This is Part 2 of a two-part series examining whether outsourcing creative and cognitive work to AI is degrading human thinking. Part 1 reviewed the substantial evidence for cognitive offloading and skill decay. Part 2 examines the counter-evidence, the conditions under which AI making us smarter is genuinely possible, and what a fair verdict actually requires.

The Evidence Deserves a Second Look

Part 1 of this series presented a genuinely troubling body of evidence: EEG studies showing weaker neural connectivity, clinicians losing diagnostic skill after AI support was introduced, and a documented illusion of competence among AI users. None of that evidence was overstated, and none of it should be dismissed. But responsible engagement with any body of research requires looking at the full picture, including the studies, researchers, and institutions actively exploring whether AI making us smarter is not just possible but already happening under the right conditions.

The truth that emerges from a complete review of the literature is neither the alarmist story nor a naive optimism. It is something more specific and more useful: the outcome depends heavily on how AI is used, not merely on whether it is used at all.

The Same MIT Study, Read More Carefully

It is worth returning to the widely cited MIT Media Lab EEG study from Part 1, because subsequent, more careful engagement with its actual design reveals an important nuance often lost in headline coverage. The study compared three conditions: writing entirely from memory, writing with a search engine, and writing with an LLM that performed the bulk of the composition itself. The condition that showed weakened neural engagement was specifically the one in which the AI did most of the intellectual work for the participant, essentially replacing their thinking rather than supporting it.

This distinction matters enormously for the AI making us smarter question, because it points toward a specific, testable hypothesis: the harm observed in cognitive offloading research may depend less on AI use per se and more on whether the human remains an active, effortful participant in the cognitive task or becomes a passive recipient of a finished output. A 2026 paper in Computers in Human Behavior captured this distinction precisely in its title: AI makes you smarter but none the wiser, describing a genuine disconnect between measurable performance gains and accurate self-assessment of understanding, a finding that complicates rather than confirms a simple decline narrative.

The Cover Letter Study: A Case for Genuine Learning

One of the more carefully designed recent studies bearing on AI making us smarter comes from behavioural scientists at Wharton, led by Benjamin Lira Luttges. Researchers taught participants to edit poorly written cover letters using either AI-generated feedback or feedback from human professionals. After this training phase, participants were then asked to edit a new, poorly written cover letter entirely without any assistance, human or AI.

The results were genuinely encouraging for anyone hoping AI making us smarter is achievable rather than wishful thinking. Letters produced by the AI-trained group were just as likely to secure a job interview, according to blind human evaluators, as letters from the group trained by human professionals. Crucially, the AI in this study did not simply hand participants a rewritten letter to copy. It walked them through structured feedback, and the learning transferred to genuinely independent performance afterward. This is precisely the kind of evidence that distinguishes AI used as a teacher from AI used as a replacement for thinking, and the distinction turns out to be the single most important variable across the entire body of research.

The Augmentation and Atrophy Framework

A comprehensive 2025 review published in the American Journal of Education and Information Technology introduced a useful conceptual framework worth adopting directly: the Problem-Solving Trade-Off Hypothesis, which proposes that AI’s cognitive impact splits cleanly into augmentation effects and atrophy effects, often for the very same tool, depending entirely on how it is deployed. When used as a research partner, actively engaged with and questioned, AI can genuinely augment critical inquiry. When accepted uncritically as a finished answer, the same tool promotes intellectual passivity.

This framework helps explain an otherwise confusing pattern in the research literature, where some studies find AI making us smarter while others find the opposite, often examining superficially similar AI tools. A comprehensive 2026 review of the cognitive literature reached a similarly nuanced conclusion: moderate AI usage shows minimal cognitive impact, while excessive reliance correlates with decreased critical thinking abilities. The relationship is not linear, and it is not simply about the amount of AI use but the structure and intentionality of that use.

What USC’s New Research Is Actually Testing

The most rigorous ongoing effort to move beyond speculation on the AI making us smarter question is a study launched in July 2026 by USC Viterbi, funded by the National Science Foundation, examining doctors, journalists, and software engineers to determine whether structured AI use can strengthen creativity and critical thinking rather than erode it. The study’s design is explicitly informed by earlier findings, including Stadler et al.’s 2024 research showing that AI use eases mental load but often at the expense of depth of understanding, precisely the tension this two-part series has traced throughout.

What makes the USC research significant is its second phase, which moves beyond simply measuring whether harm occurs and instead attempts to redesign how humans and AI interact specifically to optimise for better creativity and critical thinking outcomes. This reflects a genuine and important shift in the research community’s framing, from asking whether AI making us smarter or dumber is happening as a fixed, inevitable outcome, toward asking how interaction design itself determines which outcome occurs.

