In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning

Generative artificial intelligence (GenAI) has quickly become prolific in our daily lives, including the higher education sector. Although an AI-fuelled world is unpredictable, there is an urgent need to understand how university students use GenAI to support their learning and the factors influencing GenAI adoption. In this study, underpinned by self-regulated learning (SRL) theory and the technology acceptance model, we examined how university students’ adoption of GenAI is influenced by their SRL skills. Given the importance of SRL skills on students’ selection of study strategies, we hypothesised that SRL constructs would have a strong association with their adoption of GenAI. To explore this, we conducted an international survey-based study of 435 students from two universities in Australia and Canada to capture students’ use of GenAI within the first year of its wider adoption. Our findings reveal that SRL constructs of self-efficacy and social support predict the perceived ease of use of GenAI. Intrinsic motivation and effort regulation also predicted the perceived usefulness of GenAI, with a stronger association for students using GenAI for university learning than those who used it for non-academic purposes such as work or personal use. We discuss implications of our findings for educators. Implications for practice or policy: University teachers should demonstrate and model GenAI use that fosters SRL skill development, such as self-efficacy, social support, intrinsic motivation and effort regulation. University administrators should prioritise academic development to equip instructors with skills for fostering SRL with GenAI tools. The institution should provide guidance on GenAI tool usage and SRL strategies through social support strategies.

Mobile technology offers great potential for university students’ language learning. Numerous studies have been conducted on utilizing mobile technology in language learning classroom. However, using it in self-initiated and self-directed learning outside class remains to be explored. The present study employed the integrative model of behavior prediction to investigate the relationships between attitude, subjective norm, self-efficacy and behavioral intention, as well as the association between intention, facilitating conditions, self-regulation skills and actual use of mobile technology in self-directed language learning. This study also examined whether self-regulation skills moderated intention and actual use. Survey data from 676 language learners in different disciplines from Chinese universities were collected and analyzed using structural equation modeling approach. The results showed that 37.1 percent of respondents indicated that they never used mobile technology for self-directed language learning. Of the other 425 respondents who did indicate that they used mobile technology for this purpose, the majority of them seemed to be extrinsically motivated. Learning activities regarding vocabulary acquisition and translation were far more reported than those in terms of listening, speaking, reading and writing. In addition, attitude and subjective norm significantly explained students’ intention to use mobile technology, but self-efficacy did not have a direct effect on students’ intention. Moreover, students’ self-regulation skills and intention significantly predicted students’ actual use of mobile technology. Through moderation analysis, the results indicated that the relationship between intention and actual behavior would be stronger with any increase in self-regulation skills. These findings are discussed and implications are formulated.

The emergence and application of generative artificial intelligence (GAI), typified as ChatGPT and others have the potential for significant impact on the mental well-being. However, there is currently a lack of systematic research on GAI on mental well-being particularly among university students in Kenya. The purpose was to conduct an exploratory study on the relationship between generative artificial intelligence and mental well-being (MWB) among university students in Kenya. The study used convenience sampling technique. The data was collected from 458 respondents using a structured, closed-ended, self-administered questionnaire. It was analyzed through partial least squares structural equation modeling (PLS-SEM), which is frequently used for prediction models. The model was further checked for goodness-of-fit using Amos. The findings of this study establishes that generative artificial intelligence has a positive and significant influence on mental well-being (β = 0.129, t = 1.997, p < 0.046) among university students. These revelations contribute to the discourse on technology-enhanced education, showing that embracing GAI can have a positive impact on student mental well-being. The study recommends the university administrators to prioritize investment in generative artificial intelligence technologies with the view of enhancing students’ mental wellbeing as they undergo their university education.

Introduction: The rapid integration of generative artificial intelligence (GenAI) technologies, including ChatGPT, into educational environments has introduced both opportunities and challenges for learners and educators. While GenAI can support advanced learning practices, it also raises concerns about critical engagement and the accuracy of generated content. Previous systematic reviews have explored GenAI’s relationship with critical thinking (CT) and self-regulated learning, but a focused synthesis of recent empirical evidence on GenAI’s impact on university students’ CT skills remains lacking. Method: This scoping review followed the PRISMA-ScR guidelines and applied the Arksey and O’Malley framework alongside the Population – Concept – Context (PCC) model. Studies were identified via the Scopus database, using inclusion criteria limited to the years 2024–2025, English language, and the Social Sciences subject area. Thirty eligible empirical studies were analysed and visualised using VOSviewer to identify thematic clusters and categories in the literature. Results: The reviewed studies were grouped into seventeen thematic clusters by the VOSviewer and then manually synthesized into six categories based on semantic interpretation: cognitive and metacognitive development, pedagogical innovation and learning design, academic writing and language learning, AI literacy and learner perception, evaluation and assessment technologies, global and ethical dimensions of GenAI use. The findings were analysed as (1) direct enhancement of CT, (2) metacognitive and reflective gains, (3) contextual factors shaping CT, (4) risks of cognitive offloading, and (5) instructional strategies mediating AI’s effect. 21 publications showed predominantly positive impact of GenAI on CT (idea generation, conceptual understanding, construction of arguments, literature review, academic writing, etc.) whereas reported found mixed impact. Conclusion: The review concludes that GenAI holds substantial potential to support CT development, particularly when pedagogically integrated to promote active reasoning, metacognitive monitoring, and critical autonomy. However, the evidence base is still emerging and is limited by its short temporal scope, narrow database coverage, and reliance on self-reported data. Future research should focus on long-term effects, discipline-specific instructional models, and robust theoretical frameworks linking AI use to cognitive outcomes.

