@article{Kheder2025_1,
  author = {Kheder, Khalefa},
  title = {Using artificial intelligence in learning vocabulary by EFL undergraduate Syrian students},
  year = {2025},
  journal = {Advances in Computational Intelligence and Robotics},
  pages = {131-158},
  doi = {10.4018/979-8-3693-9511-0.ch005},
  url = {https://doi.org/10.4018/979-8-3693-9511-0.ch005},
  abstract = {Artificial Intelligence has recently attracted the attention of academics and researchers in the field of education as a potential tool to help and improve language acquisition, notably in the improvement of learners' communication skills. The purpose of this study is to investigate students' perspectives on using AI tools in learning English vocabulary. A quantitative research design was employed by administering a questionnaire to obtain the data; 8 items were adopted from Alharbi and Khalil (2023), whereas 12 items were adopted from a study by Jomaa (2024). Then, the data was analyzed using the SPSS software application. The respondents of this study were 171 undergraduate students from English Language and Literature department at Idlib and Al-Shahbaa Aleppo universities. The findings will help attain valuable insights into how Syrian undergraduate students are leveraging AI tools for English language learning, including with regard to lexical acquisition and overall improvement in the language.},
}

@article{Molla2025_2,
  author = {Molla, Nur Laila},
  title = {An exploration of undergraduate students’ perceptions of AI-assisted English learning tools},
  year = {2025},
  journal = {English Review: Journal of English Education},
  volume = {13},
  number = {3},
  pages = {955-964},
  doi = {10.25134/tfjkxv57},
  url = {https://doi.org/10.25134/tfjkxv57},
  abstract = {The growing incorporation of Artificial Intelligence (AI) in education has led to the widespread use of AI-assisted language learning applications. These tools introduce new approaches to improving English proficiency through features such as adaptive feedback and interactive practice. Gaining insights into students’ perceptions is essential for optimizing the role of such technologies in educational settings. This research investigated undergraduate students’ views on AI-assisted English learning, emphasizing their experiences, perceived advantages for language development, and the obstacles they faced. A qualitative design was applied, using semi-structured interviews with 20 English Language Education undergraduates at Universitas Pancasakti Tegal. All participants had engaged with AI tools—including Grammarly, QuillBot, and ELSA Speak—for at least three months. Data were examined through thematic analysis. The results showed that these tools enhanced learners’ vocabulary, grammatical accuracy, and pronunciation. Major benefits included individualized learning paths, responsive feedback, and a non-threatening practice environment that fostered autonomy and engagement. Nevertheless, several challenges emerged, such as the risk of dependency, occasional inaccuracies in AI-generated suggestions (particularly with cultural or idiomatic expressions), and concerns about reduced independent language production. The study also revealed a strong link between students’ digital literacy and their capacity to fully utilize AI tools. In conclusion, AI-assisted platforms serve as effective supplementary resources for English learning, offering meaningful support in personalization and skill building, though they cannot replace human instruction. Their success depends on thoughtful integration and user readiness. To optimize outcomes, it is recommended that educators embed AI literacy into curricula, while developers improve the cultural sensitivity and reliability of outputs. Ultimately, a balanced model that blends AI tools with human guidance is most beneficial for holistic language learning.},
}

@article{Prandner2025_3,
  author = {Prandner, Dimitri},
  title = {What do students use AI tools for? Assessing students’ use of AI tools in three typical study related scenarios},
  year = {2025},
  journal = {11th International Conference on Higher Education Advances (HEAd’25)},
  doi = {10.4995/head25.2025.20179},
  url = {https://doi.org/10.4995/head25.2025.20179},
  abstract = {In recent years, generative artificial intelligence (AI) has emerged as a powerful, but also highly controversial, tool in higher education. While there are in-depth debates about the ethics and regulation of the use of generative AI, discussions about the actual use cases for which students use AI are much more limited. This paper presents an empirical case study based on a survey (realised sample n=110; response rate: ~ 80%) at a medium-sized Austrian university. The analysis shows that about 20% will definitely use AI tools for writing term papers, about 30 to 35% will use AI for preparing exams and poster presentations. Alarmingly, in regression models, knowledge of AI had no statistically significant influence on whether students considered using AI or not - the decision is mostly influenced by one's enthusiasm for AI.},
}

