Evidence map›Paper›PMID 40745384›Full record

ArticleScientific reports2025

A qualitative study on ethical issues related to the use of AI-driven technologies in foreign language learning.

Marcel Pikhart, Blanka Klimova

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Marcel PikhartCentre for Basic and Applied Research & Department of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, 500 03, Czech Republic. marcel.pikhart@uhk.cz.
Blanka KlimovaDepartment of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, 500 03, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current situation in the use of AI-driven technologies in education has seen an unprecedented rise, however, the impact of these technologies from the perspective of ethical issues is largely unknown. The aim of the research is to provide a clear overview of the potential risks that could be related to the use of AI-driven apps in foreign language learning as it is perceived by the users of these apps. The data were collected by the questionnaires distributed among the students of computer science and also the users of various AI-driven apps for their language classes. The data collection was qualitative with the aim of analyzing their feelings, opinions, and experience. The tool to analyze the data collected was liwc.app software that is a gold standard for textual analysis when looking for values, feelings, and opinions. The results clearly show that the users, who are professionals in ICT, are fully aware of the potential threats but due to the fact that they are knowledgeable about the topic they are not prone to be hacked or misused in a way the general public can be. The results also highlight several grave ethical issues related to privacy in foreign language learning apps. This is the first research drawing attention to the topic of ethical issues when using AI-driven foreign language learning apps and it can be considered a pioneering work in this neglected area.

Indexed as

Artificial IntelligenceLearningAdultFemaleHumansLanguageMaleQualitative ResearchSurveys and QuestionnairesDigital learningEthical challengesHigher educationSecond language acquisitionThreats of AI

Identifiers

PMID40745384
PMCPMC12313929

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.