A Systematic Literature Review on Artificial Intelligence in English Language Education

A Systematic Literature Review on Artificial Intelligence in English Language Education

Vekneswary Krishnan, Hafiz Zaini*

Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

*Correspondent Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0002

Received: 21 December 2024; Accepted: 25 December 2024; Published: 28 January 2025

ABSTRACT

This systematic literature review explores the application of artificial intelligence (AI) in English language education, focusing on studies published between 2020 until 2024 from Malaysia and international contexts. A total of 15 articles were analysed to examine the impact of AI-driven tools such as tutoring systems, game-based apps, and automated assessment tools on learning outcomes, student engagement, and language proficiency. The findings highlight significant improvements in motivation, personalized learning experiences, and enhanced language skills facilitated by AI platforms. However, ethical considerations and regional disparities in technology access pose challenges. Recommendations for future research emphasize longitudinal studies on AI’s efficacy, the development of advanced AI tools, and comprehensive educator training. This review underscores AI’s transformative potential in shaping modern language education while advocating for continued exploration and ethical integration.

Keywords: artificial intelligence, English language education, AI tools, systematic literature review

INTRODUCTION

In recent years, the integration of artificial intelligence (AI) in education has garnered significant attention, particularly in the realm of language learning. AI technologies have the potential to revolutionize English language education by providing personalized learning experiences, automating administrative tasks, and offering new tools for assessment and feedback (Zawacki-Richter et al., 2019). This systematic literature review aims to explore the current state of AI in English language education, identifying key trends, challenges, and future directions. AI’s ability to analyse large datasets and adapt to individual learner needs makes it a powerful tool in educational contexts. According to Luckin et al. (2019), AI can support teachers by identifying students’ strengths and weaknesses, thereby enabling more targeted and effective teaching strategies. This capability is particularly beneficial in English language education, where students often have varying levels of proficiency and learning styles.

One of the primary applications of AI in English language education is through intelligent tutoring systems (ITS). These systems use AI algorithms to provide immediate feedback and personalized instruction, helping students to improve their language skills at their own pace. For instance, Chen and Chen (2020) found that AI-based ITS significantly improved students’ reading comprehension and vocabulary acquisition compared to traditional methods. Moreover, AI-powered language learning applications and platforms have become increasingly popular. These tools often employ natural language processing (NLP) to facilitate interactive language practice, offering learners a more engaging and immersive experience. As stated by Liu et al. (2020), such applications can enhance learners’ motivation and autonomy, leading to better learning outcomes.

In addition to ITS and language learning applications, AI has also been used to develop automated essay scoring systems. These systems utilize machine learning algorithms to assess students’ writing quality and provide constructive feedback, thereby reducing the burden on teachers and allowing for more timely feedback. A study by Zhang et al. (2020) demonstrated that automated essay scoring systems could achieve reliability and validity comparable to human raters. Despite the promising potential of AI in English language education, there are several challenges that need to be addressed. Ethical concerns, such as data privacy and the potential for biased algorithms, are significant issues that must be carefully considered (Holmes et al., 2021). Additionally, the integration of AI into educational settings requires substantial investment in technology and professional development for teachers.

Furthermore, the effectiveness of AI in language education depends largely on the quality of the underlying algorithms and the data used to train them. Biases in training data can lead to unfair or inaccurate outcomes, which can negatively impact learners. According to Williams et al. (2021), it is crucial to ensure that AI systems are transparent and accountable, with mechanisms in place to address any biases or errors. Another important aspect is the need for ongoing research to evaluate the long-term impacts of AI on language learning. While many studies have shown positive short-term effects, there is still limited evidence on how AI influences learners’ language proficiency and engagement over extended periods. As noted by Chen et al. (2021), longitudinal studies are essential to understanding the sustained impact of AI in education.