@article{Shao2025_4,
  author = {Shao, Shuai},
  title = {The role of AI tools on EFL students’ motivation, self-efficacy, and anxiety: Through the lens of control-value theory},
  year = {2025},
  journal = {Learning and Motivation},
  volume = {91},
  pages = {102154},
  doi = {10.1016/j.lmot.2025.102154},
  url = {https://doi.org/10.1016/j.lmot.2025.102154},
}

@article{Alkandari2025_5,
  author = {Alkandari, Hanan},
  title = {Students’ learning in the time of Artificial Intelligence (AI): Students’ perceptions of using AI tools to improve their language learning in Kuwait},
  year = {2025},
  journal = {Educational Process International Journal},
  volume = {15},
  number = {1},
  doi = {10.22521/edupij.2025.15.154},
  url = {https://doi.org/10.22521/edupij.2025.15.154},
  abstract = {Background/purpose. There is an ongoing debate on the potentiality of using AI for educational purposes, ranging from some optimistic views that anticipate AI to continue changing the interface of language education on one side, to a more cautious camp questioning the efficacy of the issue. As a significant group of stakeholders in the educational enterprise, students should be considered central to this debate, as any potential changes in the educational plans and policies will have a direct impact on their learning outcomes. Materials/methods. This study examines how students perceive tools of artificial intelligence to guide their language learning experiences in the Kuwaiti context. The quantitative approach was employed in this research, where students from the Public Authority of Applied Education and Training (PAAET) in Kuwait participated in the study by completing a questionnaire concerning what they think of AI as a learning toolkit. Findings indicate that inferences about learners’ technological aptitudes should not readily be accepted by members of academic institutions and that learners’ perceptions provide valuable insights in this matter. Results. The study uncovers some interesting findings on the realms of the importance of learner technological readiness, reliance on AI as an exclusive source of education, the social aspects of the learning process, and some ethical issues concerning trust and security. Conclusion. The study attracts attention to the value of learners’ perceptions in the transition to a digitally geared educational environment. It underscores the need for a prudent approach to integrating AI in teaching and learning experiences. This is why decision-makers need to engage in a careful appraisal of the role AI currently plays and can play in the future.},
}

@article{Alsswey2025_6,
  author = {Alsswey, Ahmed},
  title = {Examining students' perspectives on the use of artificial intelligence tools in higher education: A case study on AI tools of graphic design},
  year = {2025},
  journal = {Acta Psychologica},
  volume = {258},
  pages = {105190},
  doi = {10.1016/j.actpsy.2025.105190},
  url = {https://doi.org/10.1016/j.actpsy.2025.105190},
  abstract = {Understanding and utilizing AI tools ensure that designers, particularly students, remain involved in a rapidly evolving industry. This study aimed to investigate students' perspectives on the use of AI tools in graphic design. A pre-post-test control group design was used, involving 112 students (56 in the intervention group and 56 in the control group) from Al-Zaytoonah University of Jordan who were enrolled in the design software course. The findings revealed significant differences between the intervention and control groups regarding Utilitarian Benefits, Hedonic Benefits, Learnability, and User Experience, with students in the intervention group showing greater improvements in these areas. Based on these results, the study recommends incorporating AI design tools into the educational curriculum as an effective learning method and strategy for students.},
}

