Actionable companion · Retention & transfer
Learning with agency: Can AI teach me employability? A multi-national study in three countries
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.
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:
This paper examines the impact of using an Artificial Intelligence (AI) teacher for current Higher Education (HE) students from three countries. The study utilized an AI avatar powered by a fine-tuned Large Language Model (LLM), OIMISA, which is trained solely for teaching and learning applications. The AI teacher provided a 9-lesson course on employability and transferable skills. In total 207 students across the three institutions enrolled in the programme. The results demonstrate a noteworthy completion rate of over 47%, along with high levels of engagement across all student cohorts and high satisfaction rates from the students. These show the potential for AI-based virtual teachers across countries for students of HE compared to the use of MOOC platforms.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
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 AI teacher is an AI-powered digital teacher that is available as a web-based application which students can log in to without the need of downloading a special application. This AI teacher can support students during their entire learning journey of the set course (Figure 1). It teaches them the specific skills of the course while also quizzing them on the content (Figure 2). The AI teacher can also listen to their voice and understand their questions, giving them tailored support and answers to their individual questions in real time. Where students do not want to speak to the AI teacher, they can also communicate with it by typing.
Limitations and uncertainty · source passage
No study comes without limitations. In this study, no demographic data were collected from participants. Motivation to join the programme, age, gender, area of study could all be variables that can correlate with the interactions with the AI teacher and can be explored in further studies. A larger sample size would also be beneficial to perform inferential statistics. The study focused on students’ engagement with an AI teacher on topics related to employability and transferable skills development. Although the AI teacher is designed and development to be content agnostic, future studies could explore which domains are suitable for this form or learning and which may be less so. Another area for future studies could be a direct or controlled comparison with a traditional MOOC platform or other pedagogical methods to provide a reference/benchmark on the effectiveness of the AI teacher.
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.
- Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
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
Dev Aditya; Krizia Silvestri; Pauldy CJ Otermans. Can AI teach me employability? A multi-national study in three countries. Frontiers in Artificial Intelligence (2024-11-18). https://doi.org/10.3389/frai.2024.1461158
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.