In conclusion, AI holds great promise for enhancing English language education through personalized learning, automated assessment, and interactive applications. However, realizing this potential requires careful consideration of ethical issues, investment in technology, and ongoing research to ensure the effectiveness and fairness of AI systems. This systematic literature review aims to provide a comprehensive overview of the current state of AI in English language education, highlighting key trends, challenges, and future directions for researchers and practitioners.

Figure 1.0 Role of Artificial Intelligence in Education

Research Objectives

The objectives of this research were:

1. To identify the various AI-driven tools and platforms that have been utilized in English language education.
2. To determine the effectiveness of AI applications on enhancing students’ English language proficiency and engagement.

Research Questions

The research questions of this study were:

1. What types of AI-driven tools and platforms have been utilized in English language education?
2. How AI applications effected students’ English language proficiency and engagement?

BACKGROUND OF THE STUDY

Artificial intelligence (AI) has made significant strides in various sectors, and education is no exception. The incorporation of AI in education has introduced new paradigms of learning and teaching, enabling more personalized, efficient, and engaging educational experiences (Zawacki-Richter et al., 2019). English language education has seen substantial advancements using AI, which can tailor instruction to individual student needs, automate administrative tasks, and provide innovative tools for assessment and feedback (Holmes et al., 2021).

The definition of Artificial Intelligence (AI)

Russell and Norvig (2020) define AI as a system capable of mimicking human thought and learning processes, while Brown et al. (2020) emphasizes the ability of such systems to learn from data and improve performance based on experience. Stone et al. (2022) add that AI encompasses various fields, including machine learning, deep learning, and robotics. Despite the various interpretations, all these definitions highlight AI’s capability to mimic or enhance human cognitive abilities through advanced technology.

AI-Driven Tools and Platforms in English Language Education

AI-driven tools and platforms have become increasingly prevalent in English language education. These technologies leverage machine learning algorithms, natural language processing (NLP), and data analytics to enhance the learning experience. For example, intelligent tutoring systems (ITS) use AI to provide real-time feedback and personalized instruction, adapting to the learner’s progress and performance (Chen & Chen, 2020). These systems have been shown to improve reading comprehension and vocabulary acquisition, making them valuable assets in language education. Another prominent AI application is automated essay scoring (AES), which employs machine learning to evaluate and provide feedback on student writing. AES systems can offer timely and consistent assessments, which can help students improve their writing skills more efficiently than traditional methods (Zhang et al., 2020). Additionally, language learning applications that use NLP, such as Duolingo and Babbel, provide interactive and immersive practice, enhancing learner engagement and autonomy (Liu et al., 2020).

Impact of AI on Language Proficiency and Engagement

The impact of AI on language proficiency and student engagement is a critical area of investigation. AI technologies have the potential to significantly enhance language learning outcomes by offering personalized and adaptive learning experiences. According to Luckin et al. (2019), AI can identify individual learning needs and tailor instruction accordingly, leading to improved language proficiency. Moreover, the interactive nature of AI-powered tools can boost student motivation and engagement, as learners receive immediate feedback and are encouraged to participate actively in their learning process. Studies have shown that AI applications can lead to better learning outcomes compared to traditional methods. For instance, Chen and Chen (2020) found that students using AI-based ITS demonstrated significant improvements in reading comprehension and vocabulary. Similarly, Zhang et al. (2020) reported that automated essay scoring systems provided reliable and valid assessments, helping students enhance their writing skills. These findings underscore the potential of AI to transform English language education by making it more effective and efficient.

Ethical and Practical Challenges

Despite the promising potential of AI in English language education, several ethical and practical challenges need to be addressed. One major concern is data privacy. AI systems often require large amounts of personal data to function effectively, raising questions about how this data is collected, stored, and used (Holmes et al., 2021). Ensuring the privacy and security of student data is paramount, and educators must be vigilant about implementing robust data protection measures. Another challenge is the potential for biased algorithms. AI systems are only as good as the data they are trained on, and if this data contains biases, the resulting algorithms can perpetuate and even amplify these biases (Williams et al., 2021). This can lead to unfair to inaccurate outcomes, particularly in assessments and feedback, which can negatively impact learners. It is crucial to develop AI systems that are transparent and accountable, with mechanisms in place to identify and mitigate biases.

