Review of 34 studies finds AI teaching tools improve how students manage their own learning

The size of the gain differed by strategy type. Resource management strategies such as help-seeking and time management improved most (g = 0.93), followed by metacognitive strategies such as planning and self-evaluation (g = 0.73) and cognitive strategies such as rehearsal and elaboration (g = 0.48).

Results varied widely between studies (I² = 88.34%), and the evidence leans heavily on university students: 25 of 34 studies were in higher education, 5 in secondary and 4 in elementary schools. The authors say the true effects in K-12 settings remain uncertain.

The authors included only studies with experimental or quasi-experimental designs and a passive or active control group, and they excluded one-group pre-post designs. The 34 reports were 30 journal articles and four conference papers; 15 were experimental and 19 quasi-experimental. Most were in language learning (13) and science (9), and 15 lasted less than a week.

Study quality was modest. Appraisal scores on a six-point scale ranged from 1 to 4 (mean 2.76); three articles scored 1 and six scored 4. No study was excluded on quality grounds. Effects were calculated with robust variance estimation to handle multiple effect sizes per study.

Moderator results were mixed. Discipline, intervention length, control group type, sample size and how self-regulated learning was measured showed no significant differences. Studies with small to medium samples reported larger effects than those with more than 150 participants.

The authors say publication bias is unlikely to change the findings: Egger's test was not significant and trim-and-fill found no missing studies. They add that the funnel plot showed some asymmetry, so small-study effects cannot be ruled out.