Description
Starting in mid-2023, ITHAKA began investing in and engaging directly with generative artificial intelligence (AI) in three broad areas: a collaborative research project led by Ithaka S+R; a generative AI research tool on the JSTOR platform; and a proof-of-concept for GenAI-enabled processing of library special collections. These technologies are so crucial to our futures that working directly with them to learn about their impact, both positive and negative, is extremely important. This presentation will share early findings that illustrate the impact and potential of generative AI- powered research based on what JSTOR users are expecting from the tool, how their behavior is changing, and implications for changes in the nature of their work. In addition, we’ll share what we’ve learned from the proof-of-concept work we are doing to address the “backlog” problem that most academic libraries are struggling with in the triage and processing of library special/distinctive/archival collections. The findings will be contextualized with the cross-institutional learning and landscape-level research being conducted by Ithaka S+R. By pairing data on user behavior with insights from faculty and campus leaders, the session will share early signals about how this technology-enabled evolution is taking shape.
Early Findings from ITHAKA's Work in Generative Artificial Intelligence (GenAI)
Starting in mid-2023, ITHAKA began investing in and engaging directly with generative artificial intelligence (AI) in three broad areas: a collaborative research project led by Ithaka S+R; a generative AI research tool on the JSTOR platform; and a proof-of-concept for GenAI-enabled processing of library special collections. These technologies are so crucial to our futures that working directly with them to learn about their impact, both positive and negative, is extremely important. This presentation will share early findings that illustrate the impact and potential of generative AI- powered research based on what JSTOR users are expecting from the tool, how their behavior is changing, and implications for changes in the nature of their work. In addition, we’ll share what we’ve learned from the proof-of-concept work we are doing to address the “backlog” problem that most academic libraries are struggling with in the triage and processing of library special/distinctive/archival collections. The findings will be contextualized with the cross-institutional learning and landscape-level research being conducted by Ithaka S+R. By pairing data on user behavior with insights from faculty and campus leaders, the session will share early signals about how this technology-enabled evolution is taking shape.
