Generative Visual AI in News Organizations: Challenges, Opportunities, Perceptions, and Policies

Thomson, T. J.; Thomas, Ryan J.; Matich, Phoebe

doi:10.1080/21670811.2024.2331769

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Furthermore, AI tools are generally classified based on their conceptual definitions and applications into generative AI (or text-to-image generators) [75,76], such as Midjourney, Stable Diffusion, DALL-E, and conversational AI (or chatbots) [77,78], such as ChatGPT. In this study, AI tools are categorized into general AI (e.g., ChatGPT), design-assistive AI (e.g., Stable Diffusion), and expressive-assistive AI (e.g., Midjourney and DALL-E) according to their application scenarios in architectural creation.…”

Section: Ai Mainly Focuses On Point Interventions and Has Not Formed ...mentioning

confidence: 99%

“…Finally, half of the class expressed a strong willingness to continue using AI (50%), while a minority believed they might use AI tools in the future (29.2%), and a very small fraction indicated they might not or would not use AI tools (16.7% and 4.2%). Furthermore, AI tools are generally classified based on their conceptual definitions and applications into generative AI (or text-to-image generators) [75,76], such as Midjourney, Stable Diffusion, DALL-E, and conversational AI (or chatbots) [77,78], such as ChatGPT. In this study, AI tools are categorized into general AI (e.g., ChatGPT), designassistive AI (e.g., Stable Diffusion), and expressive-assistive AI (e.g., Midjourney and illustrated in Figure 9, general AI was generally used during the architectural programming stage, especially in the decision-making phase.…”

Section: Ai Mainly Focuses On Point Interventions and Has Not Formed ...mentioning

confidence: 99%

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Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course

Jin,

Tu,

et al. 2024

Buildings

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This study addresses the current lack of research on the effectiveness assessment of Artificial Intelligence (AI) technology in architectural education. Our aim is to evaluate the impact of AI-assisted architectural teaching on student learning. To achieve this, we developed an AI-embedded teaching model. A total of 24 students from different countries participated in this 9-week course, completing a comprehensive analysis of architectural programming and design using AI technologies. This study conducted questionnaire surveys with students at both midterm and final stages of the course, followed by structured interviews after the course completion, to explore the effectiveness and application status of the teaching model. The results indicate that the AI-embedded teaching model positively and effectively influenced student learning. The “innovative capability” and “work efficiency” of AI technologies were identified as key factors affecting the effectiveness of the teaching model. Furthermore, the study revealed a close integration of AI technologies with architectural programming but identified challenges in the uncontrollable expression of architectural design outcomes. Student utilization of AI technologies appeared fragmented, lacking a systematic approach. Lastly, the study provides targeted optimization suggestions based on the current application status of AI technologies among students. This research offers theoretical and practical support for the further integration of AI technologies in architectural education.

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Section: Ai Mainly Focuses On Point Interventions and Has Not Formed ...mentioning

confidence: 99%

Section: Ai Mainly Focuses On Point Interventions and Has Not Formed ...mentioning

confidence: 99%

Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course

Jin,

Tu,

et al. 2024

Buildings

View full text Add to dashboard Cite

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Appealing, but misleading: a warning against a naive AI realism

Engel-Hermann,

Skulmowski

2024

AI Ethics

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Scientists, educators, and instructional designers are facing numerous challenges due to the introduction of generative AI tools that can create appealing realistic imagery based on text prompts. Given that realism contributes to the trustworthiness of images coupled with people’s eagerness to externalize complex tasks to AI systems, the problem of a naive AI realism arises in which image creation and optimization is offloaded without considering the limitations of AI-driven technology. However, scientific visualizations and images used for educational purposes must go beyond an appealing presentation; above all, they should be accurate and factually correct. We argue that the utilization of generative AI tools for these types of visualizations requires human oversight, subject matter knowledge, and knowledge of effective design. In particular, we warn of a naive adoption of technological possibilities to “optimize” visualizations for educational purposes, such as memorability. A reductionist and naive view of AI-based optimization that fails to take into account the complex prerequisites for learning and instruction is likely to have negative consequences.

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A Shift Amid the Transition: Towards Smarter, More Resilient Digital Journalism in the Age of AI and Disinformation

Forja-Pena,

García-Orosa,

López-García

2024

Social Sciences

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Digital journalism is facing a growing number of challenges as the wave of digital transformation enters a new phase. The changes, both slow and sudden, are making it increasingly necessary for there to be a shift amid the transition. The surge of artificial intelligence (AI) in the journalistic world has ushered in what appears to be a new phase for digitalisation and journalism’s role as a producer of factual information and knowledge in society. Current debates suggest that we are at a critical time to reinvent journalism for the new wave of digital transformation. In this stage, which recently began and which some are calling the Fourth Industrial Revolution, both professionals and academics point to the need to make journalism more resilient, so it can adapt to the changes without losing its basic essence, and more people-centred, helping to support individuals in the age of AI. This article analyses the challenges from the perspectives of both academics and professional journalists, based on their codes of ethics. The methodology used is a content analysis of 45 European ethical codes and the Delphi method, involving experts from the journalistic sector and academia. The results highlight that codes of ethics have not kept pace with these professionally and expert-led debates, as few mention new technologies such as artificial intelligence and they tend to focus more on disinformation and social engagement.

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Generative Visual AI in News Organizations: Challenges, Opportunities, Perceptions, and Policies

Cited by 3 publications

References 34 publications

Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course

Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course

Appealing, but misleading: a warning against a naive AI realism

A Shift Amid the Transition: Towards Smarter, More Resilient Digital Journalism in the Age of AI and Disinformation

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