AI-Generated Tailor-Made Pedagogical Picture Books: How Close Are We?

The rapid development and availability of Generative AI (GenAI) tools, such as ChatGPT, offer new possibilities for academia, yet they also raise concerns for academic integrity. Chapelle's (2024) seminal work on GenAI’s implications serves as an impetus for this study, which explores teaching academic integrity in the context of AI use. Chapelle frames GenAI as an ‘inflection point’, calling for a re-evaluation of pedagogical strategies to support students in understanding its technological and ethical implications. This article presents findings from a mixed-methods study at a university in Cyprus, where ESL undergraduates in academic writing classes engaged with ChatGPT. Participants with B2/C1 English proficiency from diverse cultural backgrounds were assessed through pre- and post-intervention evaluations and qualitative interviews. A semester-long intervention incorporated activities to enhance academic writing skills and comprehension of ethical AI use, focusing on critical thinking, proper citation, and policy adherence. Findings indicate that students demonstrated increased AI knowledge and awareness of academic integrity. This study contributes to the discourse on integrating GenAI in academia, providing insights into effective instructional practices and addressing ethical considerations in academic writing. It offers guidance for educators and institutions aiming to integrate GenAI responsibly, ensuring academic integrity remains central in second language education.

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

The exponential emergence of generative AI (GenAI) tools, such as ChatGPT, is reshaping language learning in higher education. However, little is known about the psychological and perceptual factors that sustain students’ long-term use of these technologies. This study develops and empirically tests an integrated framework combining Self-Determination Theory (SDT) and the Technology Acceptance Model (TAM) to explain continuance intention and actual usage of GenAI tools in English as a Foreign Language (EFL) learning. Using a descriptive cross-sectional survey design and non-probability convenience sampling, data were collected from 451 Malaysian university students (Generation Z). Partial Least Squares Structural Equation Modelling (PLS-SEM) and PLSpredict were used to examine direct and mediated effects of autonomy, competence, and relatedness on perceived usefulness and ease of use. The findings confirm that both motivational and technological constructs significantly influence continuance intention, which in turn predicts actual behaviour. By including actual usage, the study addresses the intention – action gap in EdTech research. Situated in a Southeast Asian context, the findings offer actionable insights for culturally responsive, learner-centred AI-enhanced EFL programs. This research is relevant to policymakers, instructional designers, and developers seeking to support the adoption of sustainable GenAI in language education.

Starting today and due to the advent of generative AI, traditional enterprise portfolio management (EPM) is seriously disrupted, creating new opportunities for organizations to accelerate strategic alignment, optimize resource allocation, and foster innovation. This paper explores how generative AI will have the transformative power to create actionable insights and improve decision-making in EPM. Therefore, organizations can surpass traditional portfolio management's limitations and adapt to dynamic and real-time adjustments enabled by the use of predictive analytics and scenario modeling through AI-driven tools. AI's role in enhancing portfolio optimization, risk management, and project prioritization is discussed in this article, alongside challenges centered on data quality, workforce, and ethical considerations. It further explains the integration between AI and the existing enterprise system to provide a more encompassing, flexible approach to portfolio management. The paper applies generative AI in the real world through case studies from some leading organizations, which show how generative AI has been used to optimize portfolio performance, manage risks, and allocate resources efficiently. The paper ends with the vision for the future of EPM through generative AI, where generative AI powers more agile and data-driven decision-making processes. With each growing step of AI technology, it follows that businesses need to invest money to benefit from AI tools and train their staff for the transformation of AI-enhanced portfolio management.

Since ChatGPT's release, there has been confusion about how generative AI (GenAI) tools can be responsibly utilized in TESOL research processes. While there are many examples of inappropriate uses of GenAI, there are also growing examples of its potential utility. In addition, GenAI use has become normalized in many TESOL scholars' academic writing and knowledge‐production activities. The rapid adoption of GenAI into researchers' practices has outpaced our understanding of ethical appropriateness and empirical explorations into the tools' efficacies in supporting research tasks. Responding to a lack of standards and guidance in the TESOL field regarding the use of GenAI in our research activities, we propose a disciplinary framework for using GenAI in TESOL research. This framework, built on four elements of quality study proposed by Plonsky (2024): (1) transparency, (2) methodological rigor, (3) ethics, and (4) societal value; with an additional element proposed by us, (5) human accountability, can assist scholars in making more informed decisions about the use of GenAI at different stages of their research process—from conceptualization to dissemination. The article also provides important considerations that scholars must know if they plan to implement GenAI in their research process. The framework is useful for journal editors and reviewers to evaluate the use of GenAI in TESOL research. We end the article with a call for the TESOL community to adapt our framework to meet their needs.

