A GenAI Model for Teaching, Learning, and Assessment in Educational Literacy

Aim/Purpose: This study investigated how undergraduate and graduate students in a teacher education program perceived, used, and ethically reflected upon generative AI tools. The problem is the unclear state of preservice teachers’ understanding and use of generative AI in educational settings, which this study aimed to explain. Background: Given generative AI’s growing presence in education, this study addressed a gap in understanding by examining preservice teachers’ perceptions, experiences, and ethical concerns, particularly in relation to self-regulated learning. Methodology: A convergent parallel mixed-methods design was used to gather quantitative and qualitative data from 73 students enrolled in a teacher education program at the University of Nevada, Las Vegas. Descriptive statistics, correlations, and thematic analysis were used for data analysis. Contribution: This study contributes to the emerging literature by providing insights into preservice teachers’ usage patterns of generative AI, the relationship between AI use and self-regulatory skills, and a detailed examination of ethical concerns, informing effective AI integration into teacher education programs. Findings: Text-based generative AI was the most familiar tool among participants. The primary reasons for the use included editing papers, searching for new ideas, and understanding course material. A significant positive correlation was identified between information search with the help of generative AI and resource management skill. Key themes identified by thematic analysis included perceived benefits for student engagement and creativity, AI’s potential as a writing assistant, and ethical concerns about plagiarism and misuse. Overall, preservice teachers demonstrated surface-level use and a general lack of in-depth knowledge about AI integration. Recommendations for Practitioners: Teacher educators should provide explicit training in generative AI, including practical guidelines and clear ethical frameworks to foster effective and responsible integration into classroom practice. Recommendation for Researchers: Future researchers should develop validated measurement instruments specifically designed for studying generative AI perceptions and usage and explore longitudinal impacts as preservice teachers transition into professional teaching roles. Impact on Society: By guiding preservice teachers toward informed and ethical use of generative AI, this study supports broader societal goals of promoting digital literacy, ethical technology integration, and enhanced learning experiences for future generations. Future Research: Subsequent research should further explore reasons behind the limited adoption of generative AI among preservice teachers, examine the long-term impacts of AI use on pedagogical practices, and investigate structured frameworks for promoting deeper AI integration within the teaching and learning domain.

With the rapid integration of generative AI (GenAI) into higher education, concerns over cognitive offloading, overreliance, and diminished critical thinking underscore an urgent need to prioritize learning-to-learn (L2L) competencies. However, the lack of a clear and detailed conceptualization of L2L, a key 21st-century skill for lifelong learning, hinders interdisciplinary research and limits the development of informed, pedagogically sound AI applications. This paper presents a scoping review of L2L definitions within pedagogical and psychological literature, based on sources retrieved from ERIC, Scopus, and Web of Science. The review follows the PRISMA-ScR framework and identifies 21 relevant publications. We propose a novel three-layered framework organized by conceptual broadness: Dimensions (dispositions like cognitive and metacognitive skills), Processes (actionable activities such as self-regulation), and Tools (concrete strategies like retrieval practice), providing entry points for researchers from varying disciplines. We further highlight existing GenAI research efforts addressing these layers, outline current limitations, and propose directions for future exploration, with a focus on higher education. By unifying L2L theory with GenAI practice, this framework provides an actionable foundation for educational technologies in an AI-driven era. • Three-layer learning-to-learn (L2L) framework (Dimensions, Processes, Tools) to guide AI‑enhanced learning system design. • Synthesizes L2L definitions from 21 studies identified by a PRISMA-ScR review and offers actionable paradigms for teaching L2L, for example, using AI-supported systems. • Maps L2L components to GenAI application use cases in higher education. • Positions L2L as key to reducing GenAI overreliance and fostering learner agency.

AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

Generative AI (GenAI) in education has become a divisive topic. While some teachers view GenAI as a transformative tool, others caution against harms and impacts on teaching and learning. Since the usefulness of a tool depends on the user, how teachers view and use GenAI and their perceptions of its role in teaching and learning may influence its benefits and risks. The study adopts a qualitative approach, interviewing 23 higher education teachers with teaching experience across disciplines. Using the Technological, Pedagogical, and Content Knowledge (TPACK) and sensemaking frameworks to guide and analyse the interviews, a set of opportunities, challenges, and threats associated with integrating GenAI in higher education was identified. Teachers highlighted opportunities, including using GenAI as a study buddy. Concerns were raised about students’ overreliance on GenAI, GenAI undermining the teaching and learning process, and issues like undetected plagiarism. The findings suggest a need for professional development to help teachers understand GenAI and how it can be effectively used in teaching. Some teachers warned against the paradox of using a tool to save time, only to find that it might increase workload and frustration. These insights contribute to developing guidelines and informing policymaking to ensure integration of GenAI in education.

