Conference Agenda (All times are shown in Eastern Daylight Time)

Our goal is to advance our empirical understanding of the direct engagement of knowledge workers in academia with generative AI (Gen AI), as they are the thought leaders in our society. We interviewed 17 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Knowledge workers expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI’s potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal access to such technologies. Our study offers insights into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.

Creative work is economically and socially important, information demonstrably plays a crucial role in creativity. Creative blocks are emotionally and professionally difficult for those engaged in creative work, and disruptive of the work itself. Despite the importance of resolving blocks in creative work and the well-known role information plays in supporting creativity, little research has examined the role of information in addressing (or causing) creative blocks. In this paper, we present a dedicated empirical examination of this role of information in creative blocks—to our knowledge the first of its kind. Our results show that information interaction and behaviour are key to resolving creative blocks. Traditional information behaviours, notably browsing, are key, but these are supported by a range of digital interactions, including scrolling, algorithmic curation, and easy digital curation of multimedia. Our paper cements the importance of both information behaviour and digital information interactions to creative unblocking.

While there is increased interest in studying the information behavior of individuals engaged in creative pursuits, the body of research on the topic is still small in comparison to the volume of studies concerning the information behavior of scientists, social scientists, and humanists. Two possible and interrelated explanations for this are 1) non-artists’ misunderstanding artists’ need for information as a support and inspiration for their work; and 2) the difficulty inherent in studying the creative process, which is highly personal to the artist and often not visible to others. This short paper describes a model for data collection designed to mitigate these challenges; by shifting the preliminary data collection to the artists themselves, diary studies mitigate the challenges presented by attempting to study artists’ information behavior in the internal and non-linear processes of creation. The author designed a study to capture 21 poets’ impressions of the role of information in both inspiring and supporting their poetry writing. This paper describes the project design and execution, data collection, and next steps in the author’s data analysis process.

The growing use of artificial intelligence (AI) in scientific research has raised concerns about “AI dependency”, a phenomenon that remains conceptually ambiguous and underexplored. Guided by self-regulation theory, this study proposes a four-quadrant typology of AI dependency based on goal orientation and self-efficacy. Semi-structured interviews with 20 researchers revealed four distinct patterns: collaborative active, instrumental active, passive compensatory, and passive pathway. Researchers with high goal value and high self-efficacy (collaborative active) treat AI as a knowledge collaborator while maintaining autonomy. Those with high self-efficacy but low goal value (instrumental active) prioritize efficiency and treated AI as a pragmatic tool. In contrast, those with high goals but low self-efficacy (passive compensatory) relied on AI to compensate for skill gaps, while individuals low in both dimensions (passive pathway) exhibited habitual dependence and emotional distress when AI was unavailable. These findings reveal the complex psychological and behavioral dynamics underlying AI dependency, offering a more nuanced conceptual understanding and informing interventions that promote critical, self-regulated AI use.