Investment and Professional Development

The successful integration of AI in English language education also requires significant investment in technology and professional development for teachers. Schools and educational institutions must invest in the necessary infrastructure to support AI technologies, including hardware, software, and internet connectivity (Chen et al., 2021). Additionally, teachers need to be trained in how to effectively use AI tools and platforms, which may require ongoing professional development and support. According to Liu et al. (2020), one of the barriers to the widespread adoption of AI in education is the lack of teacher readiness and confidence in using these technologies. Providing teachers with the necessary skills and knowledge to integrate AI into their teaching practices is essential for maximizing the benefits of AI in language education. This includes training on how to interpret and use data generated by AI systems, as well as understanding the ethical implications of AI use in the classroom.

METHODOLOGY

This systematic literature review aims to comprehensively analyse the current state of artificial intelligence (AI) in English language education by identifying AI-driven tools and platforms and evaluating their impact on student learning. The methodology for this review is designed to ensure a thorough and unbiased analysis of the existing literature, guided by the research objectives and questions.  The research design of this study is a systematic literature review, which involves a structured and methodical approach to identify, evaluate, and synthesize relevant research on the topic. This design was chosen to provide a comprehensive understanding of the existing body of knowledge, highlight key trends, and identify gaps in the literature (Kitchenham & Charters, 2007).

The literature search was conducted across multiple academic databases, including Google Scholar, PubMed, IEEE Xplore, and ERIC, selected for their comprehensive coverage of education and technology research. Articles published from 2020 to 2024 were included to ensure the review captured the most recent advancements. Relevant studies were identified using keywords such as “AI in language education,” “intelligent tutoring systems,” “automated essay scoring,” and “natural language processing in education.” To ensure the relevance and quality of the studies included in the review, specific inclusion and exclusion criteria were applied.

Table 1 Inclusion and Exclusion Criteria

Relevant studies were subjected to data extraction using a standardized form, capturing details such as objectives, methodology, AI tools or platforms used, and key findings. The extracted data were then analysed thematically to identify common themes, trends, and gaps in the literature, ensuring consistency and comprehensiveness.

Phases of the Review

The review process was divided into three main phases: planning, conducting, and reporting.

Planning Phase:

The planning phase involved defining the research objectives and questions. A detailed review protocol was developed, outlining the search strategy, inclusion and exclusion criteria, and data extraction process. A preliminary search was conducted to refine keywords and identify initial relevant studies. This phase ensured that the review was methodically structured and targeted toward addressing the specific research questions.

Conducting Phase:

During the conducting phase, a comprehensive literature search was performed across selected databases such as Google Scholar, PubMed, IEEE Xplore, and ERIC. The identified studies were screened based on the predefined inclusion and exclusion criteria. Relevant data were then extracted from the included studies. The findings were synthesized using thematic analysis, which facilitated the identification of common themes and patterns across the studies.

Reporting Phase:

In the reporting phase, the synthesized findings were compiled into a coherent narrative. Key trends, gaps, and future research directions were highlighted. The final report was prepared, encompassing the introduction, methodology, results, discussion, and conclusion sections. This comprehensive approach ensured that the review’s findings were clearly communicated and contextualized within the broader field of study.

Validity and Reliability

To ensure the validity and reliability of the review, several measures were implemented. Multiple reviewers independently screened the studies and extracted data to minimize bias and ensure consistency. Any discrepancies were resolved through discussion and consensus. Additionally, the review protocol was rigorously followed, and detailed records of the search strategy and data extraction process were maintained. These measures were crucial in maintaining the integrity and robustness of the systematic review.