Objectives This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method Using a mixed‐methods approach, we combined quantitative, qualitative, and semi‐experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open‐ended questions; additionally, 38 students participated in a semi‐experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.” Results Key findings underscore the role of personality traits such as openness and self‐efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self‐reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.

The integration of generative AI in software organizations presents promising opportunities for automating and enhancing security activities; however, its practical impact on the security practices of software organizations and associated risks remains underexplored. This study investigates how generative AI can enhance software security practices through a multi-case study involving five software organizations. By conducting semi-structured interviews with developers and managers, we identified four security practices where generative AI can support threat assessment, security testing, operational management, and education & guidance. At the same time, we uncover sociotechnical risks that may impede adoption, including concerns about inadequate data management of AI, inaccurate output, and a lack of trust. Guided by the OWASP SAMM framework and rooted in the Gioia methodological approach, our findings are synthesized into a theoretical model of generative AI adoption impacting security practices. This model highlights both the perceived benefits and the risks organizations encounter. Our work provides insights for software organizations looking to adopt generative AI into security-related workflows and contributes a foundational understanding of how such tools are perceived in practice. The paper emphasizes the importance of balancing innovation with caution as software organizations increasingly incorporate generative AI within secure development.

Aim/Purpose: This paper investigates how academics in Egyptian higher education adopt and engage with generative AI tools, addressing the limited understanding of faculty perceptions and the role of technophobia in influencing adoption. Background: Existing research on generative AI adoption primarily focuses on a single tool (e.g., ChatGPT) and overlooks broader organizational and psychological factors. This study extends the Technology Acceptance Model (TAM) to include technophobia and organizational innovative culture, providing a comprehensive explanation of adoption behaviors in the Egyptian higher education context. Methodology: A mixed-methods design was employed. Quantitative data were collected from 195 academics via a structured survey measuring TAM constructs, technophobia, and organizational culture. Qualitative data were obtained through semi-structured interviews to capture experiences, perceived benefits, and concerns regarding generative AI tools. Contribution: The study refines TAM by demonstrating that technophobia indirectly affects adoption through perceived usefulness and perceived ease of use, while organizational innovative culture does not moderate adoption relationships. It offers both theoretical insights and practical guidance for the responsible use of generative AI in higher education. Findings: Perceived usefulness was the strongest predictor of adoption intention, whereas perceived ease of use was not significant. Technophobia reduced perceived usefulness and ease of use but did not directly affect adoption intention. Organizational innovative culture did not moderate relationships. Interviews highlighted efficiency benefits of generative AI alongside concerns about ethics, originality, and policy gaps. Recommendations for Practitioners: Universities should establish clear policies for the use of generative AI in teaching, assessment, and research, and provide regular training and awareness programs to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI. Recommendation for Researchers: Future studies may expand TAM by including constructs such as trust, perceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society: Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities. Future Research: Further research should involve a wider range of institutions, consider moderators such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.

This project focuses on leveraging generative AI to analyse user reviews from the Play Store for diverse applications. By utilizing advanced large language models (LLMs), the system processes extensive user feedback to identify key trends, sentiments, and actionable insights. The AI analyses reviews to categorize overall user sentiment (positive, negative, or neutral), highlight recurring issues, and identify popular feature requests. The generative AI’s capabilities enable it to provide nuanced suggestions to developers, such as improving app functionality, addressing common complaints, and implementing features aligned with user preferences. Additionally, the system can generate concise summaries of user reactions, offering developers a clear understanding of their app's strengths and areas needing improvement. This data-driven approach enhances app development by prioritizing updates based on real user needs, improving user satisfaction, and fostering higher ratings on the Play Store. The integration of generative AI streamlines the review analysis process, ensuring actionable recommendations for sustained app success. Key Words: Generative AI, Large Language Models (LLMs), sentiment analysis, App improvement, review analysis 1.INTRODUCTION This project utilizes generative AI to analyze Play Store reviews across various applications, providing developers with actionable insights. By leveraging large language models (LLMs), the system processes user feedback to identify sentiment trends, highlight common issues, and suggest improvements. This approach enables developers to understand user reactions effectively and prioritize updates, ultimately enhancing app quality, user satisfaction, and ratings. The integration of AI streamlines review analysis, ensuring data-driven decisions for better app performance.

