Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Vernacular english literacy practices among language students using AI: A case study in Saudi Arabia

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 Recent developments in Artificial Intelligence (AI) have encouraged teachers of English as a Foreign Language (EFL) to evaluate its potential benefits to the process of language learning. However, little is currently known about how language learners use AI outside the EFL classroom. Accordingly, this study addresses the underexplored area of informal, AI-mediated English literacy practices among EFL learners. Data from a qualitative case study involving 12 postgraduate students who participated in a focus group and retrospective interviews at a Saudi Arabian university were analyzed. Content analysis revealed the students engaged in English literacy practices using AI, and that these were categorized according to their usage in three domains: (1) practices for English language development, (2) to assist with daily life, and (3) to benefit academic studies. Approximately 50% of AI engagement in the English language aimed to practice the participants’ English reading, writing, and speaking skills, while leveraging customized learning opportunities. In addition, examples serving daily life functions were related to personal and vocational activities, and those serving academic life focused on preparing for examinations, academic research, and university assignments. The study therefore defined the concept of AI-mediated English literacy practices, ultimately providing insights into how language students engage in informal AI-mediated literacy practices in EFL settings. Thus, the findings reveal valuable pedagogical insights to inform EFL teachers.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

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 participants were 12 postgraduate students attending a single university in Riyadh, Saudi Arabia. The data collection started on December 5th, 2024 and lasted for five successive weeks. The data collection procedures, including the duration, participants, and tools used, are summarized in Table 1. As male and female students are separated in most educational settings in Saudi Arabia, only female students were invited to participate. The researcher was female and inviting male students would not yield insightful data, as building rapport would be a challenge for cultural and religious reasons. These female students were accessible to the researcher and had previously reported using AI in their daily lives. The sampling strategy was purposive [48] and the sampling criteria were as follows: (1) postgraduate students majoring either in Human Resources Management (HRM) or Internal Audit and Governance, and (2) who had experienced using AI apps or websites in English over a period of at least two months (Table 2). The combination of purposive, chain, and convenience sampling was employed to address the research aim effectively. Purposive sampling was first used to select participants who met the abovementioned specific criteria. This ensured that all participants had relevant experience with AI-mediated English literacy practices. Chain sampling (snowball sampling) was then used, where initial participants referred other eligible students from their networks, thereby helping to reach participants who were not easily identifiable through other means. Finally, convenience sampling was applied within university premises to recruit additional participants who were readily available and willing to participate. This multi-strategy approach is common in qualitative case studies where access to specific populations is limited, and allowed the researcher to achieve the target sample size of 12 participants within the available timeframe.

Limitations and uncertainty · source passage

Understanding the literacies of these EFL students poses pedagogical implications for the process of English learning, since learners performed more than half of the literacies for English language development. These implications are offered as tentative, context-specific observations derived from a small qualitative case study, rather than as generalizable claims about the effectiveness of AI-mediated learning. Instructors can encourage learners to engage in AI-mediated English literacies outside the classroom to support their language proficiency and generate personalized learning content. They can also encourage them to engage in verbal interactions with ChatGPT to practice their speaking skills, as it interacts in a human-like conversational style. However, they should practice with other human beings and not rely entirely on AI-generated interaction. Instructors could also encourage learners to use AI as a private tutor because it is available 24/7, and is fast, convenient, and cheaper than private tutoring. Moreover, AI-mediated literacies can enable learners to receive immediate feedback on their writing or speaking tasks, as well as scaffolding vocabulary learning. EFL learners can use the affordances of AI to enhance the literacies related to their vocational life when writing resumes and emails without encouraging overreliance. Moreover, these practices could inform curriculum designers as they design learning materials to align with the students’ daily needs and academic needs in real 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.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. 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.
  2. 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.
  3. 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.
  4. 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

Nada Bin Dahmash. Vernacular english literacy practices among language students using AI: A case study in Saudi Arabia. PLOS One (2026-09-15). https://doi.org/10.1371/journal.pone.0358237

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.