Literature Review Strategies: A Case of Current Applications of Artificial Intelligence in Science, Technology, Engineering and Mathematics Education
Bibliographic record
Abstract
ABSTRACT Students and novice researchers in education struggle with conducting meaningful, thorough and efficient literature reviews. This challenge is particularly relevant today as the number of publications is increasing exponentially. Even with the assistance of artificial intelligence (AI), researchers must make crucial decisions that significantly impact the literature review process and subsequent investigation. This conceptual paper aims to compare different literature review types, outline the process of determining the most appropriate review type, discuss the development of a search strategy step‐by‐step and compare various frameworks for study selection. By describing these processes, this methodological paper provides a guideline for the literature review process for early‐career researchers. Additionally, this paper will demonstrate this review process with an example focused on the current applications of AI in science, technology, engineering and mathematics (STEM) education.
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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 | 0.340 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".