A comprehensive Review of AI as a Catalyst that helps in Second Language Speaking Proficiency development

A comprehensive Review of AI as a Catalyst that helps in Second
Language Speaking Proficiency development

Introduction
The capacity to speak fluently in a second language (L2), especially
in the context of spoken speech, has long been accepted as one of
the most difficult skills for the acquirement of L2 learners (Xu &
Ismail, 2025). Given that most students grow up speaking as a class,
traditional classroom setups are frequently not suitable for
authentic oral discussion, and instructor feedback is limited,
whether limited by time or resources. In such an environment,
artificial intelligence (AI) offers an exciting opportunity for
language pedagogy by offering personalized feedback on the fly that
addresses exactly each and every learner’s individual needs. This
essay, therefore, consolidates current scholarship on how AI can
drastically enhance students’ speaking skills, by taking into
account their fluency, pronunciation, confidence, affective factors,
such as anxiety, and their learning autonomy.

Theoretical Foundations
AI methods for language learning match fundamental elements of
Second Language Acquisition (SLA) as they are designed to ensure the
deliberate practice, feedback, and learner autonomy that lies in the
very heart of Second Language Acquisition and Language Learning, AI
interventions. According to Ericsson et al. (1993) deliberate
practice (i.e. practice with feedback that is focused and
repetitive) is critical to reaching high level of competency. AI
services help realize this principle, allowing subjects to perform
speaking tasks repeatedly by allowing them to be coached to correct
errors instantaneously. Moreover, it enables a self-regulated
learning approach, which is associated with higher motivation and
better academic performance, since learners are able to
self-regulate by setting goals, tracking the progress and modify
their strategies.

Personalised Feedback and Adaptive Learning.
One of the most significant contributions of AI in the area of
speaking skills is the ability of AI to give individualized feedback
at unprecedented scale based upon that feedback. AI-based automatic
speech recognition (ASR) systems detect pronunciation, syntax,
lexical choice, syntax as well as other mistakes, in an automatic
system to instantaneously correcting those errors (Babayeva, 2025).
Such feedback is essential; learners will need to control the output
while the speech act can still be registered in memory, so that they
could correct errors successfully in an effort to consolidate.
According to research studies based on empirical data,
AI-facilitated feedback results in a statistically considerable
improvement in speaking proficiency and precision for language
compared to the conventional practice style (Zou et al., 2025; Xu &
Ismail, 2025). AI tools also adapt difficulty and input order to the
ability of the person to process it, a policy backed by evidence of
SLA on comprehensible input.

Structural competence, fluent pronunciation, and structural
competency.
However, evidence regarding the contribution of AI on fluency and
pronunciation is presented in multiple research fields on empirical
foundations. Automated speech recognition and pronunciation
applications such as ELSA Speak, and Speech ace have already
demonstrated improvements in pronunciation accuracy, prosody, and
general clarity of oral performance mainly because of features such
as receiving real-time feedback or adaptability in the course of
practice (Shahab et al., 2025; Babayeva, 2025). Students utilizing
these tools demonstrate measurable advances in phonetic production
as well as intonation patterns, though achieving the levels typical
native speakers can (Georgiou, 2025). Further, the study shows that
AI-supported practice has a positive impact on fluency and decreased
hesitant-speaking (suggesting that automaticity increases in SL).
Reducing Anxiety and Enhancing Communicative Confidence
It has been found that reduced anxiety and increased communicative
confidence are also beneficial. Speaking anxiety is a recognised
impediment to L2 oral performance. There is empirical evidence to
show the positive contributions of AI chat bots in augmenting not
only linguistic performance but also decreasing foreign language
speaking anxiety (FLSA), increasing the confidence and willingness
to attend speaking tasks (Zou et al., 2025; Shahab et al., 2025).
The nature of these interactions — low stakes, non judgmental —
encourages learners to practice more often without the concern of
being criticized, which aligns with theories, which emphasize
affective factors in SLA. This decrease in anxiety is enhanced by
the immediacy of feedback and the learner’s capacity to practice
problematic speech patterns until they master them.
Learner Autonomy and Self Regulated Learning
Autonomy of the Learner and Self-Regulated Learning AI-driven
speaking has contributed to learner self-control by helping learners
to guide their learning processes. Adaptive systems facilitate
self-regulation by supplying continuous task progress metrics,
tailored targeted correction suggestions, and scaffolded tasks based
on learners’ needs. AI integration leads to increased motivation
and self-regulation, key drivers of language development (Frontiers
in Psychology, 2023). When learners feel they know what their
strengths and weaknesses are thanks to their AI feedback, they
practice their metacognition where they reflect on past learning and
speed up progress on tasks.
The data supporting AI being beneficial is abundant, but academic
consensus has stressed the need to supplement this with good teacher
practice. A literature review suggests that AI tools should be put
into practice only alongside regular pedagogical learning with a
view to developing balanced skills and overcoming limitations such
as AI bias, over-reliance and misunderstanding in context (Xu &
Ismail, 2025; International Journal of Research and Innovation in
Social Science, 2025). A second critical issue was to facilitate the
integration of pedagogical approaches, particularly with respect to
how the implementation of AI may impede the traditional teaching
practice due to its bias and reliance on a particular AI technology.
A hybrid instructional method of blending teacher-directed
instruction with AI-guided practice could combine human intuition
and technical adaptability.

Constraints and Hints for the Future.
While findings are encouraging, AI is not without limitations for
speaking instruction. Existing applications might face the
limitations of contextual relevance, culture and sensitive pragmatic
competence that human interlocutors develop spontaneously. Finally,
access to technology, digital literacy and data privacy issues act
as obstacles for equitable adoption. Future research should address
long-term impacts, the incorporation of suprasegmental features,
beyond those of segmental speech, and the ethical guidelines for
responsible AI in learning environments.

Conclusion.
Finally, AI has proven to be a valuable support in fostering L2
speaking proficiency by providing personalized feedback,
facilitating fluency and pronunciation improvement, decreasing
speaking fear, and supporting learner autonomy. Empirical evidence
continually shows significantly better speaking performances with AI
interventions. While we can witness significant improvements in
controlled settings, we should consider this in the context of a
pedagogy that includes human oversight, ethical and instructional
approaches that complement each other. With such trends in AI
technology, what has attracted great potential in second language
pedagogy is this ability to extend access to effective oral practice
and to speed up learners’ development.

References
1. Babayeva, K. R. (2025). AI tools for speaking fluency and
pronunciation. European International Journal of Philological
Sciences.
2. Frontiers in Psychology. (2023). Artificial intelligence in
language instruction: impact on English learning achievement,
motivation, and self-regulated learning.
3. Georgiou, G. P. (2025). Enhancing nonnative speech perception and
production through an AI-powered application. arXiv.
4. Shahab et al. (2025). The impact of AI-powered pronunciation apps
on EFL learners’ speaking skill.
5. Xu, B., & Ismail, H. H. (2025). Evaluate the effectiveness of
using AI in spoken English: a literature review.
6. Zou et al. (2025). Input-based instruction and AI’s effect on
EFL speaking skills.