155,427 research outputs found
Generative AI tools in art education: Exploring prompt engineering and iterative processes for enhanced creativity
The rapid development and adoption of generative artificial intelligence (AI) tools in the art and design education landscape have introduced both opportunities and challenges. This timely study addresses the need to effectively integrate these tools into the classroom while considering ethical implications and the importance of prompt engineering. By examining the iterative process of refining original ideas through multiple iterations, verbal expansion, and the use of OpenAI’s DALL E2 for generating diverse visual outcomes, researchers gain insights into the potential benefits and pitfalls of these tools in an educational context. Students in the digital at case study were taught prompt engineering techniques and were tasked with crafting multiple prompts, focusing on refining their ideas over time. Participants demonstrated an increased understanding of the potential and limitations of generative AI tools and how to manipulate subject matter for more effective results. The iterative process encouraged students to explore and experiment with their creative ideas, leading to a deeper understanding of the possibilities offered by AI tools. Despite acknowledging the ethical concerns regarding copyright and the potential replacement of artists, students appreciated the value of generative AI tools for enhancing their sketchbooks and ideation process. Through prompt engineering and iterative processes, students developed a more detail oriented approach to their work. The challenge of using AI generated images as final products was conceptually intriguing, requiring further investigation and consideration of the prompts. This study high-lights the potential benefits and challenges of integrating generative AI tools into art and design classrooms, emphasizing the importance of prompt engineering, iterative processes, and ethical considerations as these technologies continue to evolve
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Graduating product designers and their attitudes to design responsibility: A survey
The notion of responsibility in designing is one that is becoming increasingly important, underlying significant areas of design activity. The research reported in this paper presents the results of a questionnaire study of 50 graduating product designers from 11 UK Universities. We find that students are focussed on environmental issues, have a very clear idea about where their responsibilities end for the products they design, but who display significant differences between the ideas about responsibility they espouse and their practice
An investigation into the perspectives of providers and learners on MOOC accessibility
An effective open eLearning environment should consider the target learner’s abilities, learning goals, where learning takes place, and which specific device(s) the learner uses. MOOC platforms struggle to take these factors into account and typically are not accessible, inhibiting access to environments that are intended to be open to all. A series of research initiatives are described that are intended to benefit MOOC providers in achieving greater accessibility and disabled learners to improve their lifelong learning and re-skilling. In this paper, we first outline the rationale, the research questions, and the methodology. The research approach includes interviews, online surveys and a MOOC accessibility audit; we also include factors such the risk management of the research programme and ethical considerations when conducting research with vulnerable learners. Preliminary results are presented from interviews with providers and experts and from analysis of surveys of learners. Finally, we outline the future research opportunities. This paper is framed within the context of the Doctoral Consortium organised at the TEEM'17 conference
Doing pedagogical research in engineering
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AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing
Recently, many AI researchers and practitioners have embarked on research
visions that involve doing AI for "Good". This is part of a general drive
towards infusing AI research and practice with ethical thinking. One frequent
theme in current ethical guidelines is the requirement that AI be good for all,
or: contribute to the Common Good. But what is the Common Good, and is it
enough to want to be good? Via four lead questions, I will illustrate
challenges and pitfalls when determining, from an AI point of view, what the
Common Good is and how it can be enhanced by AI. The questions are: What is the
problem / What is a problem?, Who defines the problem?, What is the role of
knowledge?, and What are important side effects and dynamics? The illustration
will use an example from the domain of "AI for Social Good", more specifically
"Data Science for Social Good". Even if the importance of these questions may
be known at an abstract level, they do not get asked sufficiently in practice,
as shown by an exploratory study of 99 contributions to recent conferences in
the field. Turning these challenges and pitfalls into a positive
recommendation, as a conclusion I will draw on another characteristic of
computer-science thinking and practice to make these impediments visible and
attenuate them: "attacks" as a method for improving design. This results in the
proposal of ethics pen-testing as a method for helping AI designs to better
contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on
27-10-201
Responsible Autonomy
As intelligent systems are increasingly making decisions that directly affect
society, perhaps the most important upcoming research direction in AI is to
rethink the ethical implications of their actions. Means are needed to
integrate moral, societal and legal values with technological developments in
AI, both during the design process as well as part of the deliberation
algorithms employed by these systems. In this paper, we describe leading ethics
theories and propose alternative ways to ensure ethical behavior by artificial
systems. Given that ethics are dependent on the socio-cultural context and are
often only implicit in deliberation processes, methodologies are needed to
elicit the values held by designers and stakeholders, and to make these
explicit leading to better understanding and trust on artificial autonomous
systems.Comment: IJCAI2017 (International Joint Conference on Artificial Intelligence
A Value-Sensitive Design Approach to Intelligent Agents
This chapter proposed a novel design methodology called Value-Sensitive Design and its potential application to the field of artificial intelligence research and design. It discusses the imperatives in adopting a design philosophy that embeds values into the design of artificial agents at the early stages of AI development. Because of the high risk stakes in the unmitigated design of artificial agents, this chapter proposes that even though VSD may turn out to be a less-than-optimal design methodology, it currently provides a framework that has the potential to embed stakeholder values and incorporate current design methods. The reader should begin to take away the importance of a proactive design approach to intelligent agents
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