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

Artificial Intelligence (AI) has become prevalent in all sectors of society, including higher education institutions. Many studies have examined iSchools curricula, focusing on areas such as data science, digital humanities, and archival studies. However, few studies have examined AI education at iSchools in the United States (US) and Canada. Research is needed to address AI in information science (IS) education, fueling the conversation about AI across the iSchools' curricula. This study analyzed the AI-related courses in graduate and undergraduate programs offered by members of the iSchools organization in the United States and Canada. We identified the area(s) and facet(s) covered in each course title and coded them. Of the 51 iSchools, twenty-nine offered AI-related courses. The most covered areas include general AI, Machine Learning, Natural Language Processing, Deep Learning, and Robotics. Most courses focus on AI's technical and applied facets, while a few cover the ethical, societal, cultural, and legal facets. Implications include the need for iSchools to offer AI courses that cover aspects beyond the technical, more undergraduate courses, and certificate programs that contribute to educating the labor force that needs upskilling. This study informs the iSchools' curricula strengths to build on,gaps to fill, and IS practice.

The widespread adoption of chatbots and large language models has significantly impacted various aspects of daily life. This study employs mixed methods to analyze ChatGPT logs from 36 undergraduate students, providing a comprehensive examination of how this technology is integrated into academic contexts. ChatGPT had diverse applications with the most prevalent uses centering on essay writing assistance. We identify more dynamic scenarios, such as students utilizing ChatGPT to generate and learn computer code across multiple programming languages. The study explores the evolving parasocial relationship between students and ChatGPT, particularly focusing on conversational repair processes and how these interactions change over time. Building upon previous research in human-chatbot interactions, we offer insights into the nuanced ways students engage with AI-powered language models. These findings inform a set of design recommendations aimed at enhancing future chatbot interactions and contributing to theongoing discourse on the role of AI in education and beyond.

This systematic review explores the potential of generative artificial intelligence in supporting neurodivergent students in higher education. Drawing on 21studies published between 2022 and 2025, the review synthesises current evidence on the challenges faced by neurodiverse learners, the capabilities of generative AI tools, application areas, and implementation barriers. Findings show that generative AI tools can support academic writing, task planning, emotional regulation, and self-management by providing personalised, flexible, and multimodal support. Positive outcomes include improved writing fluency, time management, self-efficacy, and engagement. However, challenges remain regarding personalisation, accessibility, and ethical use. The review identifies emerging conceptual dimensions such as cognitive regulation, emotional support, and identity formation, and calls for stronger theoretical frameworks, inclusive design practices, and sustained institutional support to ensure equitable use of generative AI in higher education.