New Report From Ithaka S+R: Making AI Generative for Higher Education: Adoption and Challenges Among Instructors and Researchers

The report linked below was published today by Ithaka S+R.

Title

Making AI Generative for Higher Education: Adoption and Challenges Among Instructors and Researchers

Authors

Claire Baytas
Ithaka S+R

Dylan Ruediger
Ithaka S+R

Source

Ithaka S+R
May 1, 2025

DOI: 10.18665/sr.322677

From the Report

Introduction

Generative artificial intelligence (AI) has been a buzz word across higher education ever since OpenAI announced the commercial release of ChatGPT in November 2022. Two and half years later, determining how generative AI will impact and is already impacting teaching, learning, and research—as well as what types of governance need to be put into place to manage that impact—remain priority issues for stakeholders across the sector.

In the immediate wake of ChatGPT’s release, student academic integrity was top of mind: the difficulty in detecting content generated by artificial intelligence led instructors to question how their previous plagiarism policies for student work could still be enforced.^[1] As time has gone on, the conversations around generative AI have become more nuanced. Stakeholders across higher education have been actively exploring whether and how the technology can enhance teaching, learning, and research. Discussions have also focused on the ethical and societal impacts of the technology, especially the risks related to data security, inaccuracy, and bias.^[2] Meanwhile, major technology companies have continued churning out new versions of large language models, while vendors have introduced new product features. For the higher education market specifically, the landscape of generative AI products is sizable and growing, with tools for researchers, teachers, and students that assist with discovering and understanding information, generating and revising writing, and more.^[3]

Higher education institutions have reacted to varying degrees to the advent of generative AI. Most universities now have AI task forces, provide sample AI policy language for syllabi, and offer workshops on basic AI literacy. Certain campuses have also begun providing generative AI access to their communities: for example, the University of Michigan, Arizona State University, and the California State University system made headlines when they announced partnerships with big tech companies to put LLM-powered tools in the hands of faculty, students, and staff.^[4] Academic publishers have been crafting publication guidelines for AI use, while scholarly societies and other organizations in and around higher education have also assembled task forces and developed support resources.^[5]

Universities recognize the need to coordinate institution-wide support for crucial AI initiatives, such as fostering AI literacy among students, faculty, and staff.

Amidst this flurry of activity, unresolved questions remain when it comes to generative AI’s integration into postsecondary teaching, learning, and research. Universities recognize the need to coordinate institution-wide support for crucial AI initiatives, such as fostering AI literacy among students, faculty, and staff.^[6] However, institutional silos and decentralized decision-making processes make achieving this goal difficult. The financial implications of going all in on AI for academic institutions remains unclear.^[7] Managing student academic integrity policies is still a challenge, and many feel that publisher policies for researchers should be more robust as well.^[8] Scholarly inquiry into best practices for integrating AI into teaching, learning, and research is proliferating, but has inevitably struggled to keep up with the pace at which these new technologies are being put in the hands of the community, meaning that many have been learning on the fly.

In fall 2023, Ithaka S+R launched a collective research project with the objective of studying generative AI’s impact on teaching, learning, and research at the postsecondary level.^[9] Through a collaboration with 19 universities from across the US and Canada, the “Making AI Generative for Higher Education” project has provided an opportunity for co-learning among cohort members, gathering and sharing data about instructors’ and researchers’ practices when it comes to generative AI, and for leveraging design-thinking to envision new forms of AI-related support. The project also led Ithaka S+R to explore the generative AI product landscape and launch its tracker of generative AI products for higher education.^[10]

The present report presents the findings of the interviews conducted by Ithaka S+R and teams from our 19 cohort institutions during the spring of 2024.^[11] These interviews asked faculty, graduate students, and other individuals to reflect on their perceptions of and experiences with generative AI in both teaching and research contexts. While the full interview guide can be found in Appendix C, our study was driven by the following questions: To what degree are instructors and researchers adopting generative AI, and how is this changing their approaches and practices in teaching and research? What challenges are they facing in the aftermath of generative AI’s emergence? What kinds of support have they benefited from, and what kinds of support do they still need?

Key Findings

• Instructors and researchers have widely varied degrees of familiarity with AI, but even those at the lower end of the scale recognize the importance of improving their AI literacy levels.
• Instructors are taking it upon themselves to integrate basic AI skills into student activities but are still determining how generative AI can help them meet course learning objectives and how/if to reimagine those learning objectives.
  • Instructors desire further top-down guidance related to student academic integrity and the formal integration of AI literacy into student general education.
• Most researchers have already experimented with AI, but far fewer have settled on productive ways of integrating the tools for the longer term.
  • Researchers seek further clarity around ethical standards and best practices to ensure research quality and integrity can be maintained.
• Instructors and researchers see a gap in discipline-specific support resources at their institutions and are concerned about having secure, affordable access to generative AI tools. They also demonstrate a need for more education on the generative AI product landscape for higher education.

Direct to Full Text Report

Direct to Full Text Report (55 pages; PDF)

Ed. Note: We would like to thank the authors for acknowledging infoDOCKET. 

Filed under: Associations and Organizations, Companies (Publishers/Vendors), Data Files, Interviews, News, Profiles, Publishing