Keyword: generative AI

ABSTRACT: Generative artificial intelligence (AI) is rapidly transforming important areas of human activity, yet adults’ acceptance of generative AI applications remains insufficiently understood. This cross-sectional study examined the acceptance of generative AI applications among 460 adults recruited through online convenience sampling. Data were collected using the generative artificial intelligence acceptance scale (GAIAS), a validated instrument based on dimensions derived from the unified theory of acceptance and use of technology (UTAUT). The scale comprised four dimensions: performance expectancy, effort expectancy, facilitating conditions, and social influence. Participants reported moderate to high overall acceptance of generative AI. Effort expectancy, reflecting perceived ease of use (PEOU), received the highest mean score, followed by performance expectancy, reflecting perceived usefulness (PU), whereas social influence received the lowest mean score. This descriptive pattern indicates that perceived usability and usefulness were rated more positively than perceived social influence within the present sample. After Holm-Bonferroni correction across the 25 primary comparisons, statistically significant group differences remained for age and educational level in overall generative AI acceptance, performance expectancy, and effort expectancy. No multiplicity-adjusted differences were identified according to gender, personal computer ownership, or previous computer training. The age- and education-related findings should not be interpreted as evidence of linear relationships, independent effects, or causation. Within this digitally connected convenience sample, the findings indicate that PEOU and PU were more prominent dimensions of generative AI acceptance than social influence. Age- and education-related group differences were also observed. However, the cross-sectional design, online convenience sampling, and marked subgroup imbalances limit causal interpretation and generalizability to the wider adult population. The findings may nevertheless inform the development of accessible generative AI tools and targeted digital-literacy initiatives.

ABSTRACT: This study investigates teachers’ reasoning around implementing generative AI (GenAI) in education. It aims to increase understanding of the importance of collective negotiation of trust that arises when teachers collectively explore generative AI as a pedagogical tool for teaching and learning. Data collected through a longitudinal focus group study comprising four workshops and two SWOT analyses conducted over nine months were analyzed using inductive thematic analysis. Trust was used as a theoretical lens to interpret the results. The results indicate that teachers negotiate trust in generative AI at three levels. At the instrumental level, negotiations concern the usability of the tool in terms of quality and efficiency, the reliability of different tools, and regulatory limitations on their use. At the pedagogical level, negotiations emerge overtrust in human–AI communication and interaction, focusing on linguistic precision and the importance of human presence in learning, alongside discussions about future teacher proficiency. At the systemic level, negotiations about trust occur in relation to beliefs about a future educational context and the teacher’s role within it, where reasoning about the socializing function of education becomes evident. The study emphasizes the importance of continuous professional dialogue and critical reflection to build collective trust in generative AI as an educational tool and ensure a responsible integration into the teaching practice. The research highlights the need for balanced, well-founded approaches to this integration, involving negotiations of trust across multiple levels and perspectives.

ABSTRACT: This review article aims to discuss the role of generative artificial intelligence (AI) as a personal tutor, particularly for learning English. Drawing on secondary sources, the article examines the potential of AI tools like Chat Generative Pre-Trained Transformer (ChatGPT) to function as personal tutors in English language learning. To achieve its aim, the article explores various functions of generative AI as a personal tutor. It then outlines key benefits of AI as a personal tutor in the context of English language teaching before briefly discussing three case studies of AI tools, such as ChatGPT, Gemini, and Khanmigo, which can be used as a personal tutor for language learning. The article then turns the discussion into several concerns associated with the use of generative AI as a personal tutor. Key concerns were related to academic dishonesty; false information; biased assessment; a lack of quality feedback and interaction; accessibility issues; psychological and social concerns; challenges concerning identity, diversity, and culture; teachers’ and students’ lack of preparedness; and AI overuse and overreliance. The article concludes with recommendations for policy, practice, and research. Specifically, it is suggested that teachers and students should experiment with AI tools and make use of AI technology to their advantage.

