL instruction in terms of improving overall language proficiency and to investigate the ethical considerations and challenges associated with the implementation of AI in ESL education.

RQ 1: How is Artificial Intelligence (AI) used to support personalized learning in ESL Context?

RQ 2:What are the ethical considerations and challenges associated with the implementation of AI in ESL education.

The advent of AI in education has led to the development of sophisticated algorithms capable of analysing learner data and dynamically adjusting instructional content (Holmes, Bialik, Fadel, 2019). This capability is particularly crucial in ESL contexts, where learners exhibit a wide spectrum of linguistic abilities, ranging from beginners grappling with basic vocabulary and grammar to advanced learners refining their communicative competence. AI-driven platforms can assess learners’ proficiency through various means, including adaptive testing, Natural Language Processing (NLP) analysis of written and spoken output, and tracking of learner interactions with educational resources. By accurately gauging proficiency levels, these systems can deliver content that is neither too challenging nor too simplistic, ensuring optimal learning engagement and progress (Luckin et al., 2016).

Furthermore, the recognition of diverse learning styles, such as visual, auditory, and kinaesthetic preferences, underscores the importance of personalized instruction.AI can leverage machine learning techniques to identify learners’ preferred modalities and tailor content accordingly. For instance, visual learners may benefit from interactive infographics and videos, while auditory learners might thrive with podcasts and audio-based exercises. Adapting to these individual styles can enhance comprehension and retention, thereby accelerating the learning process. The integration of AI tools can also address the individual needs of learners, such as specific learning disabilities or unique language learning goals.AI can create personalized learning pathways that cater to these requirements, ensuring that every learner receives the support they need to succeed (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019).      

Limitations of Traditional ESL Methodologies and the Emergence of AI-Driven Adaptive Learning

Traditional ESL teaching methodologies, predominantly characterized by teacher-centered instruction, reliance on textbooks, and rote memorization, often struggle to address the diverse learning needs of students (Brown, 2007). These methods typically involve standardized curricula, limited personalized feedback, and a one-size-fits-all approach, which may not cater to varying proficiency levels, learning styles, or individual learning goals. In contrast, AI-powered tools offer adaptive learning experiences by dynamically adjusting content based on real-time learner data, providing personalized feedback, and accommodating diverse learning styles through multimodal instruction. 1 This shift from static, uniform instruction to dynamic, personalized learning represents a fundamental departure from traditional methods, enabling more targeted and effective language acquisition. AI tools can analyze learner performance, identify areas of weakness, and provide tailored exercises, surpassing the limitations of traditional methods that often rely on generalized assessments and limited individual attention (Holmes, Bialik, Fadel, 2019). Liang et al., 2021; Crompton & Burke, 2023​ found that immediate formative feedback through AI tools improved learners’ engagement and language acquisition faster than traditional assessment methods.

Language acquisition is a very personal process that is impacted by a variety of elements, including motivation, cultural background, cognitive processes, past knowledge, and individual learning objectives (Oxford, 2003). Many students experience disengagement or delayed development as a result of traditional “one-size-fits-all” ESL training, which usually depends on standardised materials and fixed pacing (Brown, 2007). By adjusting teaching to each learner’s requirements, preferences, and skills, personalisation in language learning aims to close this gap and maximise retention, engagement, and understanding (Dörnyei, 2005). According to research, students exhibit more motivation and attain higher language competence when they receive teaching that is tailored to their unique learning preferences, whether those preferences are visual, auditory, kinaesthetic, or multimodal (Ellis, 2008; Skehan, 1998). In recent years, Artificial Intelligence (AI) has emerged as a powerful tool to operationalize personalized learning at scale, offering real-time adaptation based on learners’ behaviours, proficiency levels, and learning trajectories (Luckin et al., 2016; Holmes, Bialik, & Fadel, 2019). AI-driven personalized platforms can dynamically assess learner input and modify content delivery modes, difficulty levels, and feedback strategies, creating a more effective and individualized learning environment (Zawacki-Richter et al., 2019). In the context of ESL education, where learner diversity is particularly pronounced, personalization through AI represents a transformative shift from rigid, static instruction to a more flexible, learner-centered model that fosters greater language acquisition and autonomy.

