Generative AI for Text-to-Video Generation: Recent Advances and Future Directions

Immersive learning plays a crucial role in effective second language (L2) acquisition, but many learners face limited opportunities to interact with native speakers. While existing research highlights the importance of immersion in L2 learning, there is still a gap in understanding how Generative AI (GenAI) can provide greater access to such immersive environments. This study aims to address this gap by exploring the factors influencing immersion in GenAI‐mediated L2 learning. Drawing upon the cognitive‐affective model of immersive learning, the control‐value theory, and the technology acceptance model, the study examined the impact of cognitive factors (e.g., perceived ease of use and perceived usefulness) and affective factors (e.g., enjoyment and boredom) on immersion, using a sample of 460 Chinese college L2 learners. Structural equation modelling with Amos 24 was applied to analyse the data, yielding several key findings. (i) Perceived ease of use positively predicted perceived usefulness and enjoyment but had no direct effect on immersion or boredom. (ii) Perceived usefulness positively influenced immersion and enjoyment while negatively affecting boredom. (iii) Enjoyment was a positive predictor of immersion, whereas boredom had no significant effect. (iv) Mediation analysis revealed that perceived ease of use indirectly predicted immersion through perceived usefulness and enjoyment but not through boredom or in combination with perceived usefulness. The study concludes with implications for practice and suggestions for future research.

This article addresses the transformative role of Generative-AI (Gen-AI) in the creative media and arts industries, focusing on concerns about the disappearance of human creative labour. It critically examines the discourse of the 2023 Writers’ and Actors’ strikes, which replicates prevailing assumptions of the superiority of human creativity over Gen-AI. This discourse emphasises a ‘replacing tasks’ model, anticipating a future where AI assists human creatives in a limited capacity. Against this background, the article applies the ‘meaningful work’ framework to provide an approach to human-AI coexistence which values amplifying human creativity rather than merely supplementing (or supplanting) it. This framework is a conceptual shift which more convincingly recognises and values human contributions in the media industries. Drawing on historical parallels, such as the transition to digital visual effects during the production of Jurassic Park (1992), the article demonstrates how transformational technologies can transcend mere task simplification. The article underscores the importance of creative artists actively finding ways to begin clearly articulating the specific details of human creativity that comprise their artistic agency, and therefore the article advocates an approach that theorises the intrinsic value of human contributions to the media industries while accommodating Gen-AI.

Generative AI (GenAI) is experiencing rapid growth, particularly in its application as a tool for qualitative text analysis—a key element of Human-Computer Interaction (HCI) research. This study examines the potential of GenAI, specifically ChatGPT, to assist in the analysis of qualitative research data. Four qualitative HCI studies, previously conducted and analyzed by our research group, were selected for this investigation. ChatGPT was employed to perform AI-assisted analyses on the raw data from these studies, and the AI-generated insights were then compared with the human-led analyses already completed. The results reveal significant alignment between the human and AI-assisted analyses, indicating that GenAI can serve as an effective support tool in qualitative research. However, while GenAI offers considerable advantages in enhancing research efficiency, human oversight remains crucial to ensuring accurate interpretation and contextual alignment. This study also provides practical recommendations for researchers interested in incorporating GenAI into their qualitative analysis processes.

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.

Generative Artificial Intelligence (GAI) transforms our technological interactions, including new capabilities and concerns about biases and misuse. In the field of human-computer interaction (HCI), previous research has investigated generative AI in relation to human-centred AI, user trust, user experience, design work, co-creativity, and user personas. This study applies the theoretical lens of affordances and constraints to ask the question: Which affordances and constraints of generative AI can be identified in human-computer interaction research? The study employs a scoping literature review approach to collect data from the Web of Science Core Collection databases. The query string combined keywords, such as “generative”, “artificial intelligence”, and “human computer interaction”, with Boolean operators AND and OR. Inclusion and exclusion criteria were used in the screening of 156 identified articles, from which a total of 37 were selected for inclusion in the study. An initial categorization matrix, based on the theory of affordances, was used to conduct a deductive thematic analysis. The analysis followed the guidelines for thematic analysis suggested by Braun and Clarke. The investigation identified seven key themes, with included sub-themes, illustrating the varied applications and potential effects of generative AI. The seven key themes are: 1) improving algorithms, 2) collaborative work, 3) education support, 4) truth issues, 5) biases, 6) ethical considerations, and 7) consequences for job market. The study further highlights the importance of considering contextual differences and short-term and long-term consequences when applying GAI technologies, as well as ethical considerations, such as ethical and legal accountability. The paper concludes with a novel conceptual model for affordances and constraints of generative AI, informing future research, guiding stakeholders’ use and implementation, and providing design recommendations for generative AI systems across various sectors.

