Actionable companion · Learning design & outcomes
Learning with agency: A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): K–12 · mixed / unspecified. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract The use of artificial intelligence in education (AIEd) has grown exponentially in the last decade, particularly intelligent tutoring systems (ITSs). Despite the increased use of ITSs and their promise to improve learning, their real educational value remains unclear. This systematic review aims to identify the effects of ITSs on K-12 students’ learning and performance and which experimental designs are currently used to evaluate them. The 28 studies analyzed in this systematic review included a total of 4597 students (N = 4597) and used quasi-experimental designs with varying intervention durations. Overall, our findings suggest that the effects of ITSs on learning and performance in K-12 education are generally positive but are found to be mitigated when compared to non-intelligent tutoring systems. 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. Subject terms: Education, Education
What kind of evidence is this?
Intelligent tutoring & adaptive learning · Evidence synthesis
Stage: K–12 · mixed / unspecified. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
First, keywords related to our research topic were identified (scoping). Second, all retrieved articles were screened using the selected keywords to determine which ones to include in the systematic review (screening). Finally, the relevant data from the selected articles was extracted (extraction). The following subsections provide a detailed description of the scoping, screening and extraction methods followed by the PRISMA flow diagram.
Limitations and uncertainty · source passage
This review focused exclusively on articles written in English within the fields of education and computer science. Restricting the selection to only two databases might also have limited the scope of this review. In addition, it is imperative to consider publication and reporting bias64. Therefore, any systematic review may be more likely to report positive and significant effects of ITSs. Finally, this systematic review focused specifically on the effects of ITSs on learning and performance. Other educational variables, such as interest, attitude or motivation towards school subjects, were not considered in this review but should be considered in future research.
Design the system before the pilot
Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.
- Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Verify the grades and participant ages in each included study before selecting a classroom pilot. Use teacher-led hints and preserve a non-AI route; a K–12 label does not establish the same benefit across every school year. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Compare independent learning, delayed retention and transfer within the locally selected grade band. Report age-stage uncertainty rather than pooling young children and adolescents as one population. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Angélique Létourneau; Marion Deslandes Martineau; Patrick Charland; John Alexander Karran; Jared Boasen; Pierre Majorique Léger. A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. NPJ Science of Learning (2025-05-14). https://doi.org/10.1038/s41539-025-00320-7
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.