Generative AI

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Generative AI has garnered global attention. This study aims to explore the association between generative AI self-efficacy and generative AI acceptance among university students and underlying mechanisms. A sample of 353 university students in China was measured using scales for generative AI self-efficacy, generative AI trust, generative AI risk perception, and generative AI acceptance. Structural equation model and Model 1 from SPSS PROCESS 4.0 were employed to test the research hypotheses. The results indicated that a positive association between generative AI self-efficacy and generative AI acceptance among university students (β=0.49, P<.001). Assistance, comfort with generative AI, and technological skills are positively association with generative AI acceptance. Generative AI trust mediated the relationship between generative AI self-efficacy and generative AI acceptance, as well as the relationship between anthropomorphic interaction and generative AI acceptance. Generative AI trust also mediated the relationship between technological skills and generative AI acceptance. Moreover, Generative AI risk perception negatively moderated the relationship between generative AI trust and generative AI acceptance. This study provides a theoretical foundation for further research on generative AI.

Although artificial intelligence is fundamentally reshaping the ecology of music learning, existing research has disproportionately emphasized performance outcomes while underexamining psychological mechanisms, leaving the tension between technological empowerment and cognitive dependence theoretically underarticulated. Following PRISMA 2020, we systematically searched four databases and included 21 empirical studies to examine how three AI tool types-assessment-oriented AI, generative AI, and Comprehensive/adaptive AI-differentially shape learners' self-beliefs and cognitive agency in music education. The evidence base remains geographically and developmentally concentrated: most studies were conducted in China and in higher education, while early childhood settings were absent. Using thematic analysis, we conducted cross-type comparisons and synthesized psychological pathways. Assessment-oriented AI most consistently strengthened ability beliefs via objectified, visualized feedback and positioned cognitive agency around self-monitoring, self-reactiveness, and self-reflectiveness. Generative AI tended to enhance value-attitude beliefs and intentionality by lowering technical barriers and reconfiguring learners' creative roles toward aesthetic decision-making and output curation. Comprehensive/Adaptive AI more often supported forethought and sustained engagement by dynamically maintaining alignment between task challenge and learner capability. Across studies, psychological empowerment manifested as increased perceived competence and control, heightened motivation and engagement, and visible self-regulated learning behaviors. Cognitive dependence, however, emerged through outsourcing evaluative authority, score-driven goal distortion, algorithm-accommodating self-censorship, and attributional shifts that tether confidence to technological support. Developmental differences were also observed regarding dependence mechanisms: primary learners tended to perceive AI as a restrictive "scoring referee," whereas higher education students demonstrated strategic agency in orchestrating AI assistance. Specifically, a critical construct-tool mismatch was identified: while assessment AI consistently supports self-reflectiveness, generative AI currently lacks sufficient evidence for fostering learners' forethought. In light of the identified construct-tool mismatch, future research should prioritize addressing the paucity of evidence on how generative and adaptive AI foster forethought and intentionality, thereby clarifying whether such technologies ultimately reconstruct or erode learners' cognitive agency.

This study investigates the potential of small marketing firms to disrupt the market by adopting generative AI technology and the theory of disruptive innovation. The study employs a qualitative approach, combining a comprehensive literature review with in-depth interviews with leaders of small marketing firms. The research findings position generative and conversational AI as the next technological evolution, succeeding the internet and mobile/social era. It is the first study applying the theory of disruptive innovation to generative AI use in small marketing firms, presenting a positive outlook toward integrating generative AI into marketing operations. The study contributes to the emerging knowledge of AI in marketing, offering practical implications for scholars and practitioners to advance this field.

With the growing proliferation of generative AI, discussions about the societal implications of AI, including opportunities and risks, have intensified. Ultimately, the success of initiatives to integrate (generative) AI into news production and dissemination will depend on the concerns, trust, and willingness of citizens to accept new AI-driven solutions. This study explores attitudes toward the use of AI in journalism, perceptions of generative AI, and how these factors influence trust in and credibility of information. Using a survey on a representative sample of the Dutch population (N = 1478), we analyze perceived benefits and concerns about AI and explore individual acts of resistance against the application of AI in journalism (e.g., unwillingness to pay for news that AI produces). With this study, we extend previous research on attitudes towards AI by also considering general attitudes towards generative AI, individuals’ risk perceptions towards generative AI, and policy support regarding regulating AI. More importantly, this study also investigates individual follow-up actions in the form of acts of resistance against the use of AI in journalism. The findings of this paper are particularly significant due to the rapid growth of generative AI, its integration into the news cycle, and international policy developments.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