Over the past ten years, the pace of Artificial Intelligence (AI) integration into education has increased, changing the way that information is imparted, evaluated, and tailored to each individual. With features like adaptive learning, intelligent tutoring systems, automated assessment, and personalised material delivery, artificial intelligence (AI) technologies once limited to experimental labs have already made their way into regular classrooms (Luckin et al., 2016; Holmes, Bialik, & Fadel, 2019). AI technologies are able to analyse vast amounts of learner data, such as performance trends, mistake patterns, and interaction behaviours, in order to dynamically modify teaching in real-time when it comes to language acquisition (Woolf, 2010). A more responsive, learner-centred educational experience is made possible by this capacity, which represents a substantial shift from conventional, static forms of instruction. Researchers point out that, especially when paired with personalisation techniques, AI-supported learning environments can promote increased learner autonomy, engagement, and accomplishment (Heffernan & Heffernan, 2014; Roll & Wylie, 2016). AI’s ability to tailor ESL training to each learner’s requirements, style, and competency is especially important as the demand for English proficiency throughout the world keeps growing. To guarantee that the advantages of AI technologies are available and fair to all students, their quick development also necessitates a thorough analysis of its ethical, pedagogical, and societal ramifications (Li & Zhou, 2022; Alisoy, 2025).

Even though the amount of research on artificial intelligence in education is increasing, prior systematic reviews have frequently concentrated only on the pedagogical results or the technological efficacy of AI tools in language learning, failing to adequately incorporate the ethical aspects of these technologies in ESL contexts. The majority of previous reviews focus on engagement metrics, performance gains, or the advantages of adaptive learning, but they frequently ignore the ways in which ethical concerns—like data privacy, algorithmic bias, equitable access, and the changing role of human educators—intersect with learning outcomes in AI-mediated ESL instruction. Furthermore, there aren’t many studies that take a comprehensive look at how these ethical issues could affect or jeopardise the efficacy and equity of individualised learning experiences. This leads to a crucial research vacuum since appropriate and successful use of AI in ESL instruction requires a grasp of both the pedagogical advantages and ethical ramifications.

METHODOLOGY

As seen in Figure 1, this systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, which consists of four steps: Identification, screening, eligibility, and inclusion. PRISMA’s comprehensiveness and applicability to various investigations have led to its widespread adoption by researchers. Thus, the following are the study’s goal and the systematic review procedure.

Figure 1. Systematic review using the PRISMA method

Identification

Three databases were taken into consideration for the applicability of this study during the identification step, as detailed in the PRISMA criteria. Google Scholar, Educational Resources Information Centre (ERIC), and Scopus were the databases. In order to take into account the constructions intended for evaluation, the researcher created the key phrases. Each database has keywords that were specifically associated with AI Personalised Learning for ESL Leaners Table 1 below showed the search terms for every database.

Table 1

Screening

The identifying phase was followed by the article screening phase. By comparing the articles across the three databases, the researcher eliminated any duplicates. After 20 pieces were removed at the first screening stage, 120 were left for additional examination. Out of these 120 titles, 89 papers were disqualified because their titles, abstracts, and keywords did not meet the screening requirements. Only 31 articles were left after the various degrees of selection, and they were filtered using the inclusion and exclusion criteria listed in Table 2 below. Only 20 potential papers were selected for this systematic review after a rigorous selection procedure based on the inclusion and exclusion criteria.

Table 2

Inclusion

The papers in the systematic review have a direct connection to AI Personalised Learning for ESL Learners. Table 3 displays the included research publications. Papers were chosen from Scopus, ERIC, and from Google Scholar, as shown in the table. Most of the study was carried out in higher education institutions like universities and colleges, concentrating on different educational fields.

Table 4

Data Analysis

Theme analysis was conducted to address the following study questions:

1. How is Artificial Intelligence (AI) used to support personalized learning in ESL Context?
2. What are the ethical considerations and challenges associated with the implementation of AI in ESL education

The researcher conducted the analysis by classifying the themes according to the study questions and evaluating the articles’ contents. For the first study question, the themes were categorized by the learner factors that AI-driven systems adapt to (e.g., proficiency levels, learning styles, individual learning goals). For the second study question, the articles were arranged according to the specific elements of adaptive material design, such as content sequencing, difficulty adjustment, and modality variation, found in AI-personalized ESL platforms.