Enhancing Library Management Software (LMS) through work automation, more efficient cataloging, and customized user experiences are all made possible by generative AI. In order to better understand how generative AI fits into library systems, this paper looks at how it may provide accurate information, optimize operations, and provide tailored suggestions depending on user preferences. AI-driven systems reduce the human labor necessary for categorization and resource management while boosting user engagement through targeted offerings. Notwithstanding these advantages, problems like prejudice in content produced by AI and worries about data privacy still exist.The integration of generative AI into Library Management Software (LMS) offers substantial potential for improving the efficiency and personalization of library services. Generative AI into LMS opens new opportunities to optimize functionality, improve user experience, and expand libraries digital outreach. The report underlines the necessity of addressing ethical issues and guaranteeing data transparency. It also highlights the necessity of scalable AI solutions, especially for smaller or less well-funded libraries. Libraries must overcome these obstacles as generative AI develops further in order to guarantee fair, efficient, and transparent AI inclusion.

Objectives This study aimed to analyze the perceptual changes experienced by pre-service teachers as they derived the final products of lesson plans using generative AI, and to explore educational implications for enhancing their Technological Pedagogical Content Knowledge (TPACK). Methods For this purpose, a preliminary training on the educational use of generative AI was conducted for 20 students in a teacher education course at A Education Graduate School in Seoul. Subsequently, the students explored applicable lesson design strategies for various subjects and reflected on these processes and experiences through mid-term assignments, which were then qualitatively analyzed based on the TPACK framework. Results Firstly, pre-service teachers recognized the educational utility of selecting and using generative AI tools aligned with educational objectives and confirmed their potential as tools for delivering subject content. Secondly, through experiencing the limitations of generative AI, they recognized the importance of carefully integrating technology and cultivating digital and AI literacy among teachers. It was found that their repeated and experimental approaches to lesson design contributed to the enhancement of problem-solving and critical thinking skills. Thirdly, pre-service teachers who were initially hesitant to use generative AI developed a positive perception and showed a willingness to use it more extensively in lesson design. Lastly, they were able to effectively integrate elements of generative AI and TPACK, developing new and innovative customized teaching methods and strategies suitable for each subject, and expanded their educational thinking and perspectives by experiencing lesson design cases from fellow pre-service teachers. Conclusions Teacher training institutions should develop and operate more practical curricula that include excellent examples of technology-integrated classes and practical exercises. Additionally, the curriculum should include content that simulates real issues encountered while using generative AI, and develop customized professional courses that facilitate the systematic integration of generative AI according to each major.

The integration of Generative AI (GenAI) in Education presents immense potential for reshaping learning experiences and empowering students and educators. However, harnessing this potential requires collective action and responsible decision- making to ensure the effective and ethical use of AI technologies. This paper presents a series of recommendations and proposals aimed at effectively integrating GenAI in the higher education sector, catering to the perspectives of government, AI developers, students, educators, universities, schools, and researchers. By exploring diverse viewpoints about ChatGPT and future Google Gemini, this research aims to create a comprehensive recommendation guiding regulatory measures that address challenges, ethical considerations, and best practices of GenAI integration. Through a holistic approach, researchers believe that policymakers can foster a transformative and ethical environment, leveraging the full potential of generative AI while safeguarding students' well-being and academic integrity.

