Journal for School Superintendents · Evidence into practice

Agentic education.
The science of learning with AI.

Explore what tutors, adaptive systems and AI agents can help learners do—and what must still be demonstrated without assistance. Read the licensed research, inspect its population and methods, then open an age-sensitive implementation companion.

634new source papers and companions

3,634 / 7,000overall verified CC source collection

5Agentic & multi-agent systems47Conversational & pedagogical agents134Intelligent tutoring & adaptive learning34Embodied learning agents414Supporting AI learning science

Explicitly agentic systems are labeled separately from chatbots, adaptive tutors and supporting AI learning science. Original authors and DOIs are preserved. Automated relevance, license and design screening is not an independent quality appraisal. Companion guides are AI-assisted editorial material and are not counted as additional source papers.

Different learners. Different evidence requirements.

Stage tags come from source titles and abstracts; exact participant ages require full-text verification. Papers may mention multiple stages, including in reviews or teacher-perception studies; these counts are not counts of child-participant trials. A higher-education finding does not establish effectiveness for children, and an age-band count does not establish a proven intervention.

Stage-specific research questions and collection coverage
Stage mentioned in sourceDesign focusQuestion to testPapers mentioning stage
Early childhood · preschoolAdult-mediated language, play and turn-takingDoes the child demonstrate the skill with a person after the tool is removed?24
Primary schoolGuided hints, concrete examples and fading scaffoldsCan learners explain and transfer the idea independently?17
Secondary schoolSelf-regulation, reasoning and confidence calibrationDoes AI support thinking or replace the learner’s first attempt?72
Children · stage unspecifiedDevelopmental context must be verifiedAre participant ages and the educational setting established?59
K–12 · mixed / unspecifiedGrade-specific appraisal before implementationWhich grades were actually studied, and do results differ by age?19
Higher educationDisciplinary reasoning, feedback and assessmentAre gains measured in students rather than in model exam scores?297
Adult & professional learningAuthentic practice and expert-supervised simulationDoes performance persist on a new task without AI?37
Age / stage not establishedPopulation verification before implementationCan participant ages or education stage be established in the full text?196

What the research currently supports

K–12: inspect the comparator

A systematic review of 28 studies involving 4,597 K–12 learners reported generally positive results for intelligent tutoring, with smaller advantages against non-intelligent tutoring comparators. The included studies were quasi-experimental and varied in duration. This supports carefully designed pilots, rather than an assumption that any AI tutor will improve learning.

Read the review and search its evidence →

Preschool: individual differences matter

A study of 28 children aged 4–5 followed four robot-supported word-learning sessions. Shy children showed different interaction behavior and lower initial post-test scores, but the reported gap closed on a later test. This is a small study of individual differences within robot interaction, not a demonstration that robots outperform teachers. It argues for examining delayed outcomes and learner variation rather than equating expressiveness with learning.

Read the preschool word-learning study →

Young learners: separate engagement from attainment

A nine-week elementary afterschool robot-theater program involved 38 children aged 6–10; 21 participated in research activities. The study reports perceptions and enthusiasm, alongside teacher concerns about distraction and peer relationships. It does not establish a randomized improvement in academic achievement or justify extrapolation to preschoolers.

Read the child–robot study →

University: promising effects can have weak certainty

A review of 66 randomized trials with 4,911 undergraduate health-professions learners found that most studies had high risk of bias and rated certainty low to very low. Positive knowledge, satisfaction or confidence results must be read alongside those limits. These are adult disciplinary settings, not evidence of effectiveness for school-age children.

Read the RCT evidence synthesis →

These brief interpretations are AI-assisted editorial synthesis of the linked source abstracts, not an independent systematic review. Read each full paper’s methods, outcomes and limitations before making an implementation decision.

Performance is not learning

Separate immediate assisted task performance, student satisfaction and model benchmark scores from delayed retention and unaided transfer.

Agency requires a boundary

Specify goals, tool permissions, memory, supervision and stopping rules. A chatbot label alone says little about autonomy or instructional quality.

Evidence needs a comparator

Ask who learned, compared with what, over how long and under which conditions. Review sample size, attrition, outcome validity and risks of bias in the full paper.

Research, agents and actionable companions

196 matching papers. Design labels are screening aids; verify methods before drawing causal conclusions. Clear filters

Supporting AI learning science

Evaluating AI performance in infectious disease education: a comparative analysis of ChatGPT, Google Bard, Perplexity AI, Microsoft Copilot, and Meta AI

Abdulaziz Ibrahim Alzarea; Azfar Athar Ishaqui; Muhammad Bilal Maqsood; Abdullah Salah Alanazi; Aseel Awad Alsaidan; Tauqeer Hussain Mallhi; Narendar Kumar; Muhammad Imran; Sultan M. Alshahrani; Hassan H. Alhassan; Sami I. Alzarea; Omar Awad Alsaidan

Frontiers in Medicine · 2025-10-13 · CC BY 4.0

Age / stage not established
Model benchmark / tool evaluation

Assessment & reasoning

Read sourceActionable companionPaper agent

Supporting AI learning science

Development and pilot evaluation of a large language model-based clinical simulation system for medical education in gastroenterology

Stiliano Maimaris; Gianmarco Tedino; Giusi Aurora Memoli; Carlotta Crisciotti; Alessandra Albrizio; Camilla Sammartino; Erica Bartolotta; Giulio Massetti; Giulia Mantica; Paolo Minerba; Federico Biagi; Annalisa Schiepatti

BMC Medical Education · 2026-06-25 · CC BY 4.0

Age / stage not established
Model benchmark / tool evaluation

Feedback & scaffolding · Assessment & reasoning · Accessibility & inclusion

Read sourceActionable companionPaper agent

Embodied learning agents

Embracing the Future of Medical Education With Large Language Model–Based Virtual Patients: Scoping Review

Jianwen Zeng; Wenhao Qi; Shiying Shen; Xin Liu; Sixie Li; Bing Wang; Chaoqun Dong; Xiaohong Zhu; Yankai Shi; Xiajing Lou; Bingsheng Wang; Jiani Yao; Guowei Jiang; Qiong Zhang; Shihua Cao

Journal of Medical Internet Research · 2025-11-13 · CC BY 4.0

Age / stage not established
Evidence synthesis

Cognitive load & attention · Assessment & reasoning

Read sourceActionable companionPaper agent

Generative AI education leadership collection · Full leadership collection