Between Shortcut and Ethics: Navigating the Use of Artificial Intelligence in Academic Writing Among Indonesian Doctoral Students DOI Creative Commons
Hardiyanti Pratiwi,

Suherman Suherman,

Hasruddin

et al.

European Journal of Education, Journal Year: 2025, Volume and Issue: 60(2)

Published: March 31, 2025

ABSTRACT Artificial intelligence (AI) has emerged as a transformative tool in academic writing, leveraging advanced algorithms and natural language processing to significantly improve efficiency, quality productivity. This study investigates the use of AI tools among Indonesian doctoral students, with particular focus on ethical standards their impact critical thinking. Adopting phenomenological approach, research involved 81 participants who provided data through open‐ended questionnaires, which were analysed thematically. The findings reveal that tools—such ChatGPT, Deepl, Zotero, Scite AI, Connected Papers Humata—are extensively used for generating ideas, brainstorming, analysis, drafting, grammar correction, translation literature management. While students acknowledge advantages enhancing speed they express concerns regarding its implications potential Key issues include maintaining integrity, ensuring manual verification AI‐generated content avoiding overreliance. underscores that, although facilitates idea generation technical tasks, it may undermine thinking analytical skills. To uphold preserve intellectual rigour, advocates balanced positioning complement to, rather than replacement for, scholarly practices.

Language: Английский

Modelling Generative AI Acceptance, Perceived Teachers' Enthusiasm and Self‐Efficacy to English as a Foreign Language Learners' Well‐Being in the Digital Era DOI

Fangwei Huang,

Yongliang Wang, Haijing Zhang

et al.

European Journal of Education, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

ABSTRACT As artificial intelligence (AI) has been integrated into foreign language (FL) education, learners' well‐being is influenced by various factors, including technological, personal and contextual elements. However, few studies explored how external internal factors jointly shape FL in the era of generative AI. To fill this gap, study explores effects AI acceptance, perceived teachers' enthusiasm self‐efficacy on investigating 613 university learners English as a (EFL). The structural equation modelling results reveal that (1) acceptance positively predicts EFL self‐efficacy; (2) does not predict (3) for receptive skills mediates relationship between acceptance/perceived well‐being, whereas productive play mediation role. This research broadens understanding antecedents extends application theory AI‐driven educational environment, providing significant pedagogical implications.

Language: Английский

Citations

43
On the relationship between EFL students' attitudes toward artificial intelligence, teachers' immediacy and teacher-student rapport, and their willingness to communicate DOI
Ran Zhi,

Yongxiang Wang

System, Journal Year: 2024, Volume and Issue: 124, P. 103341 - 103341

Published: June 20, 2024

Language: Английский

Citations

19
From Technology‐Challenged Teachers to Empowered Digitalized Citizens: Exploring the Profiles and Antecedents of Teacher AI Literacy in the Chinese EFL Context DOI Open Access
Ziwen Pan, Yongliang Wang

European Journal of Education, Journal Year: 2025, Volume and Issue: 60(1), P. 1 - 16

Published: Jan. 27, 2025

ABSTRACT Artificial Intelligence (AI) literacy has come to the spotlight, empowering individuals adeptly navigate modern digitalised world. However, studies on teacher AI in English as a Foreign Language (EFL) context remain limited. This study aims identify intraindividual differences and examine its associations with age years of teaching experience among 782 teachers. Given absence reliable instrument measure literacy, we first constructed validated scale encompassing five sub‐scales: Knowledge , Use Assessment Design Ethics . Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, results revealing four distinct profiles: Poor (C1: 12.1%), Moderate (C2: 45.5%), Good (C3: 28.4%), Excellent (C4: 14.1%). Multinomial logistic regression analyses indicated significant between both experience. Additionally, 32 respondents participated semi‐structured interviews. The qualitative data analysed MAXQDA 2022 triangulated quantitative offered deeper insights into teachers’ perceptions their literacy. provides theoretical practical implications for understanding Chinese EFL context.

Language: Английский

Citations

3
AI and Uncertain Motivation: Hidden allies that impact EFL argumentative essays using the Toulmin Model DOI Creative Commons
Abdullah Al Fraidan

Acta Psychologica, Journal Year: 2025, Volume and Issue: 252, P. 104684 - 104684

Published: Jan. 3, 2025

Language: Английский

Citations

2
How AI‐Enhanced Social–Emotional Learning Framework Transforms EFL Students' Engagement and Emotional Well‐Being DOI Open Access

Yue Zong,

Lei Yang

European Journal of Education, Journal Year: 2025, Volume and Issue: 60(1)

