OpenAI’s CEO, Sam Altman, has been vocal about the concept of “artificial general intelligence,” which he believes could either match or exceed human capabilities. He has previously stated that children born today, including his own, will likely have “raw IQ” scores that never surpass those of AI.
In parallel, former President Donald Trump has recently opined on public perceptions of “high IQ,” rousing discussions around intelligence.
As someone deeply engaged in the history of Canadian higher education and its connection to discourses of disability and race, I find the term "intelligence," especially when framed as “IQ,” to be problematic. The idea of intelligence carries weighty implications for the future, while being rooted in troubling historical narratives.
To grasp what AI might replace, we must first scrutinize the concept of intelligence as it has evolved through history — recognizing its potential dangers as opposed to viewing it simply as a biological fact.
The implications of this reflection are significant for universities, which are now facing challenges in aligning their responses to AI with their foundational missions.
In the early 20th century, universities and academic careers were frequently constructed around scientifically-tinged definitions and assessments of “intelligence” and “IQ.” Peter Sandiford, head of the University of Toronto’s educational research department, conducted tests on the intelligence of Chinese and Japanese Canadian students in British Columbia during the 1920s, under the supervision of George Moir Weir, the first full professor of education at the University of British Columbia. As historian Gerald Thompson points out, these studies were intended to validate longstanding racist views that placed Anglo-white individuals above other racial groups.
Such research is inextricably linked to the eugenics movement, which propagated the idea that intelligence was a heritable trait that could be quantified but not easily cultivated. It facilitated a pseudo-scientific racial hierarchy that shaped many aspects of society.
The racial and educational legacies associated with eugenics were reflected in the treatment and education of marginalized populations. For example, in the 1930s, Kenneth E. Kidd, later a pioneer of the “Indian-Eskimo Studies” program at Trent University, administered IQ tests at the Mohawk Institute, a residential school in southern Ontario. His findings were published in two books that perpetuated racist ideas about intelligence and influenced policies from the Department of Indian Affairs, affecting not only the students from the Mohawk Institute but also those in residential schools nationwide.
This lineage of research contributed significantly to the establishment of university programs and disciplines. Consequently, universities gained the authority to serve as arbiters of intelligence, coinciding with a growing reliance on standardized testing and academic rankings.
Today, concerns over AI-related cheating have prompted educators to reconsider their assessment methods, often reverting to handwritten exams, which raises questions about inclusivity and ableism. Historically, intelligence assessments have been misused to disenfranchise certain groups. As we look ahead, the challenge becomes understanding the future implications of how intelligence will be utilized and potentially misapplied.
The histories entwined with intelligence inform today’s conversations about artificial intelligence, making it crucial to consider their effects on modern teaching and learning paradigms. Institutions like the Rutgers AI Lab are starting to delve into the connections between AI and eugenics, warning that technology can perpetuate narrow definitions of intelligence, health, and societal value through biased data and algorithms.
Moreover, the phenomenon of “de-skilling” resulting from AI's integration into education has been widely scrutinized, with critics describing it as a form of “enfeeblement.” This term draws on historically laden eugenic language, highlighting past pseudoscientific notions of “feeble-mindedness.”
Disability studies scholar Rua Williams argues that our inability to recognize these historical contexts may lead current AI developments to replicate projects that dehumanize individuals based on questionable standards of intelligence.
There is also a growing apprehension regarding the unregulated use of AI amidst rising authoritarianism.
We are frequently told that technology is surpassing human intelligence and that we can’t teach our youth to match it, yet we are expected to place our trust in figures like Altman to oversee the training and regulation of these AI systems.
This raises the question: What is the role of education in this landscape?
Reflecting on the history of IQ testing urges educational institutions to reassess the value placed on IQ itself. Originally an educational tool, IQ testing morphed into a means of segregating populations based on flawed science.
Educators must confront how much our methods of efficiency and credibility have hinged on a system of testing and ranking that attempts to quantify intelligence. Ironically, this obsession with educational standardization has made teaching ripe for automation, potentially leading to grade inflation and undermining the value of our roles.
So, what can educators do? The focus must shift to fostering collaboration, critical thinking, and connection among students, emphasizing relationship-building over high-stakes assessments.
The concept of “intelligence” has historically carried ableist and eugenic undertones, shaping how it has been measured and valued. In contemporary education, we have the opportunity to create an approach that is more dynamic and human-centric. Innovative classroom methodologies can inspire fellow educators to reject transactional ways of teaching.
Ultimately, we must continue to ponder the question: “What is intelligence?” and strive to redefine it in ways that diverge from past interpretations entrenched in higher education.