The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the technology acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate the current situations and challenges of Chinese university students using GenAI in four typical task scenarios. This was performed using a cross-sectional research design. The data were collected via questionnaire, with 486 undergraduates from a Chinese university participating. The data analysis methods include descriptive statistics, inferential statistics, and content analysis. The results show that more than 70% of university students actively use GenAI, but nearly half of them are not very proficient in its use. Doubao and ERNIE Bot are the GenAI tools they prefer most. The primary functions they use are text production and information retrieval. They mainly learn the relevant knowledge and skills through self-media and knowledge-sharing platforms. Among the four typical task scenarios, GenAI is widely used in course learning and research activities, while its application in daily life and job search is relatively limited. The analysis of demographic variables shows that grade and major have a significant impact on university students’ use of GenAI. In addition, university students suggest that universities should offer relevant courses or lectures and provide comprehensive technical support to improve the popularity and operability of GenAI. This study provides suggestions for universities, education administration departments, and technology development departments to improve GenAI services. It will help universities optimize the allocation of educational resources and promote educational equity for sustainability.

The rising application of Generative Artificial Intelligence (GenAI) tools like ChatGPT, Bing Chat, and Bard in language teaching and learning heralds a transformative era. Yet, the experiences and perspectives of university students on integrating these tools into their translation studies remain underexplored. This qualitative study, conducted in a research-intensive, Sino-foreign cooperative university in southern China, explored university students’ perceived benefits and challenges of utilizing GenAI in translation practices, as well as their preferred support mechanisms for addressing encountered issues. Data was collected through open-ended questionnaires and semi-structured interviews and analyzed by using reflexive thematic analysis. Results underscored the advantages of GenAI in enhancing translation efficiency, quality, learning, and practice, fostering a positive outlook and social benefits. Nevertheless, issues such as adequacy, prompt engineering efficacy, practical application, technical limitations, accountability, transparency, and potential AI dependency were noted. Beyond existing self-help strategies, there was an expressed need for additional guidance from educators and institutions. This study enriches our comprehension of how university students perceive and engage with GenAI tools in translation, offering insights for educators and academic institutions to optimize future teaching strategies. It also outlines the study’s limitations and proposes directions for subsequent research.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Generative artificial intelligence (GAI) tools such as ChatGPT, Google Gemini (Bard) and Claude AI have emerged as powerful tools in different aspects of English language learning. These tools provide online learners with personalized, interactive, engaging and productive language learning experiences. Unlike traditional AI, GAI analyses users’ data to create up-to-date and consistent outputs based on previously entered data. The paper explores university students’ perceptions of using GAI tools for learning the English language. The paper examines university students’ perceptions of using GAI tools for learning English. The study was conducted at the International University of Sarajevo in Bosnia and Herzegovina. The research draws on the theoretical models of Borgmann [11], Davis [15], [16], and Shoufan [28]. An adapted survey was distributed to university students enrolled in different programs, and a total number of 226 students participated in the survey (N=226). The research investigated students’ perceptions regarding the awareness of GAI tools, their usefulness, technical usage, and both negative and positive attitudes towards GAI. The independent variables included students’ gender and their field of study. The research results offer insights into current trends in university students’ English language learning and the use of GAI tools, which have become an integral part of university education. Keywords: Generative artificial intelligence (GAI), Higher education, English language learning, University students’ perceptions

Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators—student AI literacy, task planning, and task assessors—that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT—not primarily designed for language learning—was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.

