Universities Urged to Rethink AI Literacy Beyond Training Programs
Universities Urged to Rethink AI Literacy Beyond Training Programs
A new analysis by Pavana Kiranmai Chepuri of Woxsen University argues that traditional AI training programs alone are not enough to build truly AI-literate universities, warning that institutions risk falling behind if they fail to redesign systems, policies, and workflows around artificial intelligence.
In the feature article published on May 18, Chepuri said many universities have responded to generative AI through workshops, certifications, and faculty seminars, but these measures often fail to create lasting institutional transformation.
“The strategic question is not ‘how do we train more people?’ but ‘how do we redesign the institution, so AI literacy becomes a natural property of how it operates?’” Chepuri explained.
The article highlights how AI literacy remains unevenly distributed across universities, with departments that adopt AI early gaining advantages in research, teaching, and innovation while others struggle to keep pace.
Chepuri outlined five key strategies universities should adopt to build long-term AI capability across campuses.
The first recommendation is embedding AI literacy directly into institutional infrastructure rather than relying solely on optional training programs. Examples include adding AI-related prompts into curriculum design templates, research proposals, and administrative workflows.
The second strategy calls for distributing AI expertise across departments instead of centralizing it in a single office or team. Universities are encouraged to appoint departmental AI champions who can adapt institutional goals to the needs of specific disciplines.
Another key recommendation involves redesigning incentives by recognizing AI-enhanced teaching and research in promotion systems, awards, and workload models.
The article also emphasized the importance of integrating AI learning into daily work routines. Staff who regularly interact with AI-generated insights, meeting summaries, or advising tools can gradually build practical AI evaluation skills without formal seminars.
Finally, universities are encouraged to acknowledge and formalize existing “shadow AI” use across campuses, where faculty and staff are already experimenting with AI tools informally for research, administration, and content creation.
Chepuri noted that resistance to AI adoption remains a challenge, particularly in disciplines with strict professional or ethical standards. However, the article argues that lightweight governance frameworks and responsible experimentation are more effective than delaying adoption until comprehensive policies are finalized.
The piece also warned that requiring extensive certification before staff can engage with AI may unintentionally slow adoption and discourage experimentation.
According to Chepuri, universities that successfully integrate AI into governance, incentives, and everyday operations will be better positioned for the future as AI literacy becomes a defining measure of institutional strength.
“The goal is not a university full of people who have learned about AI,” Chepuri concluded. “It is a university that has learned to think with it.”
