According to the chapter, what is the difference between the substitution and augmentation paradigms for AI in academia, and why does the author argue for augmentation?

In the substitution paradigm, AI replaces the human researcher by performing scholarly tasks entirely on its own. In the augmentation paradigm, AI extends human capabilities and acts as a collaborator while the human retains judgment and oversight. The author argues for augmentation because meaningful scholarship depends on human qualities like curiosity, critical thinking, and contextual understanding, and because offloading tasks to AI can erode researchers' skills and the integrity of their work.

The chapter contrasts substitution and augmentation as two paradigms for AI in academia. Substitution treats AI as a replacement for human effort, potentially taking over tasks such as automated essay grading or fully AI-written literature reviews. Augmentation, by contrast, treats AI as an extension of human capabilities: it can relieve tedious work and amplify productivity, but the human remains in charge of decisions, oversight, and quality checks. For example, a researcher might use AI to format a bibliography while still choosing which references to include and verifying them personally. The author argues for the augmentation paradigm because scholarly work relies on distinctly human qualities—curiosity, critical thinking, ethical reasoning, and contextual awareness—that AI does not yet possess. Even when AI can perform a task competently, there may be educational or epistemic reasons to keep a human in the loop; if researchers never practice tasks like selecting sources or summarizing articles, they may lose their grasp of nuance and miss out on understanding the foundations of their field. Offloading too much to AI risks undermining the rigor and integrity of research. Instead, pairing human insight with AI's rapid analysis and synthesis can produce results that exceed what either could achieve alone.