The emergence of generative AI (GenAI), such as ChatGPT, in education reconceptualizes the realm and is novel to researchers and practitioners alike. Over the past few years, systematic reviews of the impact of GenAI on education have increased, focusing on language education and general education. Such reviews may overlook other integrated disciplines. This impact can be reflected in student engagement (learning outcomes) and teacher perspectives. In response, this review aims to investigate the impact of integrating GenAI on student engagement and teacher‐perceived support in science, technology, engineering, art, and mathematics (STEAM) education. It used a thematic analysis approach to examine relevant articles published over the past 5 years (2020–2024). The findings suggest 11 constructs on how GenAI tools affect the development of student cognitive, behavioral, and emotional engagement. They also suggest three themes about how STEAM teachers felt about GenAI tools—attitude, pedagogy, and 21st‐century skills. We used the findings to suggest recommendations for future directions of GenAI research.

This study explored students’ perspectives on the benefits and challenges of integrating Generative AI (GenAI) tools in teaching, learning, and assessment within five colleges under the Royal University of Bhutan (RUB). The study is timely and relevant as educators and students in Bhutan increasingly adopt GenAI tools, reflecting a broader global trend. The study outlines three primary objectives. First, it seeks students’ perspectives regarding the integration of GenAI tools in teaching, learning, and assessment in higher education, highlighting the GenAI tools used, the benefits they offer, and the challenges they may impose. Second, the study aspires to provide practical recommendations for educators and academics in higher education institutions for the effective integration of GenAI tools. Finally, the study aims to provide recommendations to relevant stakeholders and policymakers for the adoption or expansion of AI initiatives within the precincts of RUB colleges. This study employed a qualitative approach as qualitative research allows for a deeper understanding of human behaviour and experiences. A purposive sampling technique was used to ensure that participants were selected based on their potential to provide valuable insights relevant to the research objectives. Data were collected through semi-structured focus group interviews, with each group comprising six members (three male and three female). A total of 180 students participated across 30 focus group interviews conducted in five colleges, with six focus group interviews in each college. The data collected were transcribed, coded, and categorised into themes for analysis. To maintain confidentiality, the researchers used a systematic labelling system for both focus groups and individual students during the data analysis process, enabling a comprehensive and structured interpretation of the findings. The findings revealed key benefits of GenAI, such as providing quick and accessible information, fostering personalised learning, and offering emotional support. However, the findings also revealed challenges in terms of the reliability of AI-generated content, the potential hindrance to creativity and critical thinking, and the risk of social and emotional disconnect between students and their learning communities due to overreliance on GenAI. The study therefore recommends raising awareness and improving understanding of GenAI tools among students and establishing comprehensive policy frameworks to promote their ethical use across RUB colleges while upholding high standards of academic quality.

The study examines the design implications of leveraging generative AI tools such as ChatGPT, Stable Diffusion, Midjourney for literacy development and creative expression for children [6, 8, 18]. We sought to elicit insights on the applicability of generative AI for educational purposes from various stakeholders (i.e., parents, teachers, and AI researchers). We recruited nine participants to elicit their perspectives on designing a visual narrative app with generative AI. We examined the opportunities and limitations of the current generative AI tools. Using the implications from our evaluation, we propose AIStory, an AI-powered visual storytelling application prototype that can be used for children’s creative expression, storytelling, and literacy development.

This study presents a comparative analysis of user engagement with generative AI, using ChatGPT traffic data for March 2024 as a case study of one widely adopted tool. Using World Bank data and statistical methods, including ANOVA, it compares usage across continents. While statistical tests showed no significant differences in total visits or global traffic share, descriptive results revealed meaningful disparities in engagement intensity. Chatbot-based platforms, particularly ChatGPT, have dominated other generative AI tools due to their versatility in tasks such as content creation, learning, coding, and communication. These findings suggest a trend toward broader access but also highlight persistent structural barriers, such as device access, digital skills, and infrastructure, that shape real-world usage. Therefore, promoting digital literacy, improving infrastructure, and localizing AI tools remain essential to achieving more equitable global engagement with generative AI.

Educators are at the forefront of shaping the future of education, particularly as technological advances introduce new tools and methods. Generative AI is a powerful tool capable of producing high-quality content based on properly constructed user prompts. While these advancements present significant opportunities, they also pose challenges. This study investigates educators' general attitudes and confidence in using and guiding student use of generative AI and evaluates the effectiveness of a targeted workshop designed to increase AI literacy among educators participating in a university outreach program. Participants included 12 undergraduate resident scientists, six graduate fellows, and eight professional teachers. The study utilised a pre- and post-survey approach to evaluate shifts in AI literacy and comfort before and after the workshop. Specifically, participants reported increased confidence in using AI tools, recognizing bias in AI-generated content, and understanding the ethical implications of AI use. The study highlights the importance of targeted, multigenerational training programs to improve educators' comfort and literacy with generative AI, emphasizing the value of cross-generational collaboration in enhancing learning outcomes. The findings suggest that such training fosters a deeper understanding of generative AI, thereby enhancing its integration into pedagogical practices.

