Artificial intelligence has exposed structural flaws in higher education, according to one scholar.<\/p>\n
Hollis Robbins, a professor of English and special adviser for humanities at the University of Utah<\/a>, spoke about AI\u2019s effects on higher education to about 50 professors in a lecture on Wednesday evening sponsored by the E3 project<\/a>. From the Teplitz Memorial Courtroom in Pitt\u2019s School of Law<\/a>, Robbins said AI is exposing more problems than it\u2019s causing in the structure of higher education.<\/p>\n Universities across the country, including Pitt, are offering free AI services<\/a> and partnering with AI companies<\/a>. Pitt recently partnered with Anthropic<\/a> to provide Claude for Education<\/a> to all students and faculty. AI\u2019s integration in universities and its high usage among students<\/a> have sparked a reconsideration<\/a> of the purpose of higher education.<\/p>\n Through an analysis of different forms of information, Robbins discussed AI versus humans\u2019 capability to produce knowledge, detailed what she believes to be the highly scaled<\/a> structure of higher education, and emphasized the resulting increasing importance of specialized, expert knowledge. Robbins argued that in an effort to make their course content as transferable across institutions as possible, universities are delivering the same information that generative AI does.<\/p>\n Robbins based her lecture on the Rumsfeld Matrix<\/a> \u2014 a framework dividing information into four categories, including \u201cknown knowns,\u201d \u201cknown unknowns,\u201d \u201cunknown knowns\u201d and \u201cunknown unknowns.\u201d Robbins said she asked Google Gemini<\/a> to generate an education-oriented Rumsfeld Matrix. Gemini described the \u201cknown known\u201d category as \u201cestablished knowledge the institution delivers.\u201d In the context of higher education, Robbins said she believes this includes general education classes; survey classes; and memorizable facts, dates and vocabularies.\u00a0<\/p>\n \u201cThis is where almost every dollar in American higher education goes,\u201d Robbins said.<\/p>\n \u201cIt scales. Any competent instructor can deliver something from the same syllabus in some general education classes.\u201d<\/p>\n The problem, Robbins believes, is that while universities pour resources into teaching \u201cknown known\u201d information, artificial intelligence also specializes in this type of information.\u00a0<\/p>\n \u201cWe\u2019re in the AI era, a period \u2026 [of] \u2018known known\u2019 abundance. Because of AI, everything is everywhere, so the question is, \u2018Should we be teaching it when it\u2019s everywhere?\u2019\u201d Robbins said.<\/p>\n As a representation of her perceived \u201cknown known abundance,\u201d Robbins had ChatGPT<\/a> regenerate Trump\u2019s 1776 Commission Report<\/a> from 2021. Robbins found the product to be quite accurate to the original, because, she said, the original report did not \u201cdemonstrate familiarity with any primary source<\/a>\u201c, reference any archives, and or include archival scholars in its authorship.<\/p>\n \u201cYou can feed [the report] back into a pro model, and it will basically give you some version of the President\u2019s Advisory Report. Why? Because the entire thing is a \u2018known known\u2019 document,\u201d Robbins said. \u201cA machine can produce it because a machine is what it sounds like.\u201d<\/p>\n \u201cUnknown unknowns\u201d constitute the fourth quadrant of the matrix, which Robbins said she believes universities should focus more on. Robbins added that in higher education, this category could include \u201cdocuments in an archive that nobody has cataloged [or] expertise in a retiring professor\u2019s head that\u2019s never been transmitted.\u201d\u00a0<\/p>\n \u201cThe unknown unknowns, I think, are also really important for education \u2014 [the] buried assets,\u201d Robbins said.<\/p>\n Robbins said she believes universities are currently focused on delivering the same course to the largest possible number of students at the lowest possible cost. As a result of scaling, Robbins said, instructors, students and assessments are interchangeable across universities, making expertise more rare.<\/p>\n \u201cExpertise is the enemy of scale, because an expert is not interchangeable,\u201d Robbins said.<\/p>\n According to Robbins, 12 states mandated statewide college credit transfer in 1991, compared to at least 44 today. Dubbing it the \u201ctransfer industrial complex,\u201d Robbins said this phenomenon has left universities in a situation where large language models<\/a> can deliver most course content.<\/p>\n \u201cA course designed to transfer is a course whose content an LLM can deliver,\u201d Robbin said. \u201cAI didn\u2019t cause this, but AI is revealing it. AI can do quadrant one better than the university can.\u201d<\/p>\n The solution to this, Robbins explained, is finding the \u201clast mile\u201d of AI \u2014 what it cannot generate, leaving the student to conduct individual research. Robbins said the universities that succeed will maintain archives, teach languages and \u201cfund the slow, cumulative, painstaking labor that produces knowledge AI can summarize later, but cannot generate on its own.\u201d<\/p>\n \u201cUniversities that organize themselves around the last mile and around the unknown quadrants will survive,\u201d Robbins said. \u201cUniversities that stay in quadrant one are competing with a machine, and the machine is faster.\u201d<\/p>\n In an interview with Michael Madison, professor of law and an organizer of the E3 project, said the effect of Pitt offering Claude for Education largely depends on how individual teachers and departments implement it, but that it also poses some risk for learning, specifically in general education classes.\u00a0<\/p>\n \u201cI do think there is a risk that making Claude or other AI available will increase the disaffection of some students for course material,\u201d Madison said. \u201cThe challenge in the gen ed world is how you make the gen ed course content and the course teaching interesting and attractive and valuable to students. That\u2019s a basic problem.\u201d<\/p>\n In an interview with The Pitt News, Robbins elaborated on what she believes are the flaws in higher education that are being exposed by AI.<\/p>\n \u201cWorking with your hands has been pushed aside in most universities because it doesn\u2019t scale well. You can\u2019t take a large online asynchronous class that involves working with you like an apprentice,\u201d Robbins said. \u201cIt\u2019s the online asynchronous course model that has actually caused the problem, not the AI.\u201d<\/p>\n