"Is GenAI my supervisor?" Experiences of nursing undergraduates in using generative AI to assist in case study theses: A qualitative study.

Objective The current literature on Generative Artificial Intelligence (GenAI) in tertiary settings primarily focuses on the risk it poses to academic integrity, and ways to reduce or remove GenAI use in assessments. As psychology graduates enter a workforce where GenAI is present, educators need to prepare students to use GenAI responsibly. This scoping review aims to assess the current state of knowledge in tertiary psychology regarding opportunities for integrating GenAI into assessment methods. Method A comprehensive literature search identified four studies for inclusion. These were published in Australia, Canada, Switzerland, and the United States, and included two quantitative case studies, a mixed-method case study, and a pedagogical case study. Results Three themes were generated: 1) GenAI can be used as an effective psychology tutor, 2) GenAI can be used for authentic assessment in undergraduate psychology, and 3) Critiquing GenAI as a form of assessment can enhance student learning and AI literacy. Conclusions Only four studies were identified, but all indicate that GenAI can be meaningfully incorporated into psychology assessments. However, this is an underdeveloped area and ongoing research with a particular focus on developing evidence-based assessment methods which adapt to the evolving GenAI landscape is needed.

Generative Artificial Intelligence (GenAI) has been a viable technology for decades, yet widespread adoption in healthcare and academic settings has remained limited to research. One possible explanation for this is limited understanding about the beliefs around GenAI use amongst faculty and students training in biomedical disciplines that frequently lead to non-physician healthcare careers, including physical therapy (PT), occupational therapy (OT), allied health (AH), and biomedical engineering (BME). Furthermore, no known studies exist assessing differences that may exist across those disciplines. Given the significant number of professionals in those disciplines and the outsized impact they have on the healthcare system, investigating their beliefs around GenAI use is vital before widespread adoption. Accordingly, we investigated the perceptions of GenAI among students and faculty in the aforementioned fields that frequently lead to careers in healthcare. We found that knowledge of GenAI significantly influences comfort with its use completing college coursework including whether respondents believed it contributed to the process of completing that coursework and whether use of GenAI enhances learning. Interestingly, however, there were no statistically significant differences in perceptions of GenAI across disciplines, roles, or institution sizes. Qualitative findings revealed concerns about plagiarism, decline of critical thinking skills, and ethical challenges, while also recognizing GenAI’s potential to enhance learning efficiency and idea generation. Critically, the study results emphasize the need for proper training and guidelines to ensure GenAI is integrated responsibly into healthcare-related education.

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to whole-hearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communi-cators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation.

"This study explored the integration of Generative Artificial Intelligence (GenAI) in secondary education through a case study of ABC International School in Sri Lanka’s Gampaha District, Sri Lanka. The research to explored how GenAI is currently used by teachers, the challenges faced and strategies that can be used for effective integration. Theoretical frameworks like Bruner’s constructivist principles emphasize learner readiness, structured teaching, and extrapolation, aligning with GenAI’s capacity to scaffold learning. A qualitative case study approach was employed, utilizing semi-structured interviews. A thematic analysis approach with open coding was employed for interpreting findings. Key findings of this study aligned with Bruner’s constructivist principles whereas institutional policies, teacher perceptions and content limitations emerged as mediating factors. The research advocates for comprehensive teacher training programs, national policies regulating ethical GenAI use, and hybrid assessment models that accommodate digital literacy. These recommendations aim to guide policymakers and educators in creating equitable, innovative learning environments prepared for an AI-driven future. Keywords: Case study, constructivism, GenAI, secondary education, teacher perspectives"

Generative AI tools and empowerment in L2 academic writing

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Despite increasing interest in using Generative Artificial Intelligence (GenAI) in education, little is known about how students with disability engage with GenAI to support their own learning. This study investigates the potential of ChatGPT to support the learning agency of adolescents with disability in a secondary science classroom in Australia. Guided by sociocultural and socio‐material conceptualisations of agency, the study explored the mediated choices and capabilities of three students with disability to use ChatGPT to facilitate their learning. The study was conducted in a class comprising students of varying ages clinically diagnosed with diverse learning needs. Data sources included student interviews, the students' conversations with ChatGPT, teachers' lesson worksheets and video recordings of the lesson. Thematic analyses reveal that while students expressed clear and meaningful choices to use ChatGPT to support their learning, they faced metacognitive challenges and cognitive constraints, resulting in a misalignment between their choices and actual capability. The findings identify key theoretical perspectives and practical considerations for supporting students with disability in using GenAI to develop their learning agency. The study recommends customising GenAI for specific learning needs in line with its function as a cognitive prosthesis for students with disability and for better alignment with Universal Design for Learning, thereby supporting students' learning agency. Practitioner notes What is already known about this topic The development of students' learning agency has been widely explored in the secondary context, but not for students with disability. There is increasing interest in using Generative AI (GenAI) to support inclusive education. Current theoretical frameworks can inform the exploration of the learning agency of students with disability using GenAI. What this paper adds Foregrounds the learning choices of students with disability as indicators of their emergent learning agency. Identifies learning challenges facing students with disability that misalign their learning choices and capabilities. Examines GenAI's potential as a socio‐material mediator facilitating the material and relational agency of students with disability. Implications for practice and/or policy Consulting students with disability on how GenAI can support their learning, providing opportunities for them to express their learning preferences. Customising GenAI tools in line with the Universal Design for Learning guidelines to address the specific learning challenges of students with disability. Clarifying the roles of teachers and education assistants in facilitating the learning agency of students with disability in light of customising GenAI to mediate their learning agency.

