University-level humanities and business education are entering a period of rapid change as generative artificial intelligence tools become ubiquitous in academic work. The chapter focuses on the fundamentals of higher education in these disciplines, treating generative artificial intelligence as both a disruptive force and a potential resource for learning. It frames the discussion by explaining in accessible terms how generative artificial intelligence systems operate, including their dependence on large language models and reinforcement learning from human feedback, and uses this technical grounding to assess what kinds of advances can realistically be expected in the near term.
A central argument is that generative artificial intelligence detection is unlikely to be reliable in the foreseeable future, which makes any enforcement model that depends heavily on automated detection tools pedagogically and ethically fragile. Evidence from recent testing of detection tools for artificial intelligence generated text is used to underscore false positives, false negatives, and the difficulty of watermarking or labeling content in a durable way. At the same time, the persistence of the hallucination problem is treated as a structural feature of current architectures rather than a short-term bug that will soon disappear. The chapter links these technical limitations to core concerns about academic integrity, including plagiarism, contract cheating, blackmail and extortion risks around misconduct, and the broader erosion of trust in student work.
Rather than relying on technological fixes, the chapter positions these challenges as an opportunity to redesign teaching and learning strategies around critical engagement with generative artificial intelligence. In humanities and business classrooms, this includes assignments that require students to interrogate, verify, and critique generative artificial intelligence outputs, foregrounding source evaluation, reasoning, and domain expertise. It recommends assessment designs that reduce incentives for uncritical outsourcing of thinking, such as staged research processes, oral defenses, and reflective components that make students’ decision-making visible. The overall vision is a higher education ecosystem in which generative artificial intelligence is integrated transparently and responsibly, with educators shifting from detection and prohibition toward cultivating students’ capacity to work with fallible automated systems in a way that strengthens, rather than undermines, academic integrity.
