Beyond Productivity: Exploring the Cognitive Cost of Using ChatGPT
Bibliographic record
Abstract
Generative AI is a category of artificial intelligence that focuses on generating new content and data. The rapid integration of AI tools, including ChatGPT and other large language models LLMs offers many promises in terms of productivity and efficiency. Currently, these tools are employed as personal intellectual and managerial assistants at both the individual and professional levels. This is due to their ability to provide unprecedented access to information at speed and creativity, which enhances our short-term productivity and performance in various tasks. On the other hand, concerns emerged about potential cognitive consequences. This study aims to investigate the impact of excessive AI use on attention, critical thinking, reasoning, and memory retention. The findings revealed that excessive reliance on AI increases the extrinsic load. This strains attention and decreases deep involvement. There is also an accompanying risk of reduced internal memory retention, often referred to as digital amnesia. Additionally, it might hinder synthesis, analysis, and critical thinking skills. This will affect the well-being of the individual and society. Also, relying on it too heavily may discourage individuals from engaging in deep thinking, independent reasoning, and verifying the quality of sources and information. This study suggests a novel model, “the Cognitive Engagement Spectrum” framework. The framework presents three types of users based on their level of interaction and engagement with AI tools. The passive users, active editing users, and generative prompting and socratic dialogue users. Additionally, experts should keep in mind that the performance of AI models improves when they interact with users. Therefore, there should be a warrant for their future capabilities. Ultimately, we need to reach a point where we control the tool, not the other way around.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".