Generative AI (GenAI) tools, particularly Large Language Models (LLMs), are increasingly used across clinical contexts; including to support patient information needs. As these technologies become more prevalent, understanding their utilisation and evaluation in practice is critical. This scoping review aimed to map existing literature on GenAI applications in education for patients with cancer and identify trends in evaluation practices. A scoping review was conducted following PRISMA-ScR guidelines. PubMed and Medline databases were searched for studies published between January 2019 and November 2024. Fifty-four eligible articles were analysed for GenAI models used, treatment modalities, education contexts, prompt sources, and evaluation domains and metrics. Most studies (81.5%) were published in 2024, with over half (55.6%) originating from the USA. ChatGPT-3.5 and ChatGPT-4 were the most frequently used models. Decision-making and general disease information were the predominant education contexts. Evaluation of GenAI outputs was reported in 96% of studies, with accuracy (61.1%), readability (42.6%), and quality (29.6%) as the most common domains. More than half (50.8%) of evaluation metrics were custom scales, indicating limited use of standardised tools. Patient-centred frameworks were rarely applied. GenAI shows promise in enhancing patient education for cancer care, but evaluation practices lack standardisation and cultural responsiveness. Future research should prioritise validated frameworks, patient-centred metrics, and prompt engineering strategies to ensure safe, equitable and effective integration of GenAI in clinical care.

This paper presents a review and proposes framework for training older financial services employees (age 45+) in Generative AI applications. As banks rapidly adopt AI tools, our research identifies specific barriers facing older workers including technological anxiety, interface complexity, and knowledge retention challenges. We conclude that older workers require approximately 30-40% more training time than younger colleagues but achieve comparable proficiency with appropriate support. Key success factors include: (1) peer mentoring systems pairing tech-savvy junior employees with senior staff, (2) simplified interfaces removing unnecessary technical options, and (3) job-specific practice scenarios rather than abstract exercises. This paper further explores the critical need for training older adults in Generative AI (GenAI). While GenAI offers transformative potential across various sectors, ensuring equitable access and its adoption requires addressing the specific challenges faced by older populations. These challenges include digital literacy gaps, concerns about data privacy and security, and the need for user-friendly interfaces especially for older population who might be largely non-technical. The paper examines recent literature and key considerations for developing effective GenAI training programs for older adults, emphasizing the importance of foundational digital skills, accessible language, personalized learning, and ongoing support. Additionally, this study highlights the digital divide faced by older adults, emphasizing the need for structured AI training programs. Furthermore, it analyzes future projections of GenAI’s impact, highlighting the necessity of upskilling and reskilling the workforce, including older individuals, to bridge the emerging GenAI skills gap. The paper categorizes and quantifies the types of sources used to support its claims, providing a comprehensive overview of the current state of research and expert opinion on this topic with tables, graphics and charts. By addressing the unique needs of older learners and preparing for the future of GenAI, we can foster digital inclusion and empower all members of society to benefit from this transformative technology. This paper also examines the impact of Generative AI (GenAI) and Agentic AI on the financial services sector, with a specific focus on workforce training and upskilling. Key findings from literature indicate that by 2027, 80% of the engineering workforce will require AI-related upskilling (Gartner) and AI-driven automation can reduce manual data tasks by up to 80% (West Monroe). For example, in banking, AI adoption has led to tangible productivity gains, such as Capitec Bank employees saving over one hour per week using AI tools (as suggested by recent reports). The paper categorizes and quantifies recent AI adoption trends, workforce transformation data, and financial efficiency metrics to provide a comprehensive condensed overview of the evolving AI landscape in financial services based on recent reports.

This paper presents a study that examines tabletop roleplaying game (TTRPG) players' understanding, attitudes, and perceptions of generative AI (GAI) as it intersects with this creative, hobbyist domain. While general findings regarding challenges and potential harms of GAI are well-explored, much of this exploration centers workplaces concerned with productivity and efficiency. This study values the contrast provided by TTRPG hobbyists in the relatively underexplored domain of a non-professional, creative community that tends to lack corporate pressures. Through a qualitative study using ethnographic interviews and a shared image-generation activity, we explore how TTRPG hobbyists are responding to GAI systems in this environment and describe our participants' mental model of GAI ecosystems. We found that they tend to treat GAI as its own creative medium, using vocabulary attentive to distinct design affordances of GAI systems rather than seeing it simply as a “tool”. Further, we develop the term "creative ethos" to describe how research participants evaluate and navigate such affordances to align with ethical values of creativity fostered by TTRPGs, especially focusing on participants’ aspirations to support artists and resist unethical GAI futures.

