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Gordon Hull

Publications and source records attributed to Gordon Hull.

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Comprehensive AI Literacy: The Case for Centering Human Agency

The rapid assimilation of Artificial Intelligence technologies into various facets of society has created a significant educational imperative that current frameworks are failing to effectively address. We are witnessing the rise of a dangerous literacy gap, where a focus on the functional, operational skills of using AI tools is eclipsing the development of critical and ethical reasoning about them. This position paper argues for a systemic shift toward comprehensive AI literacy that centers human agency - the empowered capacity for intentional, critical, and responsible choice. This principle applies to all stakeholders in the educational ecosystem: it is the student's agency to question, create with, or consciously decide not to use AI based on the task; it is the teacher's agency to design learning experiences that align with instructional values, rather than ceding pedagogical control to a tool. True literacy involves teaching about agency itself, framing technology not as an inevitability to be adopted, but as a choice to be made. This requires a deep commitment to critical thinking and a robust understanding of epistemology. Through the AI Literacy, Fluency, and Competency frameworks described in this paper, educators and students will become agents in their own human-centric approaches to AI, providing necessary pathways to clearly articulate the intentions informing decisions and attitudes toward AI and the impact of these decisions on academic work, career, and society.

cs.AI

Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approach

Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the community's general public safety concerns, their view of existing surveillance technologies, and their perception of AI-driven solutions for enhancing safety in urban environments, focusing on Charlotte, NC. Through a survey approach, including in-person surveys conducted in August and September 2023 with 410 participants, this research investigates demographic factors such as age, gender, ethnicity, and educational level to gain insights into public perception and concerns toward public safety and possible solutions. Based on the type of dependent variables, we utilized different statistical and significance analyses, such as logit regression and ordinal logistic regression, to explore the effects of demographic factors on the various dependent variables. Our results reveal demographic differences in public safety concerns. Younger females tend to feel less secure yet trust existing video surveillance systems, whereas older, educated individuals are more concerned about violent crimes in malls. Additionally, attitudes towards AI-driven surveillance differ: older Black individuals demonstrate support for it despite having concerns about data privacy, while educated females show a tendency towards skepticism.

cs.CY