University Students’ Usage of Generative Artificial Intelligence for Sustainability: A Cross-Sectional Survey from China

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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.

Advances in generative artificial intelligence (GenAI) have the potential to transform learning. As research increasingly calls for transforming the human-GenAI relationship from mere tool use to a collaborative partnership, there remains a notable gap in exploring an important factor influencing this relationship: trust in GenAI, being crucial amidst growing concerns about disuse, misuse, and abuse of GenAI in learner-GenAI interaction primarily via language. This study seeks to address the gap by investigating university students’ trust in GenAI, its related factors of reliance and resistance, and boundary conditions in using GenAI for language learning. The research utilized an explanatory sequential mixed-methods design. In Study 1, a proposed conceptual model within the stimulus-organism-response framework was quantitatively tested through structural equation modeling using a survey. Data were collected from 682 university students who used GenAI in language learning. Study 2 further confirmed the results through 40 qualitative interviews and refined the conceptual model by offering a deeper understanding of the relationships. The findings indicate that trust in GenAI is a significant factor in university students’ use of GenAI for language learning, with its influence on behavioral intention to use mediated through reliance and resistance. Moreover, forms of the five factors are identified: trust in GenAI, reliance, resistance, perceived risk, and actual use. Additionally, two boundary conditions are identified: perceived risk between trust in GenAI and reliance/resistance, and self-directed learning between behavioral intention to use and actual use. These findings provide theoretical, research, and practical implications regarding the appropriate use of GenAI in education.

Generative artificial intelligence (GenAI) holds significant potential to enhance university students’ learning. However, over-reliance on it to complete academic tasks poses a risk to academic achievement by potentially encouraging cognitive outsourcing. Despite this growing concern and an expanding body of research on GenAI usage, the mechanisms through which GenAI dependency and perceived teacher caring affect their academic achievement and self-efficacy remain underexplored. Based on the theory of media system dependence, this study explores the mechanisms through which university students’ dependency on GenAI affects their academic outcomes, focusing on the mediating role of self-efficacy and moderating role of perceived teacher caring. A survey was conducted with 418 university students from Chinese public universities who had used GenAI for an extended period. The results revealed that GenAI dependency positively predicts false self-efficacy and negatively predicts academic achievement, exhibiting a significant Dunning–Kruger effect. Perceived teacher caring moderates the relationship between GenAI dependency and self-efficacy. High perceived teacher caring mitigates the Dunning–Kruger effect but has a weak moderating effect on academic achievement. These findings enhance the explanatory power of the media system dependency theory in educational contexts and reveal the pathways through which GenAI dependency and teacher caring affect learning processes and outcomes. This study expands the theoretical implications of teacher caring in the digital age and provides empirical evidence to aid higher education administrators in optimising AI governance and teachers in improving instructional interventions.

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 the potential to significantly impact educational practices. Despite the apparent increase in the adoption among students, empirical research on understanding AI use and acceptance among university students is scarce. To address this gap, this study employed an extended technology acceptance model (TAM) incorporating subjective norms, perceived enjoyment, self-efficacy, and compatibility with 943 undergraduate students from 140 universities across Turkey. Findings revealed that attitude toward use, perceived usefulness, self-efficacy, and compatibility significantly predicted behavioral intention to use GenAI tools. Notably, subjective norms had a limited influence on students’ intentions to use GenAI tools, challenging traditional assumptions about social pressures in technology adoption. These insights highlight that students prioritize personal perceptions over social influences in adopting GenAI technologies. Theoretically, the study provides insights into the proposed extended TAM, by validating it in a unique socio-cultural context. Practically, the study provides recommendations on how to foster acceptance of GenAI by enhancing the usability, compatibility, and enjoyment of GenAI tools while addressing potential perceived risks and ethical concerns. The study contributes to understanding GenAI’s role in education, providing a foundation for future investigations into additional factors influencing the use and acceptance of GenAI tools in higher education settings.