Research on artificial intelligence (AI) in education has mainly focused on the measurement instruments relevant to learning about AI and framing AI literacy as learning about AI. The present study's focus, however, was on developing and validating a scale pertinent to learning with generative artificial intelligence (GenAI) in higher education. Specifically, this study aimed to develop and validate the Generative AI Literacy for Learning Scale (GenAI-LLs). For this purpose, data were collected from three groups of university students at three time points. Exploratory factor analysis was conducted to discover the psychometric structure of the scale. Confirmatory factor analysis was conducted to confirm the explored structure with the data collected from a separate group of participants. Further item and internal consistency analyses were conducted to ensure the validity and reliability of the scale. Test-retest analysis was applied to the data from a different group to ensure the stability of the scale. The findings showed that the GenAI-LLs is a valid and reliable instrument that can be used to measure and evaluate the literacy university students need to learn with GenAI in diverse learning environments, including face-to-face, open, distance and flexible learning. The findings are discussed based on the literature on GenAI, and recommendations for future studies are offered. Implications for practice or policy: Instructors can use GenAI-LLs as a valid and reliable instrument to measure and evaluate university students’ GenAI literacy for learning. Instructors and curriculum developers may use GenAI-LLs as a framework – covering needs analysis, prompt and language skills, autonomous learning and critical thinking – during curriculum development and instructional design for integrating GenAI into learning environments. Higher education stakeholders should develop policies and actionable strategies for integrating GenAI into learning design, based on data from the GenAI-LLs.

Recent advancements in generative artificial intelligence (GenAI) have drawn significant attention from educators and researchers. However, its effects on learners’ programming performance, self-efficacy and learning processes remain inconclusive, while the mechanisms underlying its efficiency-enhancing potential are underexplored. This study addresses these gaps through a quasi-experiment comparing an experimental group using GenAI for self-directed programming learning with a control group relying on alternative tools. Additionally, the experimental group was divided into high- and low-performance subgroups to examine the relationship between learning behaviour patterns and academic outcomes using process mining techniques. The findings reveal that (a) GenAI demonstrates strong potential to enhance learning outcomes and self-efficacy but negatively affects long-term knowledge transfer; (b) excessive reliance on GenAI and cognitive outsourcing impede effective knowledge acquisition; (c) high-performing learners exhibit greater epistemic agency, actively critiquing and engaging with AI-generated content to construct knowledge proactively. This study underscores the risks of over-reliance on GenAI and the detrimental effects of cognitive offloading, highlighting the critical role of cognitive engagement and epistemic agency in fostering hybrid intelligence. It provides empirical and theoretical insights to inform the design of GenAI-supported programming education strategies and interventions. Implications for practice or policy: Instructors can enhance programming self-efficacy by integrating GenAI tools like ChatGPT into self-learning activities, particularly for reinforcing academic performance. Course leaders should emphasise GenAI use in programming courses to support student engagement, though they should also prepare students for problem-solving without external resources. Educational institutions may consider developing guidelines for balanced GenAI usage to maximise learning benefits while addressing potential limitations in problem-solving skills.

Self‐directed learning (SDL) is pivotal for academic success and lifelong learning, in which motivation has been a major challenge, especially in the context of self‐directed out‐of‐class reading. According to self‐determination theory (SDT), the fulfilment of three basic psychological needs (BPNs)—autonomy, competence and relatedness—is key to autonomous motivation. However, it is hard to provide personalised support to BPNs in self‐directed out‐of‐class reading. Generative artificial intelligence (GenAI), with its capacity for generative human‐like outputs, makes the provision of personalised interactive support possible. This study thus examined how GenAI‐powered chatbots can be utilised to serve as reading companions to fulfil BPNs in self‐directed out‐of‐class reading. An online reading platform was developed, where students engaged in self‐directed out‐of‐class reading. Sixty‐nine students from two classes at a Chinese university were randomly assigned to two reading conditions: the experimental group (EG; N = 36) where students had access to a GenAI‐powered chatbot as a reading companion on the platform; and the control group (CG; N = 33) where students did not have access to the GenAI‐powered chatbot during reading. Multiple data sources were collected and analysed, including pre‐ and post‐reading tests, pre‐ and post‐questionnaires on reading motivation and self‐directed out‐of‐class reading, log data on reading amounts and semi‐structured interviews. The findings revealed that EG showed significant improvement in autonomous reading motivation and self‐directed out‐of‐class reading. Reading performance did not differ between the two groups. This study offers valuable evidence on the efficacy of integrating GenAI chatbots into SDL through the lens of SDT. Pedagogically, educators can leverage GenAI technology to cater to students' BPNs, bolstering their SDL capabilities and academic achievement. Practitioner notesWhat is already known about the topic Self‐directed learning (SDL) has been recognised as a crucial competence for academic success and lifelong learning. Motivation has been a major challenge in self‐directed out‐of‐class reading, with the fulfilment of basic psychological needs (BPNs) identified as vital for autonomous motivation according to self‐determination theory (SDT). Generative artificial intelligence (GenAI) has the potential to fulfil students' BPNs by providing personalised interactive support to enhance their motivation and SDL. What this paper adds This study adopted the theoretical lens of SDT to develop a GenAI‐empowered online reading platform with a companion chatbot named Reade, aiming to fulfil students' BPNs in self‐directed out‐of‐class reading. This study examined the efficacy of the designed chatbot‐assisted BPNs (CaBPNs) in supporting self‐directed out‐of‐class reading. This study offers empirical evidence that CaBPNs support can enhance students' reading motivation and self‐directed out‐of‐class reading. Implications for practice and/or policy Educators should capitalise on the potential of GenAI chatbots in fulfilling learners' BPNs through the provision of personalised interactive support to boost autonomous motivation and SDL. Educators should be aware of the role of AI literacy in leveraging the full potential of chatbot support for SDL and take proactive steps to cultivate learners' AI literacy.