Two studies explored the relationship between elementary students’ creative self-efficacy (CSE) beliefs (i.e., self-judgments of creative ability) and teachers’ ratings of students’ creativity. In Study 1, elementary students’ (N 595) CSE beliefs in science predicted teachers’ ratings of students’ creative expression in science, accounting for a significant, but small (3.4%), proportion of variation in teachers’ ratings. Results of Study 1 also indicate that students’ CSE beliefs tended to decline by grade level and teachers tended to rate females and White students as more creative. In Study 2, elementary students’ (N 306) CSE beliefs in science and math predicted teachers’ ratings of creative expression in math and science, again accounting for a significant, but small (2.1% in science; 4.2% in math), proportion of variation in teachers’ ratings. Also similar to Study 1, results indicate students’ CSE beliefs declined by grade level. Results of Study 2 indicate that students tended to underestimate their creative ability and tended to differentiate between creative ability in science and math (whereas their teachers did not). Implications for creativity research are discussed.

This cross-sectional study explores relationships among teachers’ ratings of students’ social behaviours and perceived competencies and gender-role orientations. Social behaviours, self-competencies, and gender-role orientations of 295 Canadian students (118 boys, 177 girls) were assessed by teachers’ ratings and students’ self-report questionnaires. Results showed significant and consistent associations between teachers’ perceptions of students’ social behaviours and adolescents’ self-perceptions of behavioural conduct. Positive correlations were found between teachers’ ratings of prosocial behaviours and gender-feminine orientation, and between teachers’ ratings of aggression and gender-masculine orientation. Gender differences were found as teachers scored girls higher in prosocial behaviours and boys higher in relational and physical aggression. Boys perceived themselves higher in self-worth, physical appearance, and masculinity, where girls rated themselves higher in femininity and behavioural conduct. These findings highlight the reciprocal nature of students’ and teachers’ beliefs and attitudes and suggests the need for a compassionate, inclusive, and gender-sensitive school climate.

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.

Generative AI, since 2022 when ChatGPT was released, has been widely used in education. While bringing tremendous benefits to teachers and students, it also poses numerous challenges. Exploring the public's opinions and sentiment towards Generative AI in education will greatly promote their integration. Weibo, as the most important social media in China, offers a wealth of data to analyze public attitudes and viewpoints. In this study, 17,799 weibo posts was crawled for topic modelling and sentiment analysis by using BERTopic algorithm and the Large Language Model DeepSeek respectively. It's suggested that the public mainly focus on seven topics related with Generative AI in education, including AI-driven teaching innovation and digital transformation, applications of AI in academic research and the resulting changes in academic publishing, applications and experiences of AI in language education, risks, challenges of using GenAI in education and responses, feedback and complaints after using AI, the development of artificial intelligence major and related fields as well as the impact of AI on the job market and responses. Nearly half users embrace this new technology for its convenience, while some people demonstrate different attitudes. They worry such risks as the job loss, plagiarism, over-reliance and so on. At the end this study, several suggestions are put forward to tackle those negative sentiments to promote the the deep integration of Generative AI in education. Meanwhile, the limitation of this study was also proposed.

Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This paper introduces prompt engineering as a critical competency for students to develop both technical proficiency and critical thinking. Methodology The paper provides a structured framework for teaching prompt engineering in university courses. It draws on existing literature, practical applications, and pedagogical strategies to guide educators in integrating generative AI effectively into their university courses. Contribution This paper contributes to the body of knowledge by presenting a comprehensive framework for teaching prompt engineering. It highlights prompt engineering’s role in enhancing AI literacy and preparing students for technology-driven academic and professional environments. Findings Prompt engineering enhances students’ ability to generate precise and relevant outputs from AI tools by supporting student development of communication strategies tailored to large language models. This guide introduces essential concepts and skills that facilitate effective interaction with AI chatbots. Structured instruction in prompt engineering helps to foster critical thinking, problem-solving, and reflective interaction – key competencies for navigating an AI-driven environment. Additionally, integrating prompt engineering into education improves AI literacy, enabling students to tackle complex tasks and apply AI tools effectively across various disciplines. Recommendations for Practitioners Educators should integrate structured, prompt engineering instruction into their courses, emphasizing its interdisciplinary applications. Scaffolded learning will help students develop competency in applying prompt engineering techniques and strategies. Recommendations for Researchers Future studies should explore the long-term impact of prompt engineering instruction on academic performance and professional readiness. Additionally, research should examine its effectiveness across diverse disciplines. Impact on Society Teaching prompt engineering equips students with essential AI literacy skills, fostering responsible and innovative use of AI in academic, professional, and societal contexts. This contributes to a workforce better prepared for the challenges of the AI era. Future Research Further research should examine the integration of multimodal AI tools alongside prompt engineering to assess how combined approaches can enhance learning outcomes. In addition, studies should investigate the effective-ness of various instructional designs to identify best practices for promoting student engagement and skill development. Exploring discipline-specific and pedagogically meaningful student use cases will also be essential to guiding the thoughtful integration of AI tools across diverse educational contexts.