RESULTS

RQ1- How is Artificial Intelligence (AI) used to support personalized learning in ESL Context?

According to the systematic review, it can be concluded that, AI Personalised learning platforms that adapts materials by offering tailored learning experiences that adjust to each student’s ability, learning preferences, and unique needs all of which traditional approaches sometimes miss because of their one-size-fits-all methodology AI-personalized learning platforms provide a striking contrast to traditional ESL training. A dynamic and interactive learning environment that improves language acquisition and engagement is fostered by AI-driven tools that analyse learner data to generate personalised learning routes and provide instant feedback. In addition to meeting the various requirements of language learners, this personalisation fosters learner autonomy, motivation, and collaborative learning signaling a dramatic departure from traditional, homogenous educational paradigms in favour of more learner-oriented strategies.

AI’s ability to tailor the learning experience, unlike traditional methods that often use a one-size-fits-all approach. AI platforms can adapt to individual learner’s needs, making learning more engaging and effective. Other than that, AI’s capability to analyze learner data and create personalized learning paths is a clear advantage over traditional methods that lack this adaptability. AI platforms offer prompt feedback, a feature often lacking in traditional classrooms where feedback may be delayed or less individualized. Other than that, AI can transform language learning by making it more interactive and engaging through simulations and games, which can increase student motivation. Traditional ESL instruction may rely more on conventional methods like lectures and textbooks. It is also concluded that AI can create opportunities for learners to connect and collaborate, enriching the learning experience through social interaction. Traditional classrooms might offer collaborative learning, but AI can expand the scope of interaction.

The table essentially demonstrates how AI-personalized learning platforms offer customisation, adaptability, instant feedback, engaging experiences, and collaborative opportunities all of which enhance overall language proficiency and how they differ from and enhance traditional ESL instruction.

RQ2- What are the ethical considerations and challenges associated with the implementation of AI in ESL education.

While there are many advantages of using AI in ESL instruction, there are also moral questions and difficulties, such as the necessity of ensuring that all students have equal access to these tools in order to prevent the digital divide from growing. Another major worry is algorithmic bias, which occurs when AI systems unintentionally reinforce or magnify social prejudices, producing unfair or discriminating results for particular learner groups. Data privacy is also crucial; in order to safeguard students’ security and confidentiality, AI systems’ gathering and usage of student data must be managed carefully. Furthermore, even though AI might improve individualised learning, it’s critical to acknowledge that instructors play an indispensable role in offering sociocultural and emotional support roles that AI cannot completely replace.

The ethical considerations and challenges surrounding the implementation of AI in ESL education are multifaceted and critical to address for responsible integration. Ensuring fair access to AI technologies is one of the main issues. Even while AI has significant advantages, if these tools are not available to all students, there is a chance that the digital gap could worsen. This means that in order to stop uneven access from escalating educational gaps, efforts must be made to offer resources and assistance to students from a variety of socioeconomic origins, places, and skill levels.  Algorithmic prejudice is another important ethical dilemma. AI systems have the potential to reinforce or even magnify social prejudices if the data they are trained on reflects those biases. This might lead to AI systems in ESL instruction that unjustly evaluate or teach students from particular language or cultural backgrounds. Data privacy is another crucial ethical factor. Large volumes of student data are gathered and analysed by AI systems, which raises questions about how this data is utilised, kept, and safeguarded. To protect student information and keep their confidence, it is imperative to have strong data privacy rules and procedures.

Lastly, it is critical to recognise the indispensable role that educators play.The sociocultural and emotional components of education that human educators give cannot be entirely replaced by AI, even though it may offer individualised instruction and assistance. The goal of integrating AI into ESL instruction should be to enhance, not to replace, the vital role that educators play in creating a welcoming and inclusive classroom.

DISCUSSION

This systematic literature review (SLR) aimed to explore the role of AI in ESL education, focusing on two key areas: the comparison of AI-personalized learning platforms with traditional instruction and the ethical considerations surrounding AI implementation. According to the analysis, there is a clear trend towards AI-personalized learning tools, which have several benefits over conventional ESL training. The different demands of learners, who display a range of competence levels, learning styles, and personal objectives, are sometimes difficult for traditional ESL techniques to meet. AI solves this by offering personalised educational opportunities. Learner data is analysed by AI-driven technologies to provide customised routes that adjust to each learner’s goals and requirements. Research shows that AI platforms can personalise instruction, which enhances learning results and student engagement.