ABSTRACT: The integration of AI technologies into the writing process has significantly altered traditional notions of authorship, creativity, and intellectual labor. Historically, writing was seen as a human-driven cognitive and creative exercise, but with the rise of generative AI tools such as ChatGPT and Claude, the line between human and AI contributions has become increasingly ambiguous. This paper addresses the limitations of the current sliding scale model, which views AI involvement as ranging from “none” to “complete”. In its place, we propose a new multidimensional framework that more accurately reflects the complexity of human-AI collaboration in writing. The model includes axes for content generation, structural assistance, creative input, and analytical contribution, emphasizing the varying degrees of interaction between human writers and AI tools. This framework highlights how AI can assist in different aspects of writing without fully replacing human agency, while also underscoring the importance of ethical and intellectual accountability. By providing a more comprehensive understanding of the collaborative dynamics between humans and AI, this paper offers a foundation for future research into optimizing these interactions in creative and academic contexts.

ABSTRACT: ver the past decade, the growth in generative artificial intelligence (GenAI) is reshaping and changing how we interact, learn, and work and is likely to bring ongoing change in the future. However, current educational understandings, frameworks, and models concerning digital technologies and digital literacies are remaining relatively static and hierarchical and do not adequately accommodate GenAI’s unique learning capabilities, creative potential, and agency. In this conceptual article, we use critical dialogic inquiry and employ ecological thinking using the notion of symbiosis and posthuman perspectives to explore and speculate about the nature of GenAI and its potential impact on educators and learners. We offer a new way of conceptualizing human relationships with GenAI, which we call “symbi(AI)tic understandings.” Symbi(AI)tic understandings acknowledge the evolving and contextual relationships between partners: from balanced mutualism to one-sided commensalism to potentially harmful parasitism. Thus, we position human–GenAI relationships as part of change futures in which there are complex associations between technology and human endeavor. These understandings aim to foster more nuanced ways of being with and thinking about technology: ways which are vital for educators and learners as they transition into an era of education with AI.

ABSTRACT: Contemporary advances in generative AI technology have sparked considerable interest regarding its application in language education. This article explores the innovative impact that AI-powered linguistic educational tools may have, such as customized learning journeys, dynamic content, and individualized feedback mechanisms, which collectively have the potential to enhance language acquisition. At the same time, it is important to recognize the constraints associated with such technologies. Concern about maintaining precision and genuineness within AI-crafted language texts is an issue in the literature. There is also caution about AI’s current inclination to standardize language expression and to propagate limited cultural narratives, alongside the risks of overreliance on technology which may diminish analytical thought and inventiveness. This article examines the ethical considerations involving generative AI, such as the authenticity of creative work and the ownership of intellectual output. Emphasizing the necessity for clarity and conscientiousness in the application of AI, this conceptual article outlines the opportunities, limitations, and ethical concerns associated with generative AI in language instruction. This article advocates for a well-rounded strategy that
leverages the positive aspects of generative AI within language education, while also addressing possible drawbacks and championing an ethical and equitable approach to language learning in the emerging AI-centric digital landscape. A model for forging thinking in this new research and practice space is offered to synthesize many of the possibilities of generative AI in language education.

ABSTRACT: In the rapidly evolving landscape of English composition education, the integration of AI writing tools like ChatGPT and Claude 2.0 has marked a significant shift in pedagogical practices. A mixed-method study conducted in Fall 2023 across three sections, including one English Composition I and two English Composition II courses, provides insightful revelations. The study, comprising 28 student respondents, delved into the impact of AI tools through surveys, analysis of writing artifacts, and a best practices guide developed by an honors student. Initially, the study observed a notable anxiety and mistrust among students regarding the use of AI in writing. However, this apprehension gradually subsided as students increasingly integrated these tools into their writing processes, indicating a shift from skepticism to practical application. The analysis of writing artifacts, particularly early drafts, revealed distinct patterns of AI tool usage, differentiating between students utilizing the tools effectively and those attempting to shortcut the writing process. The final papers, while not overtly indicating AI usage, demonstrated nuanced integration of AI in iterative and recursive tasks like refining arguments and developing ideas at the paragraph level. This suggests a trend toward a hybrid model of writing instruction, where traditional methods are complemented by strategic use of emergent technologies. The study underscores the importance of revised instructional strategies that blend conventional writing techniques with guidance on effective and ethical AI tool usage. It highlights the potential of AI tools in supporting the writing process while also cautioning against over-reliance. The findings of this study offer valuable insights for educators and institutions aiming to develop a balanced and effective hybrid writing instruction model, catering to the needs of contemporary English composition classrooms while maintaining academic integrity.