Published: Jan. 12, 2025

ABSTRACT This study explores the transformative role of AI‐enhanced social–emotional learning (SEL) frameworks in improving engagement and emotional well‐being English as a foreign language (EFL) students China. A survey was conducted among 816 undergraduate postgraduate from universities across five provinces, utilising convenience sampling. The research focused on how AI tools integrated into contribute to student stability. Data were analysed using SPSS for descriptive regression analyses AMOS structural equation modelling. findings highlight that SEL significantly boosts well‐being. By providing tailored experiences based students' cognitive needs, systems facilitate better regulation, increased focus improved academic performance. results suggest offer personalised support not only enhances outcomes but also creates more emotionally supportive environment, contributing overall success

Language: Английский

Citations

2
The Effect of Artificial Intelligence Tools on EFL Learners' Engagement, Enjoyment, and Motivation DOI
Lingjie Yuan, Xiaojuan Liu

Computers in Human Behavior, Journal Year: 2024, Volume and Issue: 162, P. 108474 - 108474

Published: Oct. 12, 2024

Language: Английский

Citations

11
The Effects of Using AI Tools on Critical Thinking in English Literature Classes Among EFL Learners: An Intervention Study DOI Open Access
Wenxia Liu, Yunsong Wang

European Journal of Education, Journal Year: 2024, Volume and Issue: 59(4)

Published: Oct. 16, 2024

ABSTRACT Artificial intelligence (AI)‐driven learning has become an irreversible trend in foreign language education. Scholars are increasingly focusing on this field, yet few have examined its impact within English literature classes. To fill gap, we designed 8‐week intervention study with mixed methods and recruited 90 students, 42 the experimental group 48 control group, matched for average age, proficiency gender ratio. Critical thinking levels were measured before after using a standardised assessment tool. In students used AI tools (ChatGPT‐3.5, Bodoudou, SummarizBot, etc.) to generate answer text‐related questions, participate interactive quizzes AI‐assisted debates during classes, while followed traditional without tools. The findings revealed statistically significant improvement critical skills of compared as by pre postintervention assessments ( p < 0.05). This suggests that can effectively enhance abilities not only contributes emerging discourse education but also offers practical implications integrating technologies support enrich experiences EFL potential guide educators policymakers designing AI‐driven educational strategies culturally responsive pedagogically effective.

Language: Английский

Citations

11
Fostering Engagement in AI‐Mediate Chinese EFL Classrooms: The Role of Classroom Climate, AI Literacy, and Resilience DOI
Xiaochen Wang, Yang Gao, Qikai Wang

et al.

European Journal of Education, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 13, 2024

ABSTRACT The rise of artificial intelligence (AI) has significantly impacted education, yet few scholars have explored AI‐assisted classrooms, particularly in language education China. Understanding the roles classroom climate, AI literacy, and resilience is essential, as these factors foster positive learning environments enhance student engagement. In this sense, study, grounded Social Cognitive Theory, employs structural equation modelling to investigate influencing engagement Chinese English a Foreign Language (EFL) classrooms. It examines data from 606 university EFL learners explore interactions among variables mediating role resilience. findings indicate that all predict engagement, highlighting importance both environmental cognitive fostering active participation. Furthermore, serves crucial mediator, linking climate literacy This study provides some insights for educators policymakers, emphasising need cultivate supportive environments, promote programs, strengthen students' optimise educational settings.

Language: Английский

Citations

9
Leveraging artificial intelligence (AI) in English as a foreign language (EFL) classes: Challenges and opportunities in the spotlight DOI
Kun Dai,

Quanguo Liu

Computers in Human Behavior, Journal Year: 2024, Volume and Issue: 159, P. 108354 - 108354

Published: June 20, 2024

Language: Английский

Citations

8
What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model DOI Creative Commons

Dayou Chen,

Wentao Liu,

Xinyu Liu

et al.

Acta Psychologica, Journal Year: 2024, Volume and Issue: 249, P. 104442 - 104442

Published: Aug. 6, 2024

Prior research highlights the critical role of AI in enhancing second language (L2) learning. However, factors that practically affect L2 learners to engage with resources are still underexplored. Given widespread availability digital devices among college students, they particularly poised benefit from AI-assisted As such, this study, grounded an extended Technology Acceptance Model (TAM), investigates predictors learners' actual use tools, focusing on self-efficacy, AI-related anxiety, and their overall attitude toward AI. Data was gathered 429 at Chinese universities via online questionnaire, utilizing four established scales. Through structural equation modeling (SEM) AMOS 24, results indicate self-efficacy could negatively positively influence both tools. Besides, anxiety predicted Moreover, a positive predictor through reducing AI, or combination both. This study also discusses theoretical pedagogical implications suggests directions for future research.

Language: Английский

Citations

8