Numerous facets of language learning can be improved by AI, according to studies. For instance, AI-powered tools offer compelling learning experiences, while AI-driven systems provide quick feedback on language creation and skill advancement. AI tools increase student motivation by providing interactive games, simulations, and tailored feedback. This is in line with studies showing AI’s ability to evaluate English proficiency and offer immediate feedback.

Additionally, by bringing together students from various backgrounds, AI fosters social interaction and collaborative learning. Together with AI’s capacity for adaptation, this collaborative element signifies a dramatic departure from conventional, homogenous educational methodologies in favour of learner-centred strategies.

There are significant ethical issues and difficulties in using AI in ESL instruction. A major issue is making sure AI technologies are accessible to anyone. The digital gap might grow if AI technologies are not available to all students. Lastly, even though AI allows for personalised learning, it is important to acknowledge the crucial role that instructors play. Teachers offer emotional and sociocultural support that AI cannot completely replace. The intention is to enhance, not replace, the function of the instructor.

AI-personalized learning systems offer engaging, responsive, and adaptable learning experiences that are customised to each learner’s unique profile, they perform noticeably better than traditional ESL training. The varied requirements, learning styles, and skill levels of students are difficult for traditional techniques to meet since they frequently rely on textbooks, set curriculum, and generalised assessments (Brown, 2007; Oxford, 2003). AI-driven systems, on the other hand, use learner data to dynamically personalise material, providing scaffolding and real-time feedback that adapt to learners’ changing objectives and skills (Luckin et al., 2016; Naseer et al., 2024). Deeper learner engagement and improved language acquisition are fostered by this real-time response. Furthermore, as compared to passive, lecture-based training, AI technologies like simulations, interactive games, and adaptive multimedia material greatly improve motivation and retention (Crompton & Burke, 2023; Said Muhammad Khan, 2023). AI’s ability to foster learner autonomy through self-paced modules and immediate, personalised feedback also supports long-term motivation and self-regulated learning (Wei, 2023; Anjum Khan, 2024). Additionally, AI enhances cooperation chances by connecting students globally, promoting social interaction and cross-cultural exchange outside of the traditional classroom (Ahmed et al., 2025; Wang et al., 2023). These findings demonstrate how AI has the potential to transform ESL learning and how, with proper application, it may surpass conventional teaching techniques.

CONCLUSION

In contrast to traditional training, this systematic literature review looked at how AI-personalized learning platforms improve ESL learners’ language fluency. It also investigated the moral ramifications of using these technologies in educational settings. According to the results, systems with AI assistance offer a more dynamic, responsive, and learner-centred experience. Compared to traditional teacher-centered techniques, artificial intelligence (AI) promotes better engagement, motivation, and autonomy by tailoring information to each learner’s skill level, learning preferences, and individual requirements.

Deeper understanding and skill development are promoted by AI systems’ support for multimodal resources, personalised learning pathways, and real-time feedback. Artificial Intelligence (AI) offers adaptable, customised experiences that assist handle the great variability among ESL learners, in contrast to traditional ESL approaches that frequently rely on static materials and generalised training. Additionally, elements like interactive activities, collaboration tools, and simulations provide an engaging atmosphere that boosts student confidence and language learning.

But there are also serious ethical issues with using AI into ESL instruction. Important issues include protecting student data and privacy, preventing algorithmic bias, preserving the essential human component in education, and guaranteeing fair access to AI technologies to prevent the digital divide from growing. AI should complement teachers and improve their capacity to provide inclusive, individualised teaching rather than take the place of them.

From a policy perspective, educational institutions must make investments in the development of ethical AI, supply infrastructure and teacher training, and guarantee that deployment is appropriate for the given environment. AI should be carefully incorporated into education while educators continue to play a key role in promoting socioemotional learning and cultural relevance.

To sum up, AI-personalized learning has a lot of potential to revolutionise ESL instruction. It may improve student results, boost motivation, and provide more inclusive, flexible learning environments when applied sensibly and morally. Future studies should keep looking at the best methods to help different learners in equitable and relevant ways by striking a balance between technological innovation and human direction.

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