Journal for School Superintendents · Evidence into practice
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
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.
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 mentioned in source | Design focus | Question to test | Papers mentioning stage |
|---|---|---|---|
| Early childhood · preschool | Adult-mediated language, play and turn-taking | Does the child demonstrate the skill with a person after the tool is removed? | 24 |
| Primary school | Guided hints, concrete examples and fading scaffolds | Can learners explain and transfer the idea independently? | 17 |
| Secondary school | Self-regulation, reasoning and confidence calibration | Does AI support thinking or replace the learner’s first attempt? | 72 |
| Children · stage unspecified | Developmental context must be verified | Are participant ages and the educational setting established? | 59 |
| K–12 · mixed / unspecified | Grade-specific appraisal before implementation | Which grades were actually studied, and do results differ by age? | 19 |
| Higher education | Disciplinary reasoning, feedback and assessment | Are gains measured in students rather than in model exam scores? | 297 |
| Adult & professional learning | Authentic practice and expert-supervised simulation | Does performance persist on a new task without AI? | 37 |
| Age / stage not established | Population verification before implementation | Can participant ages or education stage be established in the full text? | 196 |
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 →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 →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 →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.
Separate immediate assisted task performance, student satisfaction and model benchmark scores from delayed retention and unaided transfer.
Specify goals, tool permissions, memory, supervision and stopping rules. A chatbot label alone says little about autonomy or instructional quality.
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.
17 matching papers. Design labels are screening aids; verify methods before drawing causal conclusions. Clear filters
Embodied learning agents
Jiayuan Dong; Shuqi Yu; Koeun Choi; Myounghoon Jeon
Frontiers in Robotics and AI · 2025-03-14 · CC BY 4.0
Primary school · K–12 · mixed / unspecified · Children · stage unspecified
Qualitative / mixed methods
Motivation & engagement
Intelligent tutoring & adaptive learning
Maria Bolsinova; Matthieu J S Brinkhuis; Abe D Hofman; Gunter Maris
The British Journal of Mathematical and Statistical Psychology · 2022-06-05 · CC BY 4.0
Primary school
Design requires full-text appraisal
Learning design & outcomes
Embodied learning agents
Maria T Papadopoulou; Elpida Karageorgiou; Petros Kechayas; Nikoleta Geronikola; Chris Lytridis; Christos Bazinas; Efi Kourampa; Eleftheria Avramidou; Vassilis G Kaburlasos; Athanasios E Evangeliou
Children · 2022-07-31 · CC BY 4.0
Primary school · Children · stage unspecified
Comparative / pre-post study reported