This study investigates the influence of generative artificial intelligence (GAI) on university students’ learning outcomes, employing a technology-mediated learning perspective. We developed and empirically tested an integrated model, grounded in interaction theory and technology-mediated learning theory, to examine the relationships between GAI interaction quality, GAI output quality, and learning outcomes. The model incorporates motivational factors (learning motivation, academic self-efficacy, and creative self-efficacy) as mediators and creative thinking as a moderator. Data from 323 Chinese university students, collected through a two-wave longitudinal survey, revealed that both GAI interaction quality and output quality positively influenced learning motivation and creative self-efficacy. Learning motivation significantly mediated the relationship between GAI output quality and learning outcomes. Furthermore, creative thinking moderated several pathways within the model, with some variations observed across the two time points. These findings provide theoretical and practical insights into the effective integration of GAI tools in higher education, highlighting the importance of both interaction and output quality in optimizing student learning experiences.

Artificial intelligence is defined as the totality of systems and programs that imitate human intelligence and can eventually surpass this intelligence over time. The rapid development of these technologies has raised various ethical debates such as moral responsibility, privacy, bias, respect for human rights, and social impacts. This study examines the technical infrastructure of artificial intelligence, the differences between weak and strong artificial intelligence, ethical issues, and theological dimensions in detail, providing a comprehensive perspective on the role of artificial intelligence in human life and the problems it brings. The historical development of artificial intelligence has been shaped by the contributions of various disciplines such as mathematical logic, cognitive science, philosophy, and engineering. From the ancient Greek philosophers to the present day, thoughts on artificial intelligence have raised deep philosophical questions such as human nature, consciousness, and responsibility. The algorithms developed by Alan Turing have contributed to the modern shaping of artificial intelligence and have put forward the first models to assess whether machines have human-like intelligence, such as the “Turing Test”. The study first analyzes the technical infrastructure of artificial intelligence in detail and discusses the current limits and potential of the technology through the distinction between weak and strong artificial intelligence. Weak artificial intelligence includes systems designed to perform specific tasks and do not exhibit general intelligence outside of those tasks, while strong artificial intelligence refers to systems with human-like general intelligence and flexible thinking capacity. Most of the widely used artificial intelligence applications today fall into the category of weak artificial intelligence. However, the development of strong artificial intelligence brings various ethical and theological consequences for humanity. The ethical issues of artificial intelligence include fundamental topics such as autonomy, responsibility, transparency, fairness, and privacy. The decision-making processes of autonomous systems raise serious ethical questions at the societal level. Especially autonomous weapons and artificial intelligence-managed justice systems raise concerns in terms of human rights and individual freedoms. In this context, the ethical framework of artificial intelligence has deep impacts on the future of humanity and human-machine interaction, not just limited to technological boundaries. From a theological perspective, the ability of artificial intelligence to imitate the human mind and creative processes raises deep theological issues such as the creativity of God, the place of human beings in the universe, and consciousness. The questions of whether artificial intelligence systems can gain consciousness and whether these conscious systems can have a spiritual status have led to new debates in theology and philosophy. The ethical principles of artificial intelligence are shaped around principles such as transparency, accountability, autonomy, human control, and data management. In conclusion, determining the ethical and theological principles that need to be considered in the development and application of artificial intelligence is critical for the future of humanity. A comprehensive examination of the ethical and theological dimensions of artificial intelligence technologies is necessary to understand and manage the social impacts of this technology. This study emphasizes the necessity of an interdisciplinary approach for the development of artificial intelligence in harmony with social values and for the benefit of humanity. The study provides an important theoretical framework for future research by shedding light on the complex ethical and theological issues arising from the development and widespread use of artificial intelligence.