Figure 2.0 Research Methodology Phase

Quality Assessment of Studies

A structured framework was employed to evaluate the quality of studies included in this systematic literature review. This framework ensured the inclusion of high-quality, relevant research while distinguishing rigorous studies from less robust ones. Each study was assessed based on five primary criteria: study design, sample characteristics, data collection and analysis, relevance, and findings. Each criterion was scored on a scale of 1 (low) to 5 (high), with a total maximum score of 25. This scoring framework is adapted from established systematic review methodologies, such as those outlined by Kitchenham & Charters (2007), ensuring a structured and transparent evaluation of study quality.

Boude, Rozo & González (2023) demonstrated strong methodological rigor, scoring 20, which classified it as high quality. Similarly, Nguyen & Pham (2022) scored 25, showcasing exemplary methodological design and findings, making it a significant contributor to this review. In contrast, studies like Smith & Johnson (2023) scored only 12 due to methodological weaknesses and insufficient data analysis, categorizing it as low quality. This systematic scoring ensured that the review focused on studies with robust evidence and minimized the influence of less reliable research.

FINDINGS AND DISCUSSIONS

A total of 15 articles have been collected, analysed, and tabulated for the researchers’ investigation. Notably, all articles were sourced from the period between 2020 to 2024. This selection strategy was intended to validate the relevance of the current study by aligning it with recent issues and trends. Additionally, it serves as a guide for the researcher in addressing the proposed research questions effectively.

Table 2 Table shows the details of each article

Table 3   Table shows the details thematic analysis of articles

This thematic framework highlights the multifaceted contributions of AI in English language education, emphasizing both its benefits and the areas requiring further exploration.

A review of 15 articles from various countries, including Malaysia, reveals significant insights into the application and impact of AI in English language education. These studies, published between 2020 and 2024, explore diverse AI-based platforms and their effects on learners’ motivation, engagement, and overall language proficiency. The trend over this period demonstrates an increase in AI-related research in English language education, peaking in 2023.

Figure 3.0   Trends in AI Research Publications (2020-2024)

The research encompasses a variety of educational levels and methodologies, providing a comprehensive understanding of the current landscape. The studies utilized a wide range of AI tools and platforms, including AI-driven tutoring systems, game-based learning apps, automated essay scoring systems, AI-powered vocabulary apps, and adaptive learning platforms. For example, platforms like WordTrek, CHEN-slate, Kahoot, Quizizz, and Quizalize were frequently mentioned for their effectiveness in enhancing vocabulary learning and classroom engagement (Boude, Rozo, & González, 2023; Chen, 2023; España-Delgado, 2023).

A recurrent theme across the studies is the significant increase in student motivation and engagement attributed to the use of AI tools. AI-driven platforms like WordTrek and CHEN-slate were found to encourage active participation and increase motivation among learners (Boude, Rozo, & González, 2023; Chen, 2023). Similarly, game-based platforms such as Kahoot, Quizizz, and Quizalize improved interaction and classroom dynamics, fostering a more engaging learning environment (España-Delgado, 2023). Personalization is a critical advantage of AI in education, allowing for tailored learning experiences that cater to individual student needs. Studies highlighted the effectiveness of adaptive learning platforms in personalizing learning paths and improving academic performance. AI-driven personalized learning systems were particularly beneficial in addressing the diverse learning paces and styles of students (Garcia & Torres, 2022).

The use of AI has shown substantial improvements in various language skills, including vocabulary acquisition, writing, and speaking. AI-powered vocabulary apps and writing assistants significantly enhanced students’ retention and accuracy (Ahmed & Ali, 2020; Kumar & Singh, 2021). Additionally, AI-driven speech recognition tools were effective in boosting speaking proficiency and confidence among learners (Kim & Lee, 2023). AI-based assessment tools, such as automated essay scoring systems, provided timely and constructive feedback, which is crucial for language learning. These tools not only reduced grading time for educators but also enhanced the reliability of assessments (Hassan & Wong, 2020). The feedback systems helped students understand their mistakes and improve their writing and comprehension skills more effectively (Tanaka & Yamamoto, 2022).

