Think First, AI Second: New Framework Puts Human Cognition Back at the Center of Writing

Artificial intelligence has woven itself into classrooms, laboratories, clinics, and creative studios with astonishing speed, and the most urgent question is no longer whether we should use it but when. A new editorial published in the Annals of Biomedical Engineering argues that the sequence of engagement matters more than the technology itself, and it proposes a structured answer to that question. The framework, developed by Louie Giray of Mapúa University and Khazar University, is called the SSS framework, and its central claim is deceptively simple: human cognition must come first, and AI assistance must come second. Only in that order, the author contends, can writers, students, and researchers preserve the independent thinking that AI is so efficient at replacing.

The inspiration for the framework came, in part, from a moment of professional dismay. At an academic conference, Giray watched a business professor present what was framed as a breakthrough in AI-assisted pedagogy: students would receive a case study, paste it into a generative AI tool, lightly edit the output, and submit the result. The audience applauded. For Giray, the episode crystallized a troubling trend he describes as an AI-first, think-second culture, in which students ask before they think, researchers prompt before they hypothesize, and writers generate before they reflect. In such a design, writing, long the traditional proxy for human understanding, is reduced to post-processing, and the friction of articulation through which ideas are actually formed is removed entirely.

The SSS framework is anchored in three bodies of scholarship. The first is philosophical: Marcus Aurelius’s distinction between what lies within our control and what does not. Applied to AI, the principle directs attention inward, toward the deliberate exercise of cognitive agency, rather than outward, toward futile resistance to the technology’s advance. The second foundation is empirical cognitive science, specifically Robert Bjork’s concept of desirable difficulties, which holds that learning conditions that feel effortful in the moment produce more durable and transferable knowledge than fluent, frictionless ones. When learners generate their own answers and wrestle with problems, they encode knowledge more deeply than when information is delivered efficiently and passively. The third pillar is the extensive literature on automation bias, the well-documented tendency of users to defer to algorithmic outputs even when the machine is demonstrably wrong.

The framework’s name is a deliberate metaphor borrowed from competitive gaming, where an SSS rank denotes the highest achievable performance level. The model is designed to optimize conditions for producing the best possible output through three phases: Set an AI-Free Zone, Struggle, and Spar with AI. Each phase is grounded in specific empirical findings, and each carries explicit boundary conditions acknowledging that the depth of engagement should scale with the complexity and novelty of the task at hand.

The first phase is environmental and behavioral. Because willpower is a depletable resource, and because the mere presence of tempting stimuli reduces cognitive capacity, the framework prescribes situation modification rather than resistance: physically separating AI-accessible devices from the workspace, re-centering analog tools such as paper and pen, and reconfiguring the environment for slow, uninterrupted reasoning rather than speed. Support for this phase comes from a striking study by Nataliya Kosmyna and colleagues, in which 54 participants drafted essays under different conditions. Those writing unassisted showed the strongest neural connectivity, superior memory recall, and the highest sense of ownership over their text. Critically, participants who transitioned from unassisted writing to AI assistance preserved those cognitive advantages, while those who started with AI showed attenuated brain connectivity, suppressed attentional networks, and reduced content retention.

The second phase, Struggle, is the cognitive core of the framework: sustained, unassisted ideation, drafting, and sense-making before any AI consultation. The term is chosen deliberately. Desirable difficulty research demonstrates that the effort of retrieval and generation, even when it produces incomplete and messy output, creates stronger encoding than passive reception of polished information. The framework embraces Anne Lamott’s celebrated notion of the imperfect first draft, treating incompletion not as failure but as the necessary precondition for genuine intellectual ownership. Practical sub-strategies include translanguaging, allowing thought to flow across languages and registers; non-linear ideation through scribbling and visual mapping; and tolerance of incompletion, remaining with a problem long enough for non-obvious connections to emerge.

The most compelling empirical validation comes from a controlled study by Wong and Qiu involving 196 university students who were assigned to one of three AI collaboration protocols: human-only, general free use of ChatGPT, or a regulated think-first structure. On the immediate creative task, the unrestricted AI group outperformed the others. But on a subsequent independent task completed without any AI assistance, the regulated group significantly outperformed both alternatives in independent originality. Process analysis revealed the mechanism: the general-AI group primarily instructed the AI to generate solutions directly, while the regulated group used AI to improve and challenge ideas they had already generated themselves. The conclusion is that generating one’s own ideas first, then using AI to refine them, yields durable learning gains that passive AI use does not.

The third phase, Spar with AI, borrows its logic from martial arts, where a sparring partner does not fight for the practitioner but tests them, exposing weaknesses and demanding articulation of positions under pressure. Here AI is engaged as an active intellectual interlocutor rather than a content generator. Productive uses include asking the AI to identify the strongest counterarguments to a human-drafted argument, detect logical gaps, refine terminology, review structure, and expand perspectives. What the phase explicitly excludes is delegation of thinking: AI does not write the argument, generate the thesis, or replace the writer’s voice. When that relationship inverts, the anchor effect reasserts itself, an early AI output colonizes subsequent human reasoning, and automation bias resumes its corrosive work.

The editorial also situates the framework within a set of ethical concerns that extend beyond individual cognition. At the collective level, widespread reliance on AI-generated content risks what critics have termed AI slop, minimally human content that produces an epistemic monoculture in which models trained on model output amplify existing biases and narrow the range of circulating ideas. AI language models additionally encode documented patterns of linguistic discrimination against non-native English writers and cultural bias toward Western rhetorical norms, meaning practitioners who surrender initial drafting to AI risk having their distinctive voices smoothed away. Neil Postman’s warning about the deification of technology, the cultural disposition to treat machine outputs as inherently superior to human judgment, looms over the entire discussion, and the framework is presented as a structural inoculation against it.

Giray is candid about the framework’s limitations. The three-phase structure has not been directly validated as a unified model through experimental research, even though each component draws on substantial empirical support from desirable difficulty, situation modification, cognitive forcing functions, and designed friction in human-computer interaction. He proposes a phased research agenda beginning with survey studies to map practitioner behavior, followed by randomized controlled trials comparing SSS-aligned workflows with AI-first alternatives, mixed-methods analyses of how outcomes vary with task complexity and expertise, and long-term cohort studies, including multilingual and non-Western populations. A faculty meeting toolkit accompanies the paper to help educators translate the sequence into concrete assignments. The deeper proposition, however, stands on its own: the human brain operates on roughly twenty watts of metabolic energy yet achieves associative reasoning and ethical judgment that no algorithm replicates, and those capacities require exercise. Clear the space, wrestle with the idea, and only then invite AI to challenge what human thinking has produced. In that sequence, AI may magnify the reach of human thought, but the power to judge must remain with the thinker.

Subject of Research: A three-phase framework for sequencing human cognition before AI assistance in writing

Article Title: SSS Framework: A Think-First, AI-Second Model for AI-Assisted Writing

Article References: SSS Framework: A Think-First, AI-Second Model for AI-Assisted Writing. (n.d.). https://doi.org/10.1007/s10439-026-04358-5

Image Credits: AI Generated

DOI: 10.1007/s10439-026-04358-5

Keywords: SSS framework, AI-assisted writing, human-AI collaboration, automation bias, desirable difficulties, cognitive offloading, intellectual autonomy, productive friction, generative AI, cognitive science, education, epistemic erosion