Evidence map›Paper›PMID 40567735›Full record

ArticlePeerJ. Computer science2025

Designing AI-powered translation education tools: a framework for parallel sentence generation using SauLTC and LLMs.

Moneerh Aleedy, Fatma Alshihri, Souham Meshoul, Maha Al-Harthi, Salwa Alramlawi, Badr Aldaihani, Hadil Shaiba, Eric Atwell

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Moneerh AleedyDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Fatma AlshihriDepartment of Translation, College of Languages, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.ORCID 0000-0001-7664-9218
Souham MeshoulDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Maha Al-HarthiDepartment of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Salwa AlramlawiDepartment of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Badr AldaihaniPrincess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Hadil ShaibaDepartment of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Eric AtwellSchool of Computer Science, University of Leeds, Leeds, United Kingdom.ORCID 0000-0001-9395-3764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Translation education (TE) demands significant effort from educators due to its labor-intensive nature. Developing computational tools powered by artificial intelligence (AI) can alleviate this burden by automating repetitive tasks, allowing instructors to focus on higher-level pedagogical aspects of translation. This integration of AI has the potential to significantly enhance the efficiency and effectiveness of translation education. The development of effective AI-based tools for TE is hampered by a lack of high-quality, comprehensive datasets tailored to this specific need, especially for Arabic. While the Saudi Learner Translation

Indexed as

AI-based translation technologyAI-powered translation educationCorpus annotationDidactic corpus

Identifiers

PMID40567735
PMCPMC12190550

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.