AI evidence extraction

Integrating AI into Learning English: University Students’ Perceptions and the Roles of Traditional Methods

Luy Dau · Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (online) · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.60087/jklst.v4.n2.001

Methodology & findings

Study design

Mixed-methods design combining quantitative survey (n=100) grounded in Technology Acceptance Model (TAM) and qualitative semi-structured interviews (n=10)

Sample

N = 110, 4 groups

Primary method

Technology Acceptance Model (TAM) framework for questionnaire analysis; thematic analysis or content analysis for semi-structured interviews (specific statistical methods not detailed in abstract)

Main result

The study found that "the most frequently used AI tools were Google Translate, ChatGPT, Duolingo and Grammarly" with "vocabulary, reading and writing self-reported as the most improved skills." Additionally, "Students generally perceived AI tools as beneficial" though "moderate concerns were raised regarding the trust of AI-created content and the need for guided support in optimizing AI-based learning experiences."

Reports effect sizes.

Research paradigm

Mixed methods (pragmatist/positivist-interpretivist blend)

Author conclusions

The authors conclude that "The majority of students expressed a strong desire for teachers to integrate AI into conventional forms of pedagogy to enhance motivation and engagement in learning English," and that "This requires further research on various methods to effectively apply AI in teaching and language skills."

Risk of bias

Selection bias: non-random sampling from two universities in Ho Chi Minh City, Vietnam; Response bias: self-reported perception data on AI tool usage and skill improvement; Confounding variables: no control group to isolate AI effects from other learning factors; Limited generalizability: sample from non-English major students in Vietnam only; Selection bias: Non-English major students from two specific universities in Ho Chi Minh City; Self-report bias: Reliance on students' self-reported skill improvements; Small qualitative sample: Only 10 semi-structured interviews for in-depth insights; Geographic limitation: Data from Vietnam universities only; Selection bias: Non-English major students only; non-random sampling likely; Confounding: No control group for comparison of AI versus traditional methods; Attrition: Not reported for interview participants; Subjective reporting: Self-reported skill improvement without objective measurement; Geographic limitation: Two universities in Ho Chi Minh City only; Social desirability bias: Potential reporting bias in technology acceptance items

Open questions raised

Further research on various methods to effectively apply AI in teaching and language skills; exploration of effective integration of AI into conventional pedagogical approaches; examination of how to provide guided support in optimizing AI-based learning experiences
The authors identify the need for further research on various methods to effectively apply AI in teaching and language skills, and emphasize the importance of guided support in optimizing AI-based learning experiences.
Further research needed on various methods to effectively apply AI in teaching and language skills; investigation of how to guide students in optimizing AI-based learning experiences; examination of strategies to address trust concerns regarding AI-generated content.

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