The perception of AI among educators and students was generally positive, with many acknowledging the benefits of AI in enhancing learning experiences and outcomes. Teachers appreciated the support provided by AI tools in managing classroom activities and assessments, while students found AI applications engaging and helpful for their learning (Hassan & Wong, 2020; Smith & Johnson, 2023). Despite the numerous benefits, some studies pointed out challenges and ethical concerns associated with AI in education. Issues such as data privacy, the accuracy of AI systems, and the potential for reduced human interaction were highlighted. Educators expressed concerns about the reliability of AI tools and the need for proper training to effectively integrate AI into the classroom (Smith & Johnson, 2023).

The impact of AI varied across different educational levels. For instance, higher education institutions reported significant improvements in language proficiency and student engagement, while secondary schools highlighted the benefits of AI in vocabulary acquisition and motivation (Nguyen & Pham, 2022; Mokhtar & Rahim, 2021). The adaptability of AI tools to cater to various educational contexts was a key factor in their success. Studies from Malaysia emphasized the role of AI in addressing specific educational challenges, such as large class sizes and varying proficiency levels. AI tools were particularly useful in providing personalized learning experiences in diverse classroom settings. The findings from Malaysia were consistent with global trends, indicating the universal applicability of AI in enhancing language education (Abdullah & Harun, 2021).

The review suggests several areas for future research, including the long-term impact of AI on language learning, the development of more sophisticated AI tools, and strategies to address ethical concerns. Continued exploration and innovation in AI technology are essential to fully realize its potential in education. There is also a need for comprehensive training programs for educators to effectively integrate AI into their teaching practices (Smith & Johnson, 2023). The collected studies provide a robust understanding of the benefits and challenges associated with AI in English language education. AI tools have demonstrated significant potential in enhancing motivation, personalization, language proficiency, and assessment practices. However, addressing the ethical concerns and ensuring effective implementation are crucial for maximizing the impact of AI in educational settings. The findings highlight the transformative role of AI in education and the need for ongoing research and development in this field.

CONCLUSION

The systematic literature review on AI in English language education highlights the transformative potential of AI-driven tools in enhancing learning outcomes, motivation, and personalized instruction. The analysis of 15 articles, spanning from 2020 to 2024 and encompassing research from Malaysia and other countries, demonstrates that AI platforms significantly improve student engagement, vocabulary acquisition, and skill development while providing timely feedback and personalized learning experiences. Despite the notable benefits, the review also underscores the importance of tackling ethical concerns, such as data privacy and the accuracy of AI systems. To maximize the impact of AI in education, ongoing research, innovation, and comprehensive training for educators are crucial. The findings affirm the pivotal role of AI in shaping the future of English language education, emphasizing the need for continued exploration and development in this dynamic field.

RECOMMENDATIONS

Future researchers should focus on exploring the long-term impacts of AI integration in English language education to understand its sustained effectiveness and potential areas for improvement. Investigating the development of more advanced and user-friendly AI tools tailored to diverse educational contexts will be crucial. Additionally, addressing ethical concerns, such as data privacy and the reliability of AI systems, should be a priority to ensure safe and effective implementation. Comprehensive training programs for educators on the integration and utilization of AI tools in their teaching practices are essential. Researchers should also consider comparative studies across different educational levels and regions to identify best practices and strategies for optimizing AI’s benefits globally. By addressing these areas, future research can further enhance the transformative role of AI in language education.

Limitations fostering

One limitation of this systematic literature review on AI in English language education lies in the potential bias introduced by the selection criteria and availability of published studies. Despite efforts to include a diverse range of sources, the exclusion of unpublished or non-English literature may limit the comprehensiveness of the findings. Additionally, the focus on articles published between 2020 and 2024 may not capture the full spectrum of advancements and changes in AI technology over time. Furthermore, while the review encompasses studies from Malaysia and various international settings, regional variations in educational systems and access to technology may affect the generalizability of findings. Future research could address these limitations by broadening the search criteria, including more recent publications, and incorporating a wider range of perspectives and methodologies to provide a more nuanced understanding of AI’s impact on English language education.

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