@article{Dong2025_7,
  author = {Dong, Yuhan},
  title = {Factors influencing the use of AI tools among undergraduate students in the UK: Differences by year of study and subject area},
  year = {2025},
  journal = {American Journal of Student Research},
  pages = {849-858},
  doi = {10.70251/hyjr2348.35849858},
  url = {https://doi.org/10.70251/hyjr2348.35849858},
  abstract = {As Artificial Intelligence (AI) becomes increasingly prevalent, questions have arisen regarding 
the motivations that drive students to rely on it. This study investigates the factors influencing the 
likelihood of AI use among undergraduate students, and how these factors vary by year of study 
and broad subject area. A Chi-Square Test for Homogeneity was conducted for each factor within 
these groups to determine whether significant differences existed in the proportions of students 
selecting particular motivations or uses. The dataset analyzed was drawn from a survey of 1,041 
undergraduate students in the United Kingdom. The findings indicate that students primarily use 
AI to save time, improve the quality of their work, and obtain instant support, while they are less 
likely to use AI when concerned about accusations of cheating or the risk of false or biased results. 
Significant differences emerged in students’ use and motivations behind the likelihood of AI for data 
analysis, summarization, and improving work quality across years of study. Differences were also 
observed in generating text, summarizing, coding, saving time, and concerns about cheating across 
subject areas. These results underscore the need for educators to provide greater support to younger 
students, as well as equitable education and access to AI resources across disciplines. Moreover, 
educators should refine guidelines for AI use to reflect disciplinary differences, and future research 
should examine how these needs evolve within specific fields.},
}

@article{Unknown2023_8,
  author = {},
  title = {An empirical study of AI-generated text detection tools},
  year = {2023},
  journal = {Advances in Machine Learning & Artificial Intelligence},
  volume = {4},
  number = {2},
  doi = {10.33140/amlai.04.02.03},
  url = {https://doi.org/10.33140/amlai.04.02.03},
  abstract = {Since ChatGPT has emerged as a major AIGC model, providing high-quality responses across a wide range of applications (including software development and maintenance), it has attracted much interest from many individuals. ChatGPT has great promise, but there are serious problems that might arise from its misuse, especially in the realms of education and public safety. Several AIGC detectors are available, and they have all been tested on genuine text. However, more study is needed to see how effective they are for multi-domain ChatGPT material. This study aims to fill this need by creating a multi-domain dataset for testing the state-of-the-art APIs and tools for detecting artificially generated information used by universities and other research institutions. A large dataset consisting of articles, abstracts, stories, news, and product reviews was created for this study. The second step is to use the newly created dataset to put six tools through their paces. Six different artificial intelligence (AI) text identification systems, including "GPTkit," "GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy rates between 55.29 and 97.0%. Although all the tools fared well in the evaluations, originality was particularly effective across the board.},
}

@article{Khanduri2023_9,
  author = {Khanduri, Varunni and Teotia, Dr. Anu},
  title = {Revolutionizing learning: An exploratory study on the impact of technology-enhanced learning using digital learning platforms and AI tools on the study habits of university students through focus group discussions},
  year = {2023},
  journal = {International Journal of Research Publication and Reviews},
  volume = {4},
  number = {6},
  pages = {663-672},
  doi = {10.55248/gengpi.4.623.44407},
  url = {https://doi.org/10.55248/gengpi.4.623.44407},
  abstract = {This study investigates the revolutionizing learning: an exploratory study on the impact of technology-enhanced learning using digital learning platforms and AI tools on the study habits of university students through focus group discussions.Nineteen participants were divided into groups of 4-5 members each, and the discussions were thematically analysed.Eight key themes emerged from the analysis, including changing study habits, advantages, and drawbacks of technology in education, challenges in technology-enhanced learning, benefits and limitations of conventional teaching methods, concerns surrounding technology use, and the impact of AI on education.While the findings largely align with existing literature, certain negative effects of technology use were identified, such as a decline in creativity, reduced cognitive functioning, decreased retention and memorization, and lower productivity.These findings highlight the importance of adopting a balanced approach to integrating technology in education, considering both the benefits and potential drawbacks.},
}

@article{Deci1985_10,
  author = {Deci, Edward L. and Ryan, Richard M.},
  title = {Intrinsic Motivation and Self-Determination in Human Behavior},
  year = {1985},
  journal = {Plenum Press},
  doi = {10.1007/978-1-4899-2271-7},
}

@article{Vallerand1992_11,
  author = {Vallerand, Robert J. and Pelletier, Luc G. and Blais, Marc R. and Briere, Nathalie M. and Senecal, Caroline and Vallieres, Evelyne F.},
  title = {The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education},
  year = {1992},
  journal = {Educational and Psychological Measurement},
  doi = {10.1177/0013164492052004025},
}