Generative AI and its dilemmas: exploring AI from a translanguaging perspective

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).

In today's dynamic and competitive oil and gas industry, the integration of Artificial Intelligence (AI) has emerged as a game-changer, offering unparalleled opportunities for optimization, cost reduction, and operational excellence. The main objective of autonomous operations is to minimize manual interactions and maximize self-directed plant operations. ADNOC Onshore has implemented generative AI agents in daily maintenance and production operations to boost workforce productivity in the journey of achieving autonomous operations. This paper explains the use cases, challenges, AI architecture & data security in deployment. Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. GPT-4 Turbo is a large multimodal model (accepting text or image inputs and generating text) that can solve difficult problems with greater accuracy and advanced reasoning capabilities. The scope includes empowering reliability, maintenance, and operations professionals to draw insights from equipment manuals, asset operating manuals and operating procedures, maintenance records, and safety & integrity manuals. This in-house solution with support across structured and unstructured data, an LLM-agnostic architecture, deterministic responses with source references, and granular access controls. The solution has been integrated ERP SAP system and sensor time series PI system, data historians for integrated context. A unique automated contextualization engine has been used based on oil and gas specific vocabulary to bring context to their operations. A conversational interactive agent has been built for user interactions. The maintenance and operations engineer can receive suggestions on the proper steps to identify the root cause based on OEM product manuals, previous events, and current performance. This Generative AI solution accelerates time to insight for operators by equipping teams to streamline maintenance operations and Investigate maintenance records with generative AI to troubleshoot operations challenges more efficiently. The internal study showed that operational productivity has increased by 20% after this solution's implementation. For the model to understand industrial environments, it would require retraining the model on industrial data. Using existing models on uncontextualized, unstructured industrial data significantly increases the risk of incorrect and untrustworthy answers – referred to as AI hallucinations. Another significant challenge lies in the dependence on the quality and quantity of available data for training. AI models require extensive and representative datasets to produce accurate and reliable predictions. Large language models are a type of artificial intelligence (AI) model designed to understand and generate human language. These models are built upon deep learning architectures, particularly transformer architectures. Generative AI can play a significant role in oil and gas asset operations towards the goal of achieving autonomous operations.

Generative artificial intelligence (GAI) created a whirlwind in late 2022 and emerged as a transformative technology with the potential to revolutionize various industries, including design. Its feasibility and applicability have been extensively explored and studied by scholars. Previous research has investigated the potential of AI in different domains, such as aiding data collection and analysis or serving as a source of creative inspiration. Many design practitioners have also begun utilizing GAI as a design tool, stimulating creativity, integrating data more rapidly, and facilitating iterative design processes. However, excessive reliance on GAI in design may lead to losing the uniqueness emphasized in the field and raise concerns regarding ethical implications, biased information, user acceptance, and the preservation of human-centered approaches. Therefore, this study employs the double-diamond design process model as a framework to examine the impact of GAI on the design process. The double diamond model comprises four distinct stages: discover, define, develop, and deliver, highlighting the crucial interplay between divergence, convergence, and iteration. This research focuses on GAI applications' integration, timing, and challenges within these stages.A qualitative approach is adopted in this study to comprehensively explore the potential functionalities and limitations of generative AI at each stage of the design process. Firstly, we conducted an extensive literature review of recent advancements and technological innovations in generative artificial intelligence. Subsequently, we participated in lectures and workshops and invited experts from various domains to gather insights into the functionality and impact of generative AI. Later, we interviewed design professionals experienced in utilizing generative AI in their workflow. Data triangulation and complementary methods like focus groups are employed for data analysis to ensure robustness and reliability in the findings related to the functionality of generative AI.The findings of this study demonstrate that generative AI holds significant potential for optimizing the design process. In the discovery stage, generative AI can assist designers in generating diverse ideas and concepts. During the definition stage, generative AI aids in data analysis and user research, providing valuable insights to designers, such as engaging in ideation by addressing "How might we" questions, thereby enhancing decision-making quality. In the development stage, generative AI enables designers to rapidly explore and refine design solutions. Lastly, in the delivery stage, generative AI can help generate design concept documentation and produce rendered design visuals, enhancing the efficiency of iterative processes.This study contributes to a better understanding of how generative artificial intelligence can reshape the existing framework of the design process and provides practical insights and recommendations for contemporary and future designers.

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.

ChatGPT and generative artificial intelligence have the potential to transform many business sectors. These technologies are changing customer engagement, operational efficiency, and strategic innovation. ChatGPT and generative AI models are streamlining processes, improving personalization, enabling predictive analytics, and supporting decision-making in healthcare, finance, retail, and education. Multimodal models will enable generative AI to integrate text, images, and other data types, expanding its use in complex scenarios like medical diagnostics, financial modeling, and virtual training. Since global regulatory frameworks require transparent and accountable AI, ethical AI development and data privacy are crucial. Responsible AI policies and practices are being developed by businesses to ensure compliance and build consumer and stakeholder trust. AI interpretability and explainability improvements will likely increase adoption, especially in highly regulated sectors. AI-powered workforce management automation foreshadows a collaborative future where AI complements human expertise. Continuous advancements in generative AI will boost productivity, innovation, and market opportunities, transforming industries. This research examines these anticipated developments and proposes ethical and economically beneficial strategies to maximize generative AI's potential.

