The Challenges of Critical Thinking in the Era of Artificial Intelligence
Bibliographic record
Abstract
I argue that critical thinking is based on active learning, engaged independent thinking, and examining all information including recently impactful ChatGPT and other AI sources. Thoughtfully questioning what is being learned as well as critically and creatively analyzing and evaluating information such as AI is necessary to gain a deeper understanding as an effective thinker. Critical thinking pedagogy should also promote “portability” and citizenship, including information-based online multimedia literacy such as AI, as well as employment and professional information. This means becoming a critical thinker inside and outside the classroom and take what is learned into our personal, public, and professional lives. The article begins with an examination of four discrepancies or issues related to critical thinking in higher education. The critical thinking literature and Kenedy’s Model of Cyclical Critical Thinking will then be considered. This will be followed by the discussion and summary regarding suggested guidelines for critically evaluating AI. Finally, conclusions regarding further work including pedagogical models for teaching critical thinking in the era of AI and other future work are considered.
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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".