The rapid development of Generative Artificial Intelligence (GenAI) in higher education has shifted the focus from mere technological access toward issues of trust and governance; however, empirical evidence regarding patterns of GenAI utilization remains limited. This study aims to analyze patterns of GenAI use, levels of trust in the technology, and to examine whether Human Design (HD) labels differentiate perceptions and practices of GenAI use in academic settings. The study employed a quantitative design using a cross-sectional survey approach. Data were collected through an online questionnaire administered to 213 respondents, consisting of students and lecturers from the Department of Library and Information Science. Data analysis was conducted using descriptive and inferential statistics, specifically one-way analysis of variance (ANOVA), across five main constructs: academic performance, prior experience, personal perceptions, technical knowledge, and trust in technology. The results indicate that GenAI has been routinely used at an early to intermediate stage of integration, primarily for ideation and academic drafting activities. Respondents demonstrated a “competent yet critical” attitude, with relatively low levels of trust in terms of accuracy and privacy. Furthermore, the HD labels did not reveal significant differences across all analyzed constructs. This study concludes that trust in technology is a key factor shaping the quality of academic engagement with GenAI; therefore, strengthening verification-centered literacies and inclusive privacy policies is essential to promote responsible GenAI adoption in higher education.

<title>Abstract</title> <bold>Background</bold> Generative Artificial Intelligence has the potential to enhance research efficiency and reduce clinical workload for nursing postgraduates, gradually transforming the development of the healthcare and nursing sectors. Understanding nursing postgraduates' experiences and perceptions of Generative Artificial Intelligence tools is essential for promoting their proper application. <bold>Aim</bold> To comprehensively explore Chinese nursing postgraduates' perceptions, attitudes, and needs regarding GenAI using qualitative interviews. <bold>Design</bold> A qualitative study design. <bold>Methods</bold> Semi-structured interviews was conducted among 16 nursing postgraduates. Purposeful sampling selected master's degree nursing students with experience in the use of artificial intelligence. Thematic analysis was performed to identify recurring patterns and codes. <bold>Results</bold> Five major themes emerged from the analysis: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to Generative Artificial Intelligence adoption. The findings revealed nursing postgraduates’ generally positive perceptions and usage behaviors toward Generative Artificial Intelligence among participants, alongside their barriers and concerns in its application. <bold>Conclusions</bold> Generative Artificial Intelligence is increasingly integrated into research and practice in healthcare and nursing. Nursing students should approach Generative Artificial Intelligence tools rationally and apply them appropriately. This study demonstrates that nursing postgraduates hold a relatively positive attitude and cognitive stance toward Generative Artificial Intelligence, which differs significantly from that of undergraduate students. In light of the current lack of Generative Artificial Intelligence-related education, the study also proposes educational strategies tailored to the Chinese context.

Generative AI (GenAI) is an enhanced fragment of Artificial Intelligence (AI). The emergence of GenAI has engendered numerous impacts on many sectors, including the English language learning and education industry. Motivation, one of the key aspects in language learning, has inspired many scholars as a subject of discussion within language education and second language learning and acquisition. This research sought to determine the correlation between GenAI and EFL Indonesian learners’ perceptions of using GenAI to motivate them in writing. An open-ended and close-ended questionnaire was used to collect quantitative and qualitative data from 30 Indonesian EFL undergraduate students through purposive sampling. The other group of 32 Indonesian EFL undergraduate students participated in the pilot study to test the credibility and validity of research instruments. The result suggests a moderate positive correlation between EFL undergraduate motivation in writing and GenAI’s usage. Qualitative data analysis also demonstrated students’ positive perceptions of using GenAI technology in writing. They found it resourceful for boosting motivation, delivering immediate and personalized feedback, and encouraging self-directed learning. These findings indicate that integrating GenAI into language learning classes, particularly writing, can moderately enhance students' motivation and engagement, thus improving their overall writing skills. The primary limitation stemmed from the small scale and targeted group of participants. Future scholars and researchers are encouraged to explore the influence of a specific GenAI-driven app (e.g., Google Gemini), on another language's core skills, such as speaking or listening, throughout different educational environments and student demographics.

