Does AI tutoring improve learning?

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1. 1

   The Impact of Artificial Intelligence-Supported Instruction on Student Learning in STEM: A Systematic Review and Meta-Analysis.

   Journal of Intelligence (Doğan et al.)Published Jun 15, 2026Checked Oct 3, 2026

   “Moderator analyses were conducted for educational level, STEM discipline, and intervention duration. Publication bias was assessed using multiple diagnostic methods. The meta-analysis revealed a statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p p = 0.088). Regarding intervention duration, the highest effect size was observed in interventions lasting more than one month and up to two months, though no consistent pattern of increasing effectiveness with longer durations was found. Publication bias analyses suggested minimal influence on the overall findings. AI-supported instructional interventions demonstrate a moderately to highly positive impact on student learning outcomes in STEM education. The effectiveness of these interventions varies according to educational level, disciplinary context, and intervention duration. These findings provide robust empirical evidence supporting the pedagogical value of AI in STEM education and offer guidance for educators and policymakers regarding effective implementation.”

2. 2

   Artificial intelligence augmented tutoring vs expert instruction on learning simulated general surgical skills: a systematic review and meta-analysis.

   BMC medical education (Hanna et al.)Published Jun 10, 2026Checked Oct 3, 2026

   “Four studies (3 RCTs and 1 pilot prospective study), encompassing a total of 268 participants, were included in the meta-analysis. AI tutoring showed a small, statistically significant improvement in expert-rated OSATS scores (MD 0.20; 95% CI, 0.01 to 0.39) with no heterogeneity (I2 = 0%) but with low certainty. The AI group reported a significantly higher extraneous cognitive load (MD 0.23; p = 0.01). No significant difference was found in ICEMS scores.ConclusionAI tutoring systems demonstrated comparable effectiveness to expert instructors in simulated surgical skill acquisition. The small OSATS advantage (0.20 points) is of uncertain clinical significance, falling below commonly published competency cut-points for meaningful change on global rating scales, and is based on low-certainty evidence driven by a single high-risk-of-bias study. AI tutoring imposed a higher extraneous cognitive load. These findings do not support replacing human instructors with AI. Instead, the evidence supports a hybrid model, though this itself requires rigorous empirical validation.”

3. 3

   Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial.

   BMC medical education (Zhao et al.)Published May 22, 2026Checked Oct 3, 2026

   “All groups followed the same 16-week course structure based on Gagné's Nine Events of Instruction, differing only in the teaching tools and tutoring strategies used. Learning outcomes were evaluated using module examination scores, final comprehensive scores, knowledge graph comprehension scores, knowledge retention rates, case-based inference accuracy, and multidimensional questionnaires. Statistical analyses were conducted using one-way ANOVA with post-hoc comparisons.ResultsSignificant differences were observed among the three groups across multiple learning outcomes. The AI-Enhanced Group achieved higher module scores (82.5 ± 7.3) compared with the Blended Group (77.6 ± 8.1) and Traditional Teaching Group (74.1 ± 8.5) (F = 28.64, p ConclusionsThe blended teaching model integrating a generative AI-powered digital tutor with a knowledge graph significantly improved nursing students' academic performance, knowledge retention, and clinical reasoning ability. This AI-enhanced instructional model may provide an effective approach for reforming foundational medical education and promoting structured knowledge learning.”

4. 4

   Application and effectiveness analysis of AI Intelligent tutoring system combined with knowledge graph in basic medical pathology teaching.

   BMC medical education (Yuanyuan & Ying)Published May 5, 2026Checked Oct 4, 2026

   “The control group used traditional teaching, while the experimental group adopted the "AI + knowledge graph" integrated model with AI-assisted learning and knowledge graph reviews. SPSS was used for quantitative and qualitative data analysis.ResultsThe experimental group showed significantly better outcomes: final score (84.2 ± 6.9 vs. 74.1 ± 7.6), after adjusting for class-level clustering using a two-level mixed-effects model, adjusted mean difference = 8.2, 95% CI: 4.7-11.7, P = 0.012, Cohen's d = 1.13 (95% CI not computed), indicating a large effect size. knowledge mastery rate (90.3% vs.75.8%), clinical case analysis score (28.5±3.2 vs. 21.3±4.1, PConclusionThis integrated model effectively promotes structured knowledge construction and learning efficiency, realizing the synergistic effect of AI and knowledge graph. It provides a replicable and practical reference for the digital and intelligent reform of basic medical education courses.”