Aim/Purpose: This study aims to explore the Generative AI (GenAI) literacy of English-medium instruction (EMI) undergraduates, with a particular focus on disciplinary variations across engineering, mathematics, and humanities and social sciences. Specifically, it seeks to a) examine differences in GenAI literacy across disciplines, b) identify distinct literacy profiles among disciplinary groups, and c) understand students’ self-perceptions of their GenAI literacy. Background: The advent of GenAI tools has reshaped higher education, offering personalised academic support and aiding non-native English speakers. While traditionally focused on STEM fields, the widespread adoption of GenAI, alongside concerns about accuracy and ethics, emphasises the urgent need for GenAI literacy education across all disciplines. However, gaps remain in the literature regarding how students’ GenAI literacy varies across disciplines and how their underlying literacy profiles are shaped, particularly in EMI contexts, where non-native English-speaking students face additional linguistic challenges. Methodology: Utilising a mixed-methods approach, the research assesses five critical dimensions of GenAI literacy: basic technical proficiency, communication proficiency, creative application, critical evaluation, and ethical competence. The quantitative phase, which included 347 questionnaire participants recruited via convenience sampling, employed the Kruskal-Wallis test to examine disciplinary differences in GenAI competencies. Further, a multigroup latent profile analysis was conducted to identify distinct literacy profiles. To complement the quantitative findings, follow-up semi-structured interviews were carried out with 24 students to collect in-depth qualitative data. These interviewees were drawn from the questionnaire participants using a nested sampling strategy. Reflexive thematic analysis was then applied to uncover key themes related to students’ perceived GenAI literacy. Contribution: This study highlights notable variations in GenAI literacy among students across different disciplines and identifies distinct learner profiles within an EMI university context. The findings underscore the importance of considering both disciplinary and learner-profile factors when developing educational strategies. This work offers a foundation for designing equitable and targeted strategies to develop students’ AI literacy across all disciplines, a pressing need in EMI contexts where learners navigate additional linguistic challenges. Findings: The Kruskal-Wallis test results indicated that, with the exception of ethical competence, engineering students outperformed their peers in mathematics, humanities, and social sciences across four dimensions of GenAI literacy: basic technical proficiency, communication proficiency, creative application, and critical evaluation. Additionally, the multigroup latent profile analysis identified three distinct literacy profiles across disciplines: Foundational Learners, Balanced Practitioners, and Proficient Achievers. Complementary qualitative insights from interviews corroborated these findings and provided nuanced explanations of the underlying patterns. Recommendations for Practitioners: Synthesising these insights, evidence-based pedagogical recommendations are proposed: the integration of AI literacy courses across all disciplines to foster foundational competencies and equitable access, and the implementation of profile-specific educational strategies to enhance personalised learning. Recommendation for Researchers: Refining methods for assessing GenAI literacy is recommended for future studies to enhance their validity and reliability. This includes employing more representative sampling, integrating observation-based or task-based measures alongside self-reported literacy levels, and further refining the theoretical frameworks underpinning AI literacy in response to evolving technologies. Impact on Society: The insights from this study into GenAI literacy and its disciplinary variations will enable universities to develop more responsive and inclusive educational strategies, ultimately fostering a more AI-literate society. This will ensure that graduates across all disciplines are better prepared to effectively and critically engage with AI technologies in their future careers and daily lives. Future Research: Future research should consider stratified sampling across multiple institutions, regions, and cross-national contexts to capture a more representative picture of GenAI engagement and facilitate meaningful comparisons across educational systems. In addition, while the study focused on undergraduates, postgraduate students, and academic staff are also critical stakeholders in the AI literacy agenda. Investigating how these groups engage with GenAI could provide valuable comparative insights. More importantly, the identification of learner profiles also raises new questions about movement between profiles over time and the kinds of interventions that support such transitions. Longitudinal studies and action research involving instructional design experiments could help clarify how GenAI literacy evolves and what pedagogical strategies are most effective for supporting growth.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

Based on the OpenAI ChatGPT, generative AI (Gen-AI) in education has transformed science by using contextual awareness to produce messages that resemble those of a person. Gen-AI, like ChatGPT, which OpenAI frequently employs, has gained popularity, particularly in the educational field. This study looks at many aspects that affect the uptake and real-world application of Gen-AI, such as ChatGPT, to enhance learning outcomes for students. The current study uses the unified theory of acceptance and use of technology (UTAUT2) to bolster our conceptual framework. This study examines data gathered from 482 university students in Pakistan using the partial least squares structural equation modelling (PLS-SEM) approach. According to the results, Gen-AI adoption intention is significantly influenced by performance expectancy, effort expectancy, self-efficacy, hedonic motivator price value, and habit. This study also demonstrates that adoption intention has a substantial impact on Gen-AI actual use. Instructors must encourage the safe use of Gen-AI, such as ChatGPT, in order to preserve students’ critical thinking, abilities, and creativity in their assessments. In order to ensure that Gen-AI tools are used in assessments ethically and appropriately, educators will be essential in providing clear instructions and rules.