The rapid rise of Generative AI (GenAI) tools is reshaping conversations about assessment and feedback in higher education. While much institutional attention focuses on detection, compliance, and academic integrity (Cotton et al., 2024), this presentation shifts the lens to educators and how they are actually using GenAI in assessment practice. We present findings from a grant-funded initiative at UNSW that explores educator-led innovation through a Postcards of Practice approach. The Postcards of Practice are one-page, practice-based narratives where educators document their use of GenAI tools. These postcards highlight applications including formative feedback generation, student prompting literacy, assessment redesign, and co-creation with AI. They reveal how educators are experimenting with GenAI to support student learning while navigating ethical concerns, transparency, and pedagogical alignment. Our study uses a qualitative interpretive methodology, combining thematic analysis of the postcards with follow-up interviews. The analysis draws on theoretical frameworks including feedback literacy (Carless &amp; Boud, 2018), dialogic assessment (Nicol, 2010), and new paradigm feedback design (Winstone &amp; Carless, 2020). We also apply institutional and national GenAI guidelines (Liu &amp; Bridgeman, 2023; Perkins, 2023) to surface shared values such as authenticity, inclusivity, and responsible innovation that guide educators’ decisions. The aim of this study is to explore how educators are experimenting with GenAI in assessment and feedback, and to capture their emerging practices and reflections through the Postcards of Practice initiative. The central research question guiding this work is: How are educators integrating GenAI into assessment and feedback, and what opportunities, challenges, and support needs arise from these practices? This work advances Technology Enhanced Learning (TEL) by providing empirical insights into how GenAI is actually integrated at the coalface of teaching. Educators describe how GenAI supports more frequent, personalised feedback and builds student agency in learning. At the same time, they raise concerns about over-reliance, AI hallucination, and the need for clear pedagogical scaffolding. These reflections point to the need for professional development that is discipline-sensitive, responsive, and grounded in practice. The postcard approach also functions as a professional learning intervention. It prompts reflection, encourages cross-disciplinary dialogue, and helps build a local community of practice around GenAI use. Through this model, we demonstrate an innovative and scalable method of capturing and supporting TEL innovation in real time. The findings suggest GenAI is prompting a rethinking of assessment: from summative, compliance-driven models to more transparent, formative, and student-centred designs. Educators begin to embed feedback literacy, ethical AI use, and critical prompting into their teaching, with clear implications for program-level assessment and graduate capability development. To strengthen clarity, we propose a concise diagram mapping the emerging practices captured in the postcards against the theoretical frameworks of feedback literacy, dialogic assessment, and new paradigm feedback design. This visual representation illustrates how practical insights align with, extend, or challenge these frameworks, making the study’s contribution accessible across diverse tertiary contexts. This proposal offers exemplary innovation in TEL by foregrounding bottom-up, practice-led experimentation with GenAI. It is grounded in strong theoretical frameworks and applicable across diverse tertiary contexts. The Pecha Kucha format will present key insights through rich visual storytelling, including excerpts from the postcards themselves. We conclude by proposing future directions for research and institutional strategy, including how to embed GenAI into assessment ecosystems in ways that enhance learning, uphold integrity, and empower educators to lead digital transformation from within.

Generative Artificial Intelligence or Generative AI is an innovation with a terrible name.First, generative means something that originates, produces, or even reproduces.Next, artificial Intelligence is defined as intelligence exhibited by machines (machine intelligence).So, if the two are put together the result is a machine that originates intelligence—in other words, a thinking machine. Hmmm!Generative artificial intelligence is typically defined as a technology capable of generating text, images, videos, or other data, usually in response to prompts.At best the name generative AI is confusing, and to many it is scary.Everett Rogers in his classic book Diffusion of Innovations (2003) states that “the name given to an innovation often affects its perceived compatibility, and therefore its rate of adoption (p. 250).”Certainly, Generative AI is an innovation—it is new and is probably the innovation of this decade.Everett Rogers defined Innovation as “an idea, practice or object that is perceived as new by an individual or other unit of adoption (p. 12)”.What does all this mean to distance educators?Maybe first we should learn more about Generative AI. So, just for the fun of it, the written sections above were entered into Gemini AI, a generative AI tool, and a competitor to ChatGPT.Here is what Gemini produced:You’re right, “Generative AI” while descriptive, and can be misleading.Here’s a breakdown of why it might not be the best term and some suggestions for alternatives:The Problems with “Generative AI”The best term will depend on the context. “Machine Learning for Content Creation” might be more suitable for a technical audience, while “Creative AI” could be better for a general audience.This revision process took about 5 seconds, and by any measure the results were impressive—the Generative AI analysis seemed insightful and accurate.Rogers recommends that potential innovation users should learn about the innovation by studying its attributes—there are five—relative advantage, compatibility, complexity, trialability, and observability.First is the idea of relative advantage—defined as the degree to which an innovation is perceived as being better than the idea it replaces. Relative advantage is often expressed as the innovation’s economic profitability or its ability to convey social positions.The next characteristic of an innovation is its compatibility, which is the degree to which the innovation is consistent with existing values, past experiences, or user needs. Innovations can be either compatible or incompatible.Next is complexity, explained as whether the innovation is perceived as difficult or easy to use.A complex innovation, as perceived by potential adopters, can significantly hinder adoption.Trialability is the ability to experiment with an innovation before adoption. If an innovation can be easily tried it will be likely to have a rapid rate of adoption or rejection.Observability is the visibility of applying the innovation. Observability means seeing the results of using the innovation. High observability promotes faster decisions about adoption.In other words, does Generative AI allow us to do things better? Next, are the results of use compatible with what the user needs or wants? Third, is Generative AI easy or difficult to use, and can we quickly try it out? Finally, can we see the results when Generative AI is used?Computer scientists argue that Generative AI is better, compatible, easier, not complex, and results are clear—maybe this is true, but the name is still horrible.Generative artificial intelligence sounds intimidating and threatening. Perhaps ‘creative artificial intelligence’ would be more inviting—perhaps not!Distance educators should understand, study, and evaluate Generative Artificial intelligence and write about it—perhaps someone from the U.S. Distance Learning Association could “coin” a new name.(Distance Learning would love to publish manuscripts on this and other related topics.)And finally, Galen said “The chief merit of language is clearness, and we know that nothing detracts so much from this as do unfamiliar terms.”NOTE: Rewrites of the second half of this column using personal intelligence (PI) took 5 tries and 4 hours—a relative advantage?

