A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education
Bibliographic record
Abstract
Abstract The use of artificial intelligence in education (AIEd) has grown exponentially in the last decade, particularly using intelligent tutoring systems (ITSs). Despite the increased use of ITSs and their promise to improve learning, their real educational value remains unclear. Therefore, this systematic review aims to identify which experimental designs are currently used to evaluate the effects of ITSs and what are the effects of ITSs on K-12 students' learning and performance. Studies retrieved from ERIC USDE and Scopus databases reporting effects of an ITS in a formal learning setting published after 2009 were screened and the data extracted was categorized by experimental design. The 20 studies analyzed in this systematic review included a total of 2853 students (N = 2853), predominantly in middle and high school Science, Technology, Engineering and Mathematics (STEM) -related classes. These studies used quasi-experimental designs with varying intervention durations, with half of them categorized as very short. Some studies reported positive effects of ITSs on learning and performance in contrast to traditional teaching. However, the effects were found to be mitigated when compared to non-intelligent tutoring systems. The included studies have identified optimal conditions or contexts for the application of specific ITSs. Overall, our findings suggest that the effects of ITSs on learning and performance in K-12 education are generally positive. However, additional research with longer interventions and increased sample sizes with greater diversity is warranted. Additionally, the ethical implications of using AI for teaching should be investigated.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".