Inclusive by design: Why we must rethink generative AI in dermatology.

The article is devoted to the history of the development of ICT and AI, their current and expected future achievements, and the problems (which have already arisen but will become even more acute in the future) associated with the development of these technologies and their widespread application in society. It shows the close connection between the development of AI and cognitive science, the penetration of ICT and AI into various spheres, particularly health care, and the very intimate areas related to the creation of digital copies of the deceased and posthumous contact with them. A significant part of the article is devoted to the analysis of the concept of ‘artificial intelligence’, including the definition of generative AI. The authors analyse recent achievements in the field of Artificial Intelligence. There are given descriptions of the basic models, in particular the Large Linguistic Models (LLM), and forecasts of the development of AI and the dangers that await us in the coming decades. The authors identify the forces behind the aspiration to create AI, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. It is emphasized that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, major corporations and those often referred to as globalists. The article provides forecasts of the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, published in the previous is-sue of the journal, has provided a brief historical overview and characterized the current situation in the field of ICT and AI. It has also analyzed the concepts of artificial intelligence, including generative AI, changes in the understanding of AI related to the emergence of the so-called large language models and related new types of AI programs (ChatGPT and similar models). The article has discussed the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. This second article describes and comments on the current assessments of breakthroughs in the field of AI, analyzes various predictions, and provides the authors' own assessments and predictions of future developments. Particular attention is paid to the problems and dangers associated with the rapid and uncontrolled development of AI, with the fact that advances in this field become a powerful means of control over the population, imposing ideologies, priorities and lifestyles, influencing the results of elections, and a tool to undermine security and geopolitical struggles.

The article is devoted to the history of the development of ICT and AI, their current and expected future achievements, and the problems (which have already arisen but will become even more acute in the future) assiciated with the development of these technologies and their widespread application in society. It shows the close connection between the development of AI and cognitive science, the penetration of ICT and AI into various spheres, particularly health care, and the very intimate areas related to the creation of digital copies of the deceased and posthumous contact with them. A significant part of the article is devoted to the analysis of the concept of “artificial intelligence”, including the definition of generative AI. The authors analyse recent achievements in the field of Artificial Intelligence. There are given descriptions of the basic models, in particular the Large Linguistic Models (LLM), and forecasts of the development of AI and the dangers that await us in the coming decades. The authors identify the forces behind the aspiration to create AI, which is increasingly approaching the capabilities of the so-called general/universal AI, and also suggest desirable measures to limit and channel the development of artificial intelligence. It is emphasized that the threats and dangers of the development of ICT and AI are particularly aggravated by the monopolization of their development by the state, intelligence services, major corporations and those often referred to as globalists. The article provides forecasts of the development of computers, ICT and AI in the coming decades, and also shows the changes in society that will be associated with them. The study consists of two articles. The first, published in the previous issue of the journal, provided a brief historical overview and characterized the current situation in the field of ICT and AI. It also analyzed the concepts of artificial intelligence, including generative AI, changes in the understanding of AI in connection with the emergence of the so-called large language models and related new types of AI programs (ChatGPT and similar models). The article discussed the serious problems and dangers associated with the rapid and uncontrolled development of artificial intelligence. This second article describes and comments on current assessments of breakthroughs in the field of AI, analyzes various predictions, and provides the authors’ own assessments and predictions of future developments. Particular attention is paid to the problems and dangers associated with the rapid and uncontrolled development of AI, with the fact that advances in this field are becoming a powerful means of control over the population, imposing ideology, priorities and lifestyles, influencing the results of elections, and a tool to undermine security and geopolitical struggles.

Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

This assignment is integrated into the generative AI unit of the Emerging Communication Technologies course. It includes step-by-step designs and reflective examples from students, highlighting the evolution of their perceptions of generative AI. The assignment uniquely focuses on understanding and raising awareness of stereotypes present in AI-generated images. Through experiential, analytical, and reflective learning, students build confidence and competence in interacting with various AI tools, acquire skills in AI–human communication and prompting, and develop critical thinking abilities to identify and mitigate stereotypes generated by AI. Courses Emerging Communication Technologies; Human–Computer Interaction; Communication and Technology; New Media; Digital Humanity; Digital Literacy; Media Literacy. Objectives Through these activities, students will (1) reduce fear or discomfort about interacting with generative AI, (2) familiarize themselves with popular generative AI tools, (3) understand characteristics of human–AI interaction, (4) recognize AI fallibility in producing stereotypes, and (5) think critically about identifying and preventing AI-generated stereotypes.

Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences. The convergence of these technologies fosters hybrid intelligence, where AI amplifies human potential rather than replacing it. This research explores AI-augmented cognition, quantum-enhanced simulations, and AI-driven spatial computing, addressing ethical, security, and societal implications of human-machine synergy. By integrating decentralized AI governance, privacy-preserving AI techniques, and brain-computer interfaces, this study outlines a scalable framework for next-generation augmented intelligence applications in healthcare, enterprise intelligence, scientific discovery, and immersive learning. The future of AHI lies in hybrid intelligence systems that co-evolve with human cognition, ensuring responsible and transparent AI augmentation to unlock new frontiers in human potential. Keywords: Augmented Human Intelligence, Generative AI, Quantum Computing, Extended Reality, XR, AI-driven Cognition, Hybrid Intelligence, Brain-Computer Interfaces, AI Ethics, AI-enhanced Learning, Spatial Computing, Quantum AI, Immersive AI, Human-AI Collaboration, Ethical AI Frameworks.

Purpose This study examines how three instructional conditions – Conventional Tutorial, Flipped Classroom Design Thinking (FCDT), and an AI-supported FCDT-AI model using ChatGPT – shape undergraduate students' Digital Literacy within an open and distance learning (ODL) environment at Universitas Terbuka, Indonesia. It responds to the growing need for scalable pedagogical models that integrate flipped learning, design thinking, and generative AI across Asian open universities. Design/methodology/approach A within-subjects repeated-measures design was employed with 26 undergraduate students enrolled in an Academic Writing Techniques course. All participants experienced the three conditions in counterbalanced order via TUWEB, the institutional learning management system. Digital Literacy was measured after each condition using a multidimensional performance-based questionnaire. Quantitative analysis used Huynh–Feldt-adjusted repeated-measures ANOVA with Holm-adjusted post-hoc tests, while qualitative reflection logs were examined using reflexive thematic analysis to elucidate mechanisms underlying observed differences. Findings A significant and substantial main effect of instructional condition was identified, demonstrating a clear performance gradient: Conventional < FCDT < FCDT-AI. The AI-supported condition yielded the highest Digital Literacy scores and the broadest distribution of advanced practices. Qualitative themes further revealed progressive development from basic access and retrieval (Conventional), to structured evaluation and emerging digital production (FCDT), and to multimodal, reflective, and AI-mediated digital engagement (FCDT-AI). Research limitations/implications This study has several limitations. The small sample from a single programme at one ODL institution restricts generalisability, suggesting the need for replication across disciplines, universities, and learner profiles. The reliance on self-reported reflections may introduce subjectivity; integrating learning analytics or artefact analysis would strengthen triangulation. The AI scaffolding was intentionally limited for ethical reasons, meaning future studies could examine varying intensities or types of AI support. Despite these constraints, the findings offer empirically grounded implications for designing scalable, AI-supported flipped learning models in ODL environments. Practical implications The findings provide actionable guidance for ODL institutions seeking to strengthen Digital Literacy at scale. Tutors should integrate structured flipped-learning cycles supported by design thinking to guide learners from basic access toward evaluative and creative digital practices. Incorporating generative AI as guided scaffolding – rather than as an autonomous problem-solver – can expand students' idea generation, support multimodal production, and reduce cognitive load. Curriculum designers can embed FCDT-AI workflows into tutorial manuals, LKMs, and online learning activities to promote consistent digital engagement. Institutions may also develop training programmes to enhance tutors' digital pedagogy and ethical AI facilitation. Social implications Enhancing Digital Literacy through structured flipped and AI-supported models can help narrow digital inequities among geographically dispersed ODL learners. The FCDT-AI framework supports more inclusive participation by providing scaffolding that benefits students with lower digital readiness, thereby promoting equitable access to 21st-century competencies. As generative AI becomes more widespread in education and work, developing students' evaluative, ethical, and creative digital practices contributes to a more informed and responsible digital citizenry. The model also supports lifelong learning, empowering working adults to engage confidently in digitally mediated environments and strengthening broader community digital resilience. Originality/value The study offers one of the first empirically tested pedagogical models that systematically integrates flipped learning, design thinking, and generative AI to strengthen Digital Literacy in ODL environments. It provides a theoretically grounded and scalable framework (FCDT-AI) that can support Asian open universities in implementing ethical and effective AI-enhanced digital learning.