Purpose The purpose of this study is to identify the factors that contribute to the continued intention and actual use of GenAI tools among Indian university students. Generative artificial intelligence (GenAI) tools significantly transform and disrupt the education sector, offering substantial opportunities for educators and learners. Previous research has predominantly concentrated on adopting ChatGPT within the education sector; however, the effects of other generative artificial intelligence (AI) tools in education remain insufficiently explored. Furthermore, it examines how AI characteristics such as perceived anthropomorphism, perceived intelligence and perceived technology novelty (TN) influence perceived usefulness and perceived ease of use, which are the predictors of continuance intention. Design/methodology/approach This study develops a theoretical model incorporating AI features as antecedents to the Technology Continuance Theory (TCT). This study uses a cross-sectional survey to collect responses from Indian higher education students. The authors use the PLS-SEM model with SmartPLS 4.0 for analysis. Findings This study tested the applicability of TCT in GenAI usage in education settings. This research empirically proved that perceived intelligence and TN are the antecedents to perceived use and ease of use. From the TCT model, satisfaction and attitude positively influenced continuance intention to use GenAI tools, and continuance intention positively influenced the actual use of GenAI in education. Originality/value This study empirically validated the use of various GenAI tools in the teaching and learning process, contributing to the existing research. This study is one of the earliest to examine the human–AI interaction in an educational setting. This study empirically tested TCT from the perspective of higher education students’ actual use of GenAI tools. The findings of this study offer many practical insights for stakeholders, such as EdTech companies, AI companies, GenAI developers, educators and academic institutions, to implement GenAI tools successfully.

The increasing integration of generative Artificial Intelligence (AI) tools, such as ChatGPT, in education has prompted growing interest in their pedagogical potential and the emergent competencies required for their effective use in language instruction. While generative AI is beginning to influence language teaching and learning practices, emerging research suggests a growing need to address AI-related literacies and ethical considerations within language teacher education programs. Despite the growing number of studies examining generative AI’s use in language learning contexts, there remains a notable gap in systematically reviewing how generative AI is being addressed in teacher preparation and professional development. To address this gap, this study presents a bibliometric-based systematic literature review of research on generative AI in language teacher education, employing text-mining algorithms, data-mining heuristics, and social network analysis. The findings identify five major thematic clusters in the literature: (1) Professional Development and AI Literacy in Teacher Education, (2) Chatbots and Conversational AI in Language Learning, (3) Generative AI for Instructional Design, Assessment, and Lesson Planning, (4) Generative AI as a Tool for Enhancing EFL Writing Skills, and (5) Exploring Pre-Service Teachers’ Perceptions and Readiness. This review contributes to the growing discourse on AI in education by mapping the current research landscape and identifying critical directions for advancing generative AI integration in language teacher education.

Generative artificial intelligence (Gen-AI) tools havebeen widely used in English teaching and learning. However, in the context of EFL teachers in vocational high schools, not all the teachers are aware of the potential of Gen-AI for English instruction. This study aims to address the gap identified in previous research by investigating Indonesian EFL teachers' perceptions and challenges of integrating Gen-AI into their classrooms. The participants of this study were 37 EFL teachers from diverse vocational high schools in Eastern Jakarta, Indonesia. This study employed a sequential explanatory mixed-methods design, combining quantitative and qualitative data. A cross-sectional survey was conducted to identify the perceptions of teachers and the challenges they face when integrating Gen-AI in their teaching practices. A semi-structured interview was conducted with a subset of participants to gather qualitative data on their experiences and challenges in utilizing Gen-AI. The results of the survey reveal positive perceptionsamong EFL teachers regarding the use of Gen-AI in English classes (Mean=4.00, SD=0.53). The findings also reveal that teachers are confident and willing to adopt Gen-AI tools (Mean=3.92, SD=0.49) acknowledging their ease of use and practicality in daily teaching practices (Mean=3.92, SD=0.60) and aligning with industry-specific needs (Mean=3.95, SD=0.52).The study underscores the need for equitable access and professional development to support the effective use of Gen-AI in vocational EFL context, ultimately enabling more innovative and future-ready English instruction.