The rapidly expanding use of ChatGPT and other artificial intelligence tools has fired up a fervent debate in academia. On one side of the debate, professors and teachers are concerned over the future of postsecondary learning and threats to traditional disciplines, especially within the humanities, as headlines warn of “The End of the English Major.”
Nevertheless, AI is here and about a third of teachers, from kindergarten through high school, report using it in the classroom, according to a recent survey. While many of our colleagues in higher education policy, science policy, and university design criticize or dismiss generative AI, we are instead decidedly optimistic it will follow a pattern seen in other technologies that have enhanced educational access and success. We believe that when new technologies are embraced, core aspects of learning, including curriculum, instruction and assessment, can be revolutionized. We are optimistic about AI, but we don’t see it as a hero. Students and instructors are still the heroes of human learning, even when AI is involved.
History supports this view. From the Gutenberg press to online math classes, technologies that improve access to quality learning opportunities are routinely dismissed by critics and skeptics, especially by those who hold the reins in the classroom.
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Consider the calculator. A survey in the mid-1970s carried out by Mathematics Teacher magazine found that 72 percent of respondents—mainly teachers and mathematicians—opposed equipping seventh graders with calculators. Highlighted in 1975 in Science News, this survey mirrored the broader discourse of the Sesame Street era concerning the introduction of calculators into classrooms, just when costs were approaching the point that some schools could afford to have up to one calculator per student.
Calculators met resistance from educators who feared an overdependence on technology would erode students’ math skills. As one professor observed of students and calculators, “I have yet to be convinced that handing them a machine and teaching them how to push the button is the right approach. What do they do when the battery runs out?”
It is easy to see how the case of the calculator mirrors current concerns about generative AI. The College Board made a similar argument in an article published last spring that mused about the “Great Calculator Panic of the 1980s and ‘90s.” Critics of AI in the classroom argue that students might never learn to writeor respond to written prompts independently if they can simply ask an AI to do it for them. The hypothetical scenario where the Internet or servers are down raises fears that students would be unable to write a simple sentence or compose a basic five-paragraph essay.
Narrow arguments over essay integrity and potential declines in learning quality miss the broader perspective on how this technology could positively reshape curriculum, instruction and assessment.
In classrooms, technology, curriculum, instruction, and assessment evolve together to reshape education. We see this historically with calculators and are now witnessing it unfold in real time with the emergence of generative AI tools.
The introduction of calculators into classrooms didn't set in motion the demise of mathematics education; instead, it significantly broadened its scope while inspiring educators and academics to rethink the educational limits of mathematics. This shift fostered a climate ripe for innovation. Looking at today’s math landscape and what existed in the 1970s, we would be hard-pressed to consider the past superior to the present, to say nothing of the future. Today, high school students use (and more importantly, comprehend) graphing calculators and computers better than undergraduate engineering students in university labs could only a generation ago. Today’s math learning environment is observably more dynamic, inclusive and creative than it was before ubiquitous access to calculators.
In a parallel vein, generative AI promises to extend this kind of innovation in critical thinking and the humanities, making it easier for students to grasp foundational concepts and explore advanced topics with confidence. AI could allow for customized learner support—adapting to the individual pace and learning style of each student, helping to make education more inclusive and tailored to specific needs. Generative AI can better the humanities by making reading and writing more accessible to diverse students, including those with learning disabilities or challenges with traditional writing methods.
Just as calculators led us to reevaluate legacy teaching methods and embrace more effective pedagogical approaches, generative AI calls for a similar transformation in how we approach assignments, conduct classes and assess learning. It will shift us from viewing the college essay as the pinnacle of learning to embracing wider creative and analytical exercises, ones facilitated by AI tools.
The successful integration of calculators into math education serves as a blueprint for the adoption of generative AI across the curriculum. By designing assignments with the expectation that generative AI will enhance rather than shortcut them, educators can foster learning that values creativity, critical thinking and efficient study. This shift necessitates a broader, more adaptable approach to teaching and learning, one that recognizes the potential of technology to elevate educational standards and broaden access to knowledge.
This history points to broader questions over the efficiency and fairness of long-standing educational mechanisms. Take, for example, college admissions essays, which are known to perpetuate bias in university admissions. What if AI allowed us to reconceptualize the tools for students to demonstrate their aptitude and college preparedness? What if AI could allow students to match their intended college major more accurately to the most supportive and corresponding place of higher learning? In academia, we shouldn’t focus solely on AI’s potential for misuse but also on its capability to revolutionize curricula and approaches to learning and teaching.
Far from fearing technological progress, history teaches us to embrace it to broaden and democratize learning. The greater challenge lies not in resisting change, but in leveraging these innovations to develop curricula that address the needs of all learners, paving the way for a more equal and effective education for everyone. Looking ahead, generative AI is not so much a problem to be solved, but instead a powerful ally in our efforts to make education meaningfully universal.
This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.