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

Generative AI (GAI) is reshaping the future of work in architecture by introducing innovative ways for humans to interact with technology, transforming the design process. In education, GAI offers students immersive environments for iterative exploration, enabling them to visualize, refine, and present design concepts more effectively. This paper investigates how GAI, through a structured framework, can enhance the learning of design tasks in elaborating interior design proposals, and preparing students for the evolving professional landscape. Drawing on the platform Midjourney, students explored concepts, material moodboards, and spatial compositions, simulating professional scenarios. Each student was assigned a real client and tasked with developing tailored design solutions, guided by client and tutor feedback. This approach demonstrates how GAI supports the development of future-oriented skills, directly linking education to the technological shifts in professional practice (Araya, 2019). The study adopts a practice-based methodology, documenting the outcomes of an interior design workshop where students employed GAI tools to develop client-specific proposals. Students engaged in role-playing, meeting their assigned clients face-to-face to gather requirements, acting as junior architects. They analyzed client feedback to inform the design phase, after which they used a structured framework for better using GAI to iteratively refine their proposals. By generating AI-assisted visualizations of spatial configurations and materials, students developed final design solutions that aligned with client expectations. Data from GAI iterations, client feedback, and tutor evaluations were used to assess how effectively AI tools contributed to producing professional-quality designs (Schwartz et al., 2022). Two research questions frame this investigation: (1) How does Generative AI enhance students' ability to create client-specific interior design solutions, from concept generation to final visualization, within a structured educational framework? (2) How does the integration of GAI tools impact the teaching of iterative design processes in architecture, particularly in preparing students for the future of work in the profession? The findings reveal that GAI significantly improved students' design outcomes by enabling them to visualize and refine their proposals based on real-world scenarios. GAI facilitated the exploration of current trends and supported the creation of material moodboards and space visualizations. The iterative nature of AI tools allowed students to better grasp the relationships between spatial configurations, design choices, and client needs. Their final proposals, incorporating AI-generated outputs, were praised for their conceptual clarity and technical precision, reflecting how AI-driven processes can transform traditional workflows (Burry, 2016). This study illustrates the transformative potential of GAI in architectural education, particularly in fostering dynamic human-technology interactions. By leveraging AI, students maintained control over outputs while transforming abstract concepts into client-ready designs. Moreover, the iterative feedback loop enabled by GAI promoted a more adaptive and responsive learning process, giving students real-time insights into their design decisions. These insights reflect broader changes in the future of work, where AI-driven tools will become integral to professional practice. Future research could explore expanding GAI’s role in more complex design stages, such as schematic design and development, building on the benefits observed in this study.

Generative AI (GAI) marks significant advancements in technology and machine learning models. It has achieved a newer and higher level of creativity and innovation through the AI system. With such rapid growth and boom in GAI, gaps exist in the current literature about the organizations and individual levels of applying GAI. The researchers conducted a mixed-methods study to explore business professionals’ experiences and perceptions of using GAI. This current study examined the purpose of using GAI and the statistically significant differences in productivity before using GAI versus after using GAI. The impact of gender, age, and educational background on work productivity while using GAI was also investigated. Furthermore, this study researched the most prominent GAI tools these business professionals use. The advantages and disadvantages of using GAI were analyzed through detailed content analyses of the qualitative data using NVIVO and SQL. This study highlights the vital impact of GAI in improving efficiency, increasing productivity, and fostering innovation. It also calls for strategic planning to maximize the GAI benefits in organizational implementations while addressing overreliance, ethics, security, hallucination, and user experience concerns.

