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the framework of the digital economy, the need to be able to learn a language effectively
increased significantly. English is now the universal communication and business language,
as well as academic and electronic communication. Consequently, ESL learners will have
to acquire effective linguistic competencies, especially in knowledge of vocabulary and
accuracy of pronunciation so that they can effectively communicate in global contexts.
The acquisition of vocabulary can be regarded as one of the most basic components of
second language learning since this process has a direct impact on the capability of the
learner to comprehend the text, convey ideas, and engage in any meaningful
communication (Nation, 2013). In the event that learners lack adequate vocabulary
knowledge, then they might not understand the spoken or written language regardless of
having a good idea of the structures of grammar. On the same note, pronunciation is a
key element in a second language communication. Proper pronunciation assists learners
to express themselves well, and it also makes their speech to be understood by others.
Lack of good pronunciation on the other hand, can cause misunderstandings and
breakdown in communication. The conventional method of teaching pronunciation is
prone to chances of using teacher demonstration and repetition which might not offer an
adequate individualized feedback to students. New opportunities in the field of
pronunciation training can be realized by the AI-based technologies that offer immediate
corrective feedback and enable the learners to repeatedly practice in an autonomous
learning environment, especially through the use of automatic speech recognition (ASR)
systems (Liakin, Cardoso, and Liakina, 2015). The recent phase in application of AI has
brought in numerous intelligent learning systems which facilitate the learning of a second
language. An example is that intelligent tutoring systems can scan the progress of the
learners and adjust the instructional contents according to their unique needs and
performance rates. Equally, natural language processing technologies can be used to
analyze the language usage of the learners and give recommendations on how to improve
it. These characteristics assist in designing a more interactive and individualized teaching
setting, which is also crucial to efficient language acquisition (Holmes, Bialik, and Fadel,
2019). Moreover, AI tools are capable of assisting learners in training language skills out
of the classroom, therefore, encouraging autonomous learning and constant involvement
with the language. Empirical studies have shown that AI technologies are effective in
language teaching more and more often. Research has revealed that AI-based learning
environments have the potential to greatly enhance vocabulary acquisition in learners
through the provision of contextualized learning resources, interactive activities as well as
adaptive feedback systems (Godwin-Jones, 2019). Also, AI-based pronunciation aids can
enable learners to obtain an in-depth feedback on the pronunciation mistakes to enhance
their phonetic accuracy and fluency in speaking. The technologies also minimise the
anxiety that learners have when it comes to speaking in a second language since they
enable the students to practice in their own time and privacy. The other significant
benefit of AI-based language learning systems is that it can be used to enable customized
learning. The common challenges in traditional language classrooms can be categorized
as the large number of students in the classroom, a lack of learning time, and varying
levels of student proficiency. These aspects render the instructors unable to give
personalized attention to each student. This limitation is resolvable with the help of AI
technologies that examine the progress of learners and modify learning activities based
on the analysis outcomes. Zawacki-Richter et al. (2019) state that AI-based educational
systems can have a revolutionary impact on higher education as they allow creating
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