Technology developers in corporate environments hold responsibility as technologies transition from laboratories to market adoption, particularly with Generative Artificial Intelligence (GAI). This transformative technology presents substantial opportunities alongside profound ethical challenges that require immediate attention. As companies seek practical guidelines before regulatory frameworks are established, ethically informed self-regulation becomes essential for aligning business interests with ethical standards. In response, we have developed an actionable framework to promote responsible innovation in GAI. This framework emphasizes defining objectives, maintaining documentation, conducting bias and fairness assessments, implementing security measures, and fostering active engagement. By adhering to these principles, the framework aims to facilitate the ethical integration of GAI into science and technology operations, in order to mitigate potential harms while harnessing its transformative potential. Our bottom-up, self-service approach empowers use case owners to make ethically informed decisions, enhancing ethical capacity across the workforce and increasing awareness of critical considerations in data and GAI. Ultimately, frameworks like the GAI Ethics Framework are important tools for navigating the complex moral landscape of GAI, fostering responsible innovation, and building trust among stakeholders, thereby complementing existing regulations such as the EU AI Act.

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 rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the legitimacy of diverse Englishes in global scholarly communication. This article responds to these questions through a structured scholarly dialogue involving five sociolinguists from World Englishes and adjacent fields. Organised around five guiding questions, the dialogue interrogates how GenAI tools influence writing practices, reinforce or disrupt dominant language norms, and raise ethical challenges. Contributors reflect on the potential of GenAI to democratise writing processes while also raising concerns about GenAI’s tendency to marginalise minoritised varieties and flatten nuance in scholarly writing. Across the dialogue, themes of linguistic (in)justice, researcher agency, and institutional responsibility emerge, with contributors calling for equity-informed policies, critical AI literacy, and inclusive co-design in GenAI development. The article shows the value of dialogic reflection in understanding GenAI’s role in AWP. It concludes that while GenAI may reinforce existing hierarchies, it can also serve as a site of resistance, depending on how it is designed, governed and used within scholarly communities committed to linguistic diversity.

The advancements in Generative Artificial Intelligence (GenAI) can provide opportunities for enriching educational experiences, but at the same time raise concerns regarding academic integrity. Many educators have expressed anxiety and hesitation when it comes to integrating GenAI in their teaching practices. Thus, recommendations and guidance from institutions are needed to support instructors in this new and emerging GenAI era. In response to this need, this study explores different U.S. universities' academic policies and guidelines regarding the use of GenAI tools (e.g., ChatGPT) for teaching and learning, and from there, gains understanding of how these universities respond and adapt to the development of GenAI in their academic contexts. Data sources include academic policies, statements, guidelines, and relevant resources provided by the top 100 universities in the U.S. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Primary concerns lie in ethical usage, accuracy, and data privacy. Most universities actively respond and provide diverse types of resources, such as syllabus templates, workshops, shared articles, and one-on-one consultations; focusing on a range of topics, namely general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings provide four practical pedagogical implications for educators when considering GenAI in teaching practices: 1) accepting GenAI presence, 2) aligning GenAI use with learning objectives, 3) evolving curriculum to prevent misuse of GenAI, and 4) adopting multifaceted evaluation strategies. For recommendations toward policy making, the article suggests two possible directions for the use of GenAI tools: 1) establishing discipline-specific policies and guidelines, and 2) managing students' sensitive information in a transparent and careful manner.

User Experience (UX) practitioners, like UX designers and researchers, have begun to adopt Generative Artificial Intelligence (GenAI) tools into their work practices. However, we lack an understanding of how UX practitioners, UX teams, and companies actually utilize GenAI and what challenges they face. We conducted interviews with 24 UX practitioners from multiple companies and countries, with varying roles and seniority. Our findings include: 1) There is a significant lack of GenAI company policies, with companies informally advising caution or leaving the responsibility to individual employees; 2) UX teams lack team-wide GenAI practices. UX practitioners typically use GenAI individually, favoring writing-based tasks, but note limitations for design-focused activities, like wireframing and prototyping; 3) UX practitioners call for better training on GenAI to enhance their abilities to generate effective prompts and evaluate output quality. Based on our findings, we provide recommendations for GenAI integration in the UX sector.