Objectives This study aims to explore the direction of Generative AI literacy education through the exploration of college students' experiences using Generative AI. Methods For this purpose, written and face-to-face in-depth interviews were conducted with 12 university stu-dents (5 male students and 7 female students) and analyzed by applying Colaizzi's phenomenological method, which consists of seven steps to explore the nature of common experiences rather than the individual participants. Results As a result of analyzing the interview data, 5 semantic themes and 14 sub-themes were derived under the essence of the experience called ‘co-evolution’. The five semantic themes consist of ‘getting to know new media’, ‘useful but dangerous existence’, ‘sharing roles with Generative AI’, ‘adapting to changes in the learning environment’, and ‘seeking a life that coexists with Generative AI’. The 14 semantic themes consisted of ‘matching up with Generative AI’, ‘creating my own method of using prompts’, ‘limitation of inanimate object’, ‘repeating unnecessary learning activities’, ‘incomplete learning assistant’, ‘my own tutor’, ‘reducing learning time’, ‘setting my own scope of use’, ‘another team member’, ‘passive new media user’, ‘recognition of reality and acceptance of new media’, ‘improving the competence to use Generative AI’, ‘selecting Generative AI according to the purpose of use’, and ‘competition with the human-specific domain’. Conclusions The discussion points on the direction of Generative AI literacy education for university students are as follows. First, it provides education in which prompts can be effectively input. Second, it provides guidelines so that the Generative AI can be used as an auxiliary tool rather than the main tool for learning. Third, it provides education on the characteristics of the Generative AI. Fourth, it enhances education on usage ethics.

The purpose of this study is to analyze the experiences and educational needs of foreign undergraduate students enrolled in Korean universities using Generative AI and to find ways to effectively utilize Generative AI in the writing process. To this end, a survey was conducted on 219 foreign undergraduate students who took Liberal Arts &lt;College writing&gt; courses at A University. As a result of the analysis, 39.7% of foreign undergraduate students who participated in this survey answered that they had used Generative AI when performing assignments at university. Respondents mainly used Generative AI for outlines, summaries, solving exercises, and writing general reports, and used Generative AI to better understand the content, to generate ideas, to translate, and to revise their expressions. And as a result of analyzing their educational needs, we found that foreign undergraduate students need writing, citation, and writing ethics education when using Generative AI, even if they are aware of citation methods and problems when using Generative AI in the process of performing university assignments. Based on these results, this study suggested educational implications for writing when using Generative AI in writing subjects. It is necessary for us to teach ciation methods and writing ethics when using Generative AI. Also, it is necessary for us to teach writing students using Generative AI the types of writing that take into account the majors of foreign undergraduate students or the types of writing that learners write with frequently. How to use Generative AI in writing classes can be taught to foreign undergraduate students, especially in the writing revision stage.

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.

Generative AI (GenAI) is increasingly being integrated into creative work, either as a collaborator or as a replacement for human creators. More previous work has focused on augmenting users' creativity in the context of individual-GenAI collaboration. Humans often engage in group creative works across countless real-world contexts, yet the effects of GenAI on such group creativity remain largely unexplored, an urgent gap that demands immediate research attention. To address this gap as a first step, we conducted an electronic brainstorming experiment with three conditions in a within-subjects design: (A) groups of three participants without GenAI, (B) groups of three participants with GenAI, and (C) individual participants with GenAI (N = 24). In the results, GenAI-assisted group brainstorming significantly reduced the number of human-generated ideas, and did not significantly change the quality compared to brainstorming without GenAI. Plausible explanations for these are that reliance on GenAI is further increased in a group setting, and social loafing is more likely to occur. Therefore, we found that simply incorporating a GenAI agent does not necessarily lead to more effective human-GenAI co-creation in groups. On the other hand, compared to individual use of Gen AI, originality, elaboration, and flexibility improved significantly, so GenAI-assisted group brainstorming may have useful aspects. Based on our findings, we discuss the design implications of the strategy for leveraging GenAI effectively, future ideation methods, and creativity support systems. In particular, we suggest two interventions: 1) interactive idea generation, where humans and GenAI take turns combining and improving each others ideas, or 2) reducing over-reliance on GenAI. Our paper contributes to this domain by investigating the effects of human-GenAI collaboration in groups on brainstorming and providing design implications for more effective co-creation.