Generative AI and Education

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).

An ASEE 2023 paper considered whether student use of Generative Artificial Intelligence (GenAI) to write engineering papers constitutes cheating. It was concluded that it depended on specific circumstances, but it was noted that students might potentially undermine their own learning by relying on tools like ChatGPT to answer questions and compose papers. This paper addresses how to enable student use of these tools in a way that students are not cheating themselves. Educators faced a related issue 20 years ago when calculators were introduced into the classroom. As time passed, instructors realized that allowing students to use calculators allowed them to concentrate on teaching concepts, strategies for problem solving and critical thinking. Calculators allowed more individualized instruction and more use of real-world applications. The introduction of calculators into the classroom stimulated discussion on ethical use of technology in teaching. A similar revolution is occurring with the introduction of Generative Artificial Intelligence tools such as ChatGPT, BARD and many others, and a similar set of opportunities is emerging. A key issue is how to use GenAI tools constructively to encourage critical thinking in the solving of engineering problems. The tools can make it easier to differentiate instruction to meet the diverse needs of students and tailor teaching to students with different levels of skills. Using Generative AI tools can help build students' confidence in their own abilities to function as engineers. For example, students can analyze a problem from a variety of perspectives by varying the prompts they give to their GenAI tool. In this paper, we discuss some actions taken in our classes, including changing the style of assessments, teaching students "prompt engineering" as part of the critical thinking process, teaching students how to use Generative AI effectively in coding, and helping students to acquire the habit of validating outputs from Gen AI tools to eliminate hallucinations and fake references. Students can work at their own pace and explore concepts on their own, as Gen AI tools provide instant feedback. Students can explore a problem from multiple perspectives, including the impact of various ethical considerations by varying their prompts. Using Gen AI to generate simple answers to simple questions could, indeed, cheat students out of the ability to learn important engineering concepts. Using Gen AI to explore alternatives and assess different approaches to an engineering problem, for example asking for code using different Python libraries, can make students better and more effective engineers.

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.

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

The rapid development of generative artificial intelligence (GenAI) has brought opportunities and new challenges to higher education. Students need a high level of self‐regulated learning to adapt to this change. However, it is difficult for students to persist in self‐regulation without guidance. Metacognitive support has a significant advantage in enhancing self‐regulated learning, but fewer studies have explored the effects of its role in GenAI environments. The purpose of this study was to investigate the impacts of metacognitive support on college students' self‐regulated learning and learning experiences in a GenAI environment. A quasi‐experiment was designed in which 68 college students were divided into two groups. The experimental group ( N = 35) received explicit metacognitive support, while the control group ( N = 33) did not receive any metacognitive prompts. The experiment lasted 4 weeks. The study measured students' academic performance, self‐regulated learning ability and learning experiences (including cognitive load and technology acceptance). The results indicate that in the GenAI environment, metacognitive support, while not producing significant between‐group differences in achievement, enhances students' self‐regulated learning abilities particularly in terms of task strategy and self‐evaluation, as well as optimizing their learning experience. The study also found that students were at risk of decreasing their level of self‐regulated learning if they lacked metacognitive support in the GenAI environment. The conclusion points out that GenAI supports learners to accomplish learning tasks while potentially reducing self‐regulated learning effectiveness, and that metacognitive support is key to supporting effective regulation in learners' GenAI environments. This study provides an important theoretical and practical basis for how to better support learners' learning in GenAI environments. Practitioner notes What is already known about this topic SRL is vital for effective learning in digital environments. Generative AI tools, like ChatGPT, can enhance learning but require support. Learners often struggle to apply SRL strategies without guidance. What this paper adds Metacognitive support improves SRL in Generative AI environments. It reduces cognitive load and increases the perceived usefulness of AI tools. Structured support leads to better academic outcomes. Implications for practice and/or policy Teachers should integrate metacognitive support when using AI tools. Teacher training should focus on SRL strategies in tech‐rich settings. Policies should promote ethical and effective AI use in education.

The incorporation of generative artificial intelligence in governance, political knowledge, electoral, and democratic processes is essential as the world transitions to a digital paradigm. Numerous nations have adopted Generative AI (GenAI), a disruptive technology that compels electoral bodies to advocate for the integration of such tools into governance, electoral, and democratic processes. Nevertheless, these technologies do not ensure effortless integration or efficient usage owing to intricate socio-cultural and human dynamics. Certain African jurisdictions are ill-prepared for the adoption of these technologies due to factors including underdevelopment, insufficient electrical supply, lack of technology literacy, reluctance to change, and the goals of governing parties. This study examines generative artificial intelligence as a disruptive technology for enhancing governance, political knowledge, electoral processes, and democracy. A mixed-method approach was employed, incorporating surveys and in-person interviews. The analysis of data, debates, and interpretation of findings were grounded in postdigital theory and theme analysis employing an abductive reasoning technique, in alignment with the tenets of critical realism. The study demonstrated that GENAI can influence political knowledge, election processes, and enhance efficiency in government and democracy. Moreover, GENAI, including ChatGPT, can either exacerbate or mitigate societal tendencies that contribute to human division, facilitate the dissemination of misinformation, perpetuate echo chambers, and undermine social and political trust, as well as polarise disparate groups or sets of viewpoints or beliefs. AI offers substantial opportunities but also poses many obstacles, including technical constraints, ethical dilemmas, and social ramifications. The swift progression of AI may disrupt labour markets by automating tasks conventionally executed by people, resulting in job displacement. Implementing AI necessitates significant upskilling and proficiency with digital tools; therefore, governments and organisations must adequately train their personnel to reconcile the disparity between AI's capabilities and users' comprehension. Additionally, there is a requisite for governmental oversight, regulation, and monitoring of AI adoption and utilisation across all facets of its implementation.

