The interaction between a physics professor and a student questioning the efficacy of visualizations encapsulates the central theme of this article: the crucial role of human engagement in the learning process, particularly in the face of rapidly advancing artificial intelligence (AI). The student’s desire for a simplified, animated explanation is countered by the professor’s insistence on the importance of internalizing concepts through individual effort. This exchange sets the stage for a broader discussion on the implications of AI in education and the very nature of knowledge acquisition.
The author, Professor Ulf Danielsson, argues that the current fascination with AI in academia is misguided and potentially detrimental to education. He likens AI to a mirror, reflecting and reproducing existing data but creating nothing new. AI, powered by machine learning, excels at pattern recognition and prediction, much like charting a child’s growth to project future development. However, just as the growth chart lacks understanding of the child, AI comprehends neither the questions it answers nor the information it provides. The danger, Danielsson warns, is a Narcissus-like infatuation with AI’s seemingly intelligent output, blinding us to its inherent limitations and leading to an over-reliance on its capabilities.
Danielsson criticizes the uncritical embrace of AI in universities, where some view it as a tool to enhance pedagogy. He highlights the problematic desire to automate feedback and grading processes, even lamenting the legal restrictions preventing the use of AI to evaluate student work. He emphasizes the irony of this trend coinciding with the recognized failure of digitalization in schools, which has contributed to declining literacy and attention spans. The core of learning, he argues, lies in the student’s cognitive development, not simply the production of an assignment. AI, therefore, cannot replace the crucial role of a human teacher in guiding this process and ensuring the quality and veracity of student work. Education is not a passive spectacle but an active process of mental and physical engagement, akin to developing physical fitness through exercise, not observation.
Danielsson foresees widespread plagiarism becoming the norm in an AI-driven world. AI systems generate seemingly original text and images from existing human-generated data, making proper attribution impossible. This creates a chain of plagiarism: the AI generates plagiarized content, the student submits it as their own, the teacher evaluates it (potentially using AI), and the university ultimately validates the plagiarized work by awarding a degree. While some argue that human writing also synthesizes various influences, Danielsson emphasizes a crucial difference: AI lacks autonomy and relies entirely on human input. If trained solely on its own output, AI degrades, much like repeatedly photocopying a copy. This is a significant risk as AI-generated content proliferates online, contaminating the data pool used for future training. The only solution, he argues, is more human-generated data.
Humans, unlike AI, possess a connection to the physical world and a history of lived experience that enriches even our most inane utterances. This inherent subjectivity and originality, according to Danielsson, is vital to counter the sterile, closed-loop nature of AI. He emphasizes the importance of his own writing being entirely human-generated, contributing to the pool of authentic data for future AI training. He expresses concern that universities, supposed bastions of original thought, are failing to uphold this standard, potentially producing devalued degrees based on AI-generated work.
The only way to restore meaning and integrity to education, Danielsson concludes, is to rely solely on in-person, proctored examinations, devoid of any electronic assistance. This shift would necessitate study methods that prioritize independent thinking and problem-solving abilities. He envisions a return to traditional learning methods like reading physical books and writing by hand, supplemented perhaps by some innovative AI-based tools. He ends with a sense of optimism, envisioning a future where human intellect, honed through rigorous and authentic engagement, rises to meet the challenges posed by AI. The image of the professor cleaning the chalkboard, covered in chalk dust yet invigorated, symbolizes the enduring power of human engagement in the pursuit of knowledge.
