Actionable companion · Feedback & scaffolding

Learning with agency: Integrating AI in Pakistani ESL classrooms: Teachers’ practices, perspectives, and impact on student performance

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract The global rise of Artificial Intelligence (AI) in English as a Second Language (ESL) education has shown promise, yet its application in resource-constrained contexts like Pakistan remains underexplored. This study examines the integration of AI tools in Pakistani ESL classrooms, with a focus on (1) teachers’ instructional practices, (2) student learning outcomes, and (3) implementation challenges. Using a mixed-methods approach, data were collected through classroom observations, focus group discussions, and pre- and post-tests on vocabulary and writing skills administered to 100 undergraduate students (50 in the experimental group and 50 in the control group) over 16 weeks. The experimental group, taught with AI tools such as Grammarly and QuillBot, demonstrated significantly greater gains in vocabulary (+45%, d = 1.12) and writing performance (+46%, d = 1.03) compared to the control group. Qualitative findings revealed that while AI tools supported grammar correction and vocabulary enhancement, their effectiveness was limited by infrastructural constraints, insufficient teacher training, and cultural misalignment in language feedback. The study concludes that AI can meaningfully enhance ESL instruction when paired with teacher facilitation and localized design. It offers novel insights into culturally responsive AI integration in under-resourced educational contexts.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The current study employed an exploratory sequential mixed-methods design, which involves collecting and analyzing qualitative data first, followed by a quantitative phase to validate and expand on the findings [22]. This design was chosen to comprehensively explore the integration of AI tools in Pakistani ESL classrooms, capturing both the depth of participants’ experiences and the breadth of AI’s impact on learning outcomes. The study adopts a pragmatic paradigm, prioritizing practical solutions to the research problem and leveraging the strengths of both qualitative and quantitative methods to provide a holistic understanding of AI integration in ESL education [23]. From an epistemological perspective, the study is grounded in pragmatism, which emphasizes the practical application of research and the use of multiple methods to address complex problems [24]. From an ontological perspective, the study recognises that the sociocultural and infrastructural contexts of Pakistani ESL classrooms shape multiple realities. This perspective aligns with the study’s aim to explore how AI tools are perceived and utilized in a specific, resource-constrained environment, where participants’ experiences may differ significantly from those in more technologically advanced settings.

Limitations and uncertainty · source passage

A key limitation of this study is its use of convenience sampling from a single private university, restricted to high-performing students (CGPA ≥ 3.5). This selective sampling introduces potential bias and limits the generalizability of the findings to the broader ESL learner population in Pakistan, particularly those in public institutions, rural areas, or with varying academic abilities. Additionally, the digital infrastructure at the study site may not reflect conditions in less resourced settings, further constraining external validity. While the study provides valuable insights into AI-assisted learning, future research should involve more diverse and representative samples, employ randomized sampling, and investigate long-term outcomes across different educational contexts.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Tahir Saleem; Aisha Saleem; Dr Muhammad Aslam. Integrating AI in Pakistani ESL classrooms: Teachers’ practices, perspectives, and impact on student performance. PLOS One (2025-09-30). https://doi.org/10.1371/journal.pone.0333352

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.