The aim of the present study was to narrow the gap in the literature on the adoption of massive open online courses (MOOCs) and the role of task-technology fit (TTF), which influences student satisfaction, academic performance, and the long-term viability (sustainability) of MOOCs in higher education. While researchers have examined MOOC acceptance in a variety of contexts, the role of TTF as a mediating variable in evaluating education sustainability has not been explored using the technology acceptance model (TAM). As a result, the aim of this study was to create a new paradigm by combining two theories: TTF and TAM. Therefore, this study surveyed 277 university students from public universities using the structural equation modeling (SEM) approach to learn about their perceptions toward MOOCs as a method of achieving higher education sustainability. According to the findings, perceived ease of use had a positive impact on perceived enjoyment, perceived usefulness, and social influence, which in turn had a positive impact on task-technology fit and MOOCs use as a method of sustainability in higher education. Task-technology fit also had a positive impact on MOOC use as a method of sustainability. Finally, the role of task-technology fit and MOOCs in educational sustainability had a positive effect on students satisfaction and academic performance. As a result, the use of MOOCs in learning processes should be encouraged in higher education institutions to ensure their long-term viability (sustainability).

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.

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.

<span lang="EN-US">Continuous advancements in the fields of science and technology have led to the emergence of innovative technologies such as Generative Artificial Intelligence (GenAI). While there have been increased use of GenAI among university students, some scholars relate its use with negative impacts regarding students reading habits while others relate it with positive. It is against this backdrop that the present study was carried out to explore prospects, challenges and future directions of GenAI in the developing effective reading amongst university students. The study employed systematic review of literature. Findings revealed that GenAI presents both prospects and limitations for students’ effective reading. improved accessibility, convenience of reading, personalized reading resources and interactive reading were found to be the potential prospects. Regarding limitations, the study found that GenAI can potentially create students’ overdependency on it. In addition, there are potential biasness and inaccuracies of AI algorithms that can lead to a generation of biased reading contents. The system can also lead to breach of data privacy and it is resources intensive. Most of the limitations are, however, manageable. Thus, it was reasonably concluded that the prospects outweigh the limitations. It was, further, found that future directions of AI in developing reading environments involve integration of AI with virtual reality, diminished human-human interaction, human-AI integration, and lifelong learning. The study calls for universities to institute and operationalize students’ data governance and protection policies, among other recommendations.</span>

The introduction of Generative Artificial Intelligence (GenAI) has transformed the way university students learn. To understand the factors that affect the adoption of GenAI among university students, we proposed a comprehensive research model based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), along with personal factors customized for GenAI. We conducted a cross-sectional survey to collect data from university students in Malaysia and China through an online questionnaire, yielding a total of 500 valid responses. The data were analyzed using the Partial Least Squares method to assess the influence of various factors on GenAI adoption. Our findings reveal notable differences in the factors affecting GenAI adoption between the two countries, with the Malaysian group showing a more diverse range of influencing factors compared to the Chinese group. This study highlights the importance of considering country-specific differences when devising strategies for the adoption of GenAI. By integrating UTAUT2 with personal factors and conducting a cross-country comparative analysis, this study offers significant insights into how factors influencing GenAI adoption vary between countries. These insights can be valuable for university stakeholders.

Objective: This study investigates the integration of generative artificial intelligence (GenAI) into educational activities in communication faculties across the three most populous cities in Türkiye: Istanbul, Ankara, and Izmir. Methods: Using a robust semi-structured in-depth interview technique, a qualitative research method, we conducted online interviews with 15 academics from communication faculties in state and private universities. Results: The findings, evaluated through the lens of the Technology Acceptance Model (TAM) and Diffusion of Innovations Theory (DIT), are organized into several categories: "GenAI in the context of technology acceptance," "First encounter with GenAI," "Practices of use in academic activities," " Academics attitude towards students’ use of GenAI," "Potential benefits and challenges," "Institutional GenAI policies," "GenAI policies in Türkiye’s higher education," and "Future predictions." Conclusions: Academics reported using GenAI in both theoretical and practical courses, utilizing its creative capabilities. However, they also expressed a critical stance on ethical issues, such as inaccuracies, fabricated content, bias, potential loss of creativity, and copyright concerns. This critical perspective underscores their unwavering commitment to the ethical use of GenAI, reassuring about the responsible implementation of GenAI. Participants emphasized the importance of shifting from knowledge-based to skill-based education for the "Generation Prompt," and predicted a significant decline in media-related employment in the future.

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.