The Original Sin of Bad Comparisons

A significant portion of the alarm in this debate traces back to comparing AI-assisted outcomes against an idealised, effortful baseline that most people were never actually achieving in the first place. Before generative AI, the realistic alternative to using ChatGPT for a first draft was often not deep, effortful, independent composition. It was frequently a rushed, low-effort draft produced under time pressure, or simply not producing the work at all. The relevant comparison for AI making us smarter is not AI use versus an idealised deep thinker with unlimited time. It is AI use versus the actual behaviour people were engaging in before AI existed, which was frequently far from ideal itself.

This reframing does not excuse genuine skill atrophy in domains, such as medical diagnosis, where the underlying skill is safety-critical and must be actively maintained regardless of convenience. But for a great deal of everyday writing, brainstorming, and problem solving, the honest comparison group was never a maximally engaged human mind. It was often a tired, distracted, or simply absent one, and against that realistic baseline, AI assistance frequently represents a genuine net gain in both output quality and, when used interactively rather than passively, in the thinking that produces it.

Fostering Collaboration Rather Than Replacement

A 2025 paper in the Journal of Student Research at Indiana University East reviewed the competing evidence directly and reached a conclusion that deserves to anchor any balanced verdict on this topic: AI fosters collaboration and efficiency, and in some cases may enhance critical thinking skills, while overuse without deliberate structure can deplete those same skills. The word collaboration is doing important work in that sentence. It suggests the healthiest relationship with AI tools treats them as a genuine thinking partner, one whose output is questioned, challenged, and integrated actively, rather than either a replacement for thought or a threat to be avoided entirely.

The CHI 2025 Tools for Thought workshop, convening 56 researchers across cognitive science, human-computer interaction, and education, framed the challenge in exactly these terms: the goal is not merely to protect human cognition from AI’s potential negative impacts, but to actively design AI tools and interaction patterns that augment thinking, in the same way that older external tools, including writing itself, have historically extended and strengthened human cognitive capacity rather than simply replacing it.

A Genuinely Balanced Verdict

Bringing both parts of this series together, the fairest conclusion is neither AI making us dumber nor AI making us smarter as a fixed, universal outcome. It is that AI is a cognitive amplifier whose effect depends almost entirely on the structure of the interaction. Passive, unstructured use, accepting AI output wholesale without engagement, reliably correlates with skill atrophy and a documented illusion of competence. Active, structured use, treating AI as a partner to question, challenge, and learn from rather than simply defer to, shows genuine evidence of strengthening rather than weakening independent capability afterward.

For the specific creative activities that motivated this series, writing and image generation, the practical implication is clear. Using an LLM to produce a finished piece of writing with minimal engagement likely does erode the specific compositional and reasoning skills that writing itself has always cultivated as a side effect of the struggle to express an idea clearly. Using an LLM as an interactive collaborator, one whose suggestions are evaluated, revised, and pushed back against, appears considerably more likely to leave those same skills intact or even strengthened, closer to how a skilled writer benefits from an editor’s feedback than a diminishment of their own capability.

Conclusion: The Choice Is Still Ours

The question of whether AI is making humanity dumber or AI is making us smarter turns out not to be a question about the technology at all. It is a question about human choices, individual and institutional, about how deeply we engage with tools that are, for the first time in history, capable of doing so much of our thinking for us if we allow them to. The calculator did not make humanity worse at abstract mathematical reasoning, because the deeper reasoning skills calculators freed us from tedious computation to pursue turned out to matter more than the arithmetic itself.

Whether generative AI follows a similar trajectory, freeing humans for a more valuable kind of thinking, or instead erodes capacities more central to what makes thinking meaningful in the first place, remains genuinely undetermined, and will likely be decided differently across different domains, different age groups, and different patterns of use.

What the evidence assembled across both parts of this series makes clear is that the outcome is not predetermined by the technology itself. It is being determined, right now, by millions of individual decisions about how deeply to engage with the tools already in nearly everyone’s hands. That is, in the end, a more demanding and more hopeful conclusion than either a simple story of decline or a simple story of progress would offer. The evidence suggests humanity retains meaningful agency in this outcome. Whether AI making us smarter becomes the dominant story or the exception may depend less on further research and more on whether that agency is actually exercised.

This concludes our two-part series on AI and human cognition. Explore Part 1 for the full evidence on cognitive offloading