5. 5

   Effects of Artificial Intelligence-Supported Education on Critical Thinking, Problem-Solving, Clinical Reasoning, and Decision-Making in Health Science Education: A Systematic Review of Interventional Studies.

   Advances in medical education and practice (Alhur et al.)Published Sep 8, 2026Checked Oct 4, 2026

   “The included studies evaluated conventional and generative AI technologies, including diagnostic AI systems, intelligent tutoring systems, AI-enabled chatbots, generative image systems, and large language models. Findings varied across the four cognitive outcomes. Structured AI-supported interventions frequently demonstrated favorable effects on selected clinical reasoning, decision-making, problem-solving, and critical-thinking outcomes; however, null, mixed, and negative findings were also reported, particularly when AI was compared with established educational resources or used without structured instructional guidance.ConclusionAI-supported education may enhance selected higher-order cognitive outcomes among health science learners, but the evidence is inconsistent across technologies, instructional designs, comparators, and cognitive constructs. Structured integration that preserves active learner engagement and independent reasoning appears important. Further rigorous studies using validated outcome measures and assessments of sustained, unaided performance are needed.”

6. 6

   Artificial Intelligence-Assisted Versus Traditional Learning and Long-Term Knowledge Retention Among Undergraduate Medical Students: A Sequential, Explanatory Mixed-Methods Study.

   Cureus (Padmavathi et al.)Published Aug 11, 2026Checked Oct 3, 2026

   “Both learning methods significantly improved immediate post-test scores. However, AI-assisted learning was associated with significantly higher knowledge retention at 1 week (75.5% vs. 57.9%) and 1 month (61.4% vs. 35.9%) compared with traditional learning (p<0.001). Repeated-measures ANOVA showed significant effects of learning method (F=181.66, p<0.001), assessment time (F=625.73, p<0.001), and their interaction (F=62.23, p<0.001). A significantly greater proportion of students exceeded a Bloom-inspired heuristic benchmark for high memory retention following AI-assisted learning (43.4% vs. 3.0%). AI-assisted learning was associated with higher long-term memory retention compared with traditional learning under the specific, non-randomized conditions investigated; this association does not establish that AI-assisted learning caused the improvement. Students' perceptions of personalized, interactive explanations and active engagement appeared linked to improved retention, whereas passive reliance on AI-generated responses was associated with cognitive offloading.”

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   Comparison of artificial intelligence assisted training and traditional learning paths in clinical simulation skills training: meta-analysis of randomized controlled trials.

   Frontiers in medicine (Che et al.)Published Aug 21, 2026Checked Oct 3, 2026

   “Procedure time and Operation completion rate did not differ between groups. Learner satisfaction and willingness to recommend the training and stress level of AI assisted training were higher than those of the control group.ConclusionThis study shows that AI, especially the vision-based deep-learning, can effectively guide the training of procedural skills. However, for complex cognitive skills like clinical reasoning, AI is best used to replace inefficient peer practice with standardized, high-intensity training. Expert teachers should then focus on advanced clinical judgment, emotional communication, and personalized correction. Overall, AI offers a major opportunity to improve quality and scale up medical simulation education. Its best path is deep integration with traditional teaching-combining their strengths rather than simply replacing teachers.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261411402. The protocol was registered in advance in PROSPERO online platform as CRD420261411402.”

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   Intelligent tutoring system (Wikipedia)

   WikipediaPublished Sep 30, 2026Checked Oct 3, 2026

   “An intelligent tutoring system (ITS) is a computer system that imitates human tutors and aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher (e.g., classroom lectures), or no teacher at all (e.g., online homework). ITSs are often designed with the goal of providing access to high quality education to each and every student.”