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Factors Involved On The Attribution Of Consciousness To Generative Artificial Intelligence And Its Impact On Trust
Get PDFThis study investigates the cognitive factors influencing the perception of mind in generative artificial intelligence systems, focusing on the role of prior experiences with them, theory of mind (ToM), and the tendency to anthropomorphize. The research examined how these factors predict perceived consciousness in ChatGPT and impact users\u27 trust in this system, specifically exploring: (1) how ToM and anthropomorphism contribute to AI consciousness perception, (2) how prior AI experiences influence consciousness attribution, (3) the relationship between consciousness attribution and trust in AI, and (4) how perceptions of AI consciousness relate to mind perception in other entities. Undergraduates (N= 220) completed a survey including measures of ToM, anthropomorphism tendencies, mind perception in other entities, and prior experiences with generative LLMs. Results revealed a strong positive relationship between anthropomorphic tendencies and perceived AI consciousness, while ToM showed no significant association. Surprisingly, frequency of AI use negatively correlated with consciousness attribution. Trust in AI demonstrated a weak but significant positive relationship with perceived consciousness. Additionally, perceived AI consciousness strongly correlated with attributed agency and experience in robots. This study contributes to a deeper understanding of human-AI interaction by analyzing user-specific factors in human-AI interaction, offering insights into the design of AI systems that align with user expectations while mitigating risks associated with trust and over-reliance on AI. The findings have important implications for improving transparency, fostering ethical AI usage, and addressing the potential consequences of AI\u27s impactful role in society
Parisians\u27 Perceptions of the 2024 Olympics: A Mixed-Methods Analysis of Local Attitudes Towards Housing Epidemics Using Social Exchange Theory, Place Attachment, and Media Framing Theory
Get PDFThe Paris 2024 Olympics introduced significant socio-economic transformations to the city, particularly in the areas of housing policy, urban development, and community displacement. This study examines Paris residents\u27 perceptions of these changes using a mixed-methods approach that integrates sentiment analysis and media framing analysis. The research is guided by Social Exchange Theory (SET), Place Attachment Theory, and Media Framing Theory to understand the relationship between public sentiment, urban transformation, and media narratives. Findings indicate that the perceived effects of the Paris 2024 Olympics on its host city were overwhelmingly negative, with consistent concerns centered on housing insecurity, displacement, and a perceived lack of reciprocal benefit for local residents. The study underscores a disparity between media/government narratives and public apprehensions, with official discourse prioritizing economic expansion, whilst citizens predominantly addressed displacement and various other housing epidemics. These insights provide critical recommendations for Los Angeles 2028 on mitigating community backlash and fostering inclusive policies
Free Pre-Conference: Primer for Generative AI and Higher Education
No full textIf you’re attending this conference to learn about the use of AI in teaching but don’t yet have a strong knowledge base of what generative AI is and what it can do, start here. After an initial introduction, we will explore the opportunities and challenges AI creates on campuses, and suggest a nuanced approach going forward. We can\u27t hide from AI, but if we teach (and use) it thoughtfully, we can preserve and enhance student learning
AI Unlocked: Transforming Teaching with Artificial Intelligence
No full textThis session introduces educators to the core principles of generative AI prompt engineering, showcasing its broad applications within higher education. Through a community-driven approach, participants will discuss and engage in a hands-on session that emphasizes creating effective AI prompts to meet diverse academic needs. Participants will learn strategies for integrating AI into innovative assignments and engagement techniques that enhance teaching and learning within their field. This session empowers educators to navigate and apply generative AI\u27s transformative potential in education
The Field Guide to Effective AI Use: A Springboard for Ethical AI Discussions
No full textWe cannot define ethical AI use without sharing how we are using the systems and engaging in discussion. That\u27s the mission of The Field Guide to Effective AI Use, a research exhibit published in the WAC Repository (Writing Across the Curriculum). In it, six educators share transcripts of their own AI use - unfiltered - and annotate themselves as if the user on the page is someone else. Each educator walks the reader through their approach, their reactions to the AI outputs, and their subsequent iterative prompts. We can only define ethical and effective AI use through observation. That\u27s why we made The Field Guide
From Skeptic to Advocate: Leveraging Generative AI for Deep Reading
No full textStudent academic success is rooted in students\u27 ability to read and understand scholarly content. This session will showcase early adopter\u27s ongoing success following adoption of Alethea. Discover how Academic AI can tailor reading assignments to different levels of student understanding and provide insights into reading skills, enabling refined teaching methods and learning resources. One recent university survey showed that with Alethea 85% of students better grasped key themes and ideas in their readings, while 80% were more motivated to engage with course materials. The presenter will share insights that helped enhance efficiency and instructional quality while adhering to core teaching methodologies
Build Your Own Generative AI Syllabus Statement
No full textThis interactive presentation will take participants through an activity in which they build a statement to include in their syllabus that ensures students have a clear understanding of the parameters for Generative AI use specific to the course. Multiple examples will be provided from faculty statements across the disciplines. Three main takeaways from this session include the following: 1) how to communicate the reasons behind establishing course parameters; 2) how to host a conversation with students about the syllabus statement; and 3) how to identify questions that students are likely to pose
Advancing Clinical Education in Speech-Language Pathology through AI
No full textWe will explore the potential role of artificial intelligence (AI) to enhance clinical education in speech-language pathology. We will discuss ethical considerations and development of interactive learning experiences specifically for clinical education using AI-enhanced assignments. Attendees will learn to ensure patient confidentiality and responsible decision-making in clinical settings. We emphasize the importance of collaboration among educators to share best practices and innovative applications of AI in education. By integrating AI into clinical education, we can advance teaching and learning in speech-language pathology, ultimately preparing students to meet the challenges of a rapidly evolving healthcare landscape while improving patient care outcomes
Consequences vs. Compassion: Turning A.I. abuse into a learning opportunity
No full textWith cases of A.I. related misconduct on the rise, faculty are increasingly being called upon to make decisions that can lead to serious consequences for students. Does mercy have a place in this process? Where do we draw the line between academic integrity and grace when students engage in A.I. misconduct? In this session we will discuss strategies to turn student A.I. mistakes into learning opportunities with the potential to nudge student behavior into more ethical and productive directions
AI-Generated Quizzes: Enhancing Student Engagement and Critical Thinking
No full textThis session presents an exploratory study of the use of generative AI to create quizzes that prepare first year students for group discussions. This project explores how AI-generated quizzes can be utilized to enhance student engagement, stimulate critical thinking, and foster collaborative learning. A key focus is on how these quizzes help students identify their own strengths and weaknesses in relation to a given agenda in a lecture, thereby better preparing them for meaningful participation in e.g. group discussions. The study examines the process of creating and integrating AI-generated quizzes, the types of questions generated, and the feedback mechanisms used to help students reflect on their learning. This study explores the impact of these quizzes on student preparedness and how designs for learning transfer can inform the use of AI generated quizzes