With the rapid advancement of digital transformation and artificial intelligence technologies, generative artificial intelligence (Generative AI) has shown broad application prospects in the domain of corporate management. Based on the Technology–Organization–Environment (TOE) theoretical framework and value-chain reengineering theory, this paper constructs an optimized model for empowering corporate financial management with Generative AI. First, it analyzes the core capabilities of generative models in data synthesis, text comprehension, and decision support, and explores innovative pathways for multi-scenario automatic budget preparation, dynamic financial forecasting, and automated compliance audit report generation within the contexts of corporate budgeting, predictive analysis, and risk control. Second, it proposes implementation strategies—such as organizational restructuring, enhancement of data-governance systems, and establishment of continuous iteration mechanisms—and, drawing on representative enterprise case studies, demonstrates how Generative AI improves forecast accuracy, optimizes risk alerts, and enhances audit efficiency. Finally, it addresses challenges related to model bias, data-privacy protection, computational resource investment, and algorithmic transparency, offering technical improvements and governance measures to guide enterprises in deploying Generative AI applications under compliance and ethical constraints. The study shows that Generative AI not only elevates the intelligence level of financial management but also drives enterprise value creation and sustained innovation through dynamic decision support.

Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain. This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature. Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques). In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care. This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

In today's world, generative artificial intelligence (GenAI) is driving transformative shifts in technological advancements and research paradigms, requiring new methods and ideas to be adopted in qualitative research. This chapter explores the opportunities of using GenAI for advanced data analysis in qualitative methodologies. It focuses on how GenAI integration can reveal previously hidden patterns in the analysis of social phenomena. By reviewing the latest advancements in GenAI applications for qualitative studies from 2022 to 2024, the chapter highlights the significant role of GenAI in improving the quality, validity, and creativity of qualitative research. Furthermore, it discusses how GenAI can open up new pathways for comprehensive exploration, allowing a deeper understanding of complex social dynamics.

This research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Using a qualitative research approach grounded in the principles of grounded theory, we conducted semi-structured interviews and an open-ended qualitative data collection instrument with technology vendors, implementation consultants, and HEI professionals that are actively exploring GenAI applications. Our findings highlight six primary types of GenAI—textual analysis and synthesis, data summarization, next-best action recommendations, speech synthesis and translation, code development, and image and video creation—each with applications across student recruitment, advising, alumni engagement, and administrative processes. We propose an evaluative framework with eight readiness criteria to assess institutional preparedness for GenAI adoption. While GenAI offers potential benefits, such as increased efficiency, reduced costs, and improved student engagement, its success depends on data readiness, ethical safeguards, and institutional leadership. By integrating GenAI as a co-intelligence alongside human expertise, HEIs can enhance CRM ecosystems and better support their constituents.

PurposeThe purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations.Design/methodology/approachThe study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI.FindingsThe study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly.Research limitations/implicationsThe small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding.Originality/valueThe study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.

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.

Identifying highly correlated determinants influencing student nurses' behavioral intention of using generative artificial intelligence (Generative AI): A network analysis.

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.

Despite concerns about students’ use of generative AI (GenAI) in assessment, the technology has become embedded into students’ everyday assessment practices. It is unclear how students are making judgements about their ways of working with GenAI and what impact this has upon their learning. This qualitative multimodal study examines students exercising judgement as they work with GenAI to complete assessment tasks. Twenty-six interviews were conducted with Australian university students, primarily using a scroll-back approach, which revisits traces of students’ historical interactions with GenAI in the interviews. Employing a holistic definition of judgement and a narrative approach to analysis, we interpreted six distinct categories of judgement events. These are: 1) making judgements about knowledge when working with GenAI; 2) learning to judge GenAI through its limitations; 3) relying on GenAI for things they could not otherwise do; 4) adopting ideas with low levels of criticality; 5) misjudging GenAI contributions as their own; and 6) submitting GenAI content in an assignment without judging it. This study suggests GenAI use strongly shapes student learning in complex ways when undertaking assessment tasks, and that making judgements about GenAI entails a student making judgements about their own knowledge, deficits, and quality of contributions.

Throughout history, humans have experienced numerous transformative events. Currently, we are experiencing a new disruptive event, generative artificial intelligence (GenAI). The research objectives of this article are to analyse and discuss how communication professionals use GenAI in their work, to analyse and discuss to what extent communication professionals have the required knowledge and skills to use GenAI optimally, and finally to analyse and discuss what type of help communication professionals need in their GenAI communication practices. The article draws on quantitative and qualitative data from an online survey of communication professionals and on qualitative interview data from eight semi-structured research interviews. The analysis was framed by a specially developed human-machine communication network model, referred to as the HMC network model, and a thematic analysis. The analysis showed that GenAI tools are widely known by communication professionals, but that they still do not seem to use GenAI optimally when researching and writing. The analysis also indicated that communication professionals need GenAI competencies and training, and that they need models, frameworks, and guidelines on how to use GenAI. The article presents the HMC network model and three GenAI support tools designed to help communication professionals use GenAI in their communication practices.