Recent emergence of generative artificial intelligence (GenAI) technology has stimulated interests as well as concerns in their potential in teaching and learning. Situated in the new and transforming context, this study provides an avenue for students to introspectively explore their use of GenAI in a postgraduate course. Seventy-four students from three Chinese universities participated in this study. By analyzing student interviews conducted pre- and post-course, alongside their chat logs with GenAI and reflective journal entries detailing their learning approaches, the research uncovers a spectrum of student perspectives on GenAI’s impact, ranging from beneficial optimism, to cautious skepticism and adaptable pragmatism. Notably, student agency is identified as a crucial element in relation to these themes. This was articulated in four types of learning activities: receptive, resistive, resourceful, and reflective. The research underscores the importance of supporting and empowering student agency in the learning approaches aided by GenAI in education, highlighting its role in optimizing its use and enhancing autonomous, lifelong learning skills amidst the evolving technologically advanced learning landscape.

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.

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

Generative artificial intelligence (GenAI) is rapidly reframing academic practice, yet staff confidence to harness its potential remains uneven. Many university staff indicate that a “lack of familiarity with the technology, uncertainty about its use, or a lack of time to engage, contributed to their reluctance to use AI tools for their work” (McDonald, 2024). Building this confidence demands accessible opportunities that honour diverse starting points, and courageous experimentation which normalises trial-and-error—two of the principles guiding the University of Newcastle’s GenAI response. Our GenAI Tinker Workshops operationalise these principles by offering low-stakes, experimentation-based experiences where educators co-explore AI tools, reflect on ethical dimensions and iterate on real teaching artefacts. Playful, informal learning environments may lower cognitive load and heighten curiosity by reducing the focus on serious workplace implications (O’Hara & Lo, 2025). Tinkering studios, for example, cultivate “permission to fail” that supports creativity and innovation (Henriksen, 2021). The informal, low-stakes format of the sessions promoted engagement, echoing Worth’s (2024) assertion that “AI playgrounds” backed by appropriate guidance can support learning. Responding to TEQSA’s call for structured capacity-building around GenAI (TEQSA, 2024), Learning Design & Teaching Innovation designed monthly Tinker Workshops capped at roughly ten participants. Each 60-minute session invited academics to “tinker” with tools like ChatGPT, NapkinAI Copilot, NotebookLM and Gamma to draft real artefacts (course plans, presentations, podcasts, images, etc) for use in their teaching activities. Facilitators guide the participants through a simple introductory activity and focus on identifying opportunities to promote discussion in the group. Evaluation of the workshops, through informal participant feedback and in-session observations, showed an appreciation for this approach. Participants reported increased confidence not only with the tools but with navigating rapid change more broadly, citing reduced anxiety and a stronger sense of community. Participants noted possible operational benefits identified by exposure to various AI tools (example comments – “this will save me so much time” and “That just took about 20 seconds, it would take me three days to do the same.”). They also developed insights beyond the immediate operational focus of the activity (example comments – “I’d never thought of structuring my course that way, it’s really clear” and “This has really helped me think about how I use images and graphics in my lectures.”). Further, the informal nature of the sessions, with facilitator positioned as fellow learner rather than expert instructor supported honest discussion and debate around the implications (both positive and negative) of GenAI (example comments – “it’s just good to be able to talk about this stuff, and know everyone is in the same boat” and “I kind of knew that not all AI is not bad, but have not had the time to learn more.”). End of session discussion is guided towards awareness of the fallibility of GenAI outputs, and the importance of human expertise in co-creating learning experiences with GenAI. By embedding accessible, iterative, low-stakes Tinker Workshops alongside more traditional GenAI initiatives, we aim to directly tackle the challenge of uneven confidence driven by unfamiliarity, uncertainty and limited time. The format normalises trial-and-error, scaffolds ethical reflection and focuses “AI playground” tinkering on real world use cases—delivering immediate efficiency gains while supporting consideration of broader pedagogical impacts. Participants report reduced anxiety, stronger professional community and increased confidence in decision making around the use of GenAI. In short, structured opportunities to experiment safely are building adaptable educators who can collaborate with AI responsibly, meeting TEQSA’s call for capability-building and strengthening resilient, human-centred learning.

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).

This study dives into how Generative Artificial Intelligence (GenAI) can boost higher-order thinking skills among vocational college students in Guangxi, China. The researchers used a quasi-experimental mixed-methods approach, involving 70 students from information technology and engineering programs. They split the students into two groups: one that experienced GenAI-enhanced interactive teaching and another that followed traditional instruction. The experimental group utilized GenAI in specific roles like “virtual debater” and “decision consultant,” which helped them engage more actively in debates, case analyses, and reflective decision-making. The quantitative results from pre- and post-tests showed a notable improvement in critical thinking, logical reasoning, and reflective skills for the students in the experimental group. Meanwhile, qualitative data gathered from observations, student journals, and interviews highlighted an increase in metacognitive awareness, deeper engagement with problem-solving tasks, and a positive view of AI as a helpful partner in learning. However, there were also concerns about students becoming too reliant on AI tools and the necessity for guided facilitation. The findings indicate that when implemented thoughtfully, GenAI tools can significantly enhance higher-order thinking in vocational education, especially when paired with teaching strategies that promote active learning and reflection. This study underscores the importance of integrating AI ethically and calls for customized instructional designs to ensure fair access and ongoing cognitive growth. These insights add to the ongoing conversation about human-machine collaboration in education and provide valuable guidance for future curriculum innovations in tech-driven learning environments.

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 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.