Academic Paper: AI adoption in higher education
A qualitative study of how university staff actually experience AI adoption, beyond what AI can technically do.
The need
Almost everyone studies what AI can do technically. Far less captures what daily AI use is actually like for the lecturers, administrators and support staff who have to live with it. I wanted that lived experience.
The challenge
Lived experience is messy and contradictory: people feel AI as help and as threat at the same time. I had to make sense of that without flattening it into a simple 'good' or 'bad' verdict.
What I made
Eight semi-structured interviews across roles and experience levels, coded in three cycles into hundreds of codes and a set of integrated findings. I reframed the 'enabler' and the 'worry' as two sides of one phenomenon, shaped by digital literacy, role and tool overload.
The outcome
Concrete findings: tool complexity flips AI's time-saving into overload, management overestimates how supported staff feel, and people prefer informal peer help over formal helpdesks. This is the root of my AI-adoption throughline, straight into the DTT capstone and my applied-AI work.
Key points
- Eight semi-structured interviews with purposive sampling across roles and generations
- Three cycles of coding into hundreds of codes (thematic analysis)
- Reframed AI's 'enabler' and 'worry' as one phenomenon shaped by context
- Surfaced that staff prefer informal peer support over formal channels