What happens when AI begins to perform work that universities have historically employed people to do? At that point, AI ceases to be merely an educational technology and becomes a labour technology.
Once AI enters the employment relationship, questions arise that an information technology office alone cannot resolve. Who decides whether AI may perform work previously assigned to faculty? Can AI change staffing requirements? Who owns academic materials used to develop AI systems? Can algorithmic analysis influence faculty evaluations? If AI makes academic work faster, who benefits from the time saved? If supervising AI creates new work, where does that labour appear in workload calculations? These questions are no longer hypothetical. They are increasingly appearing in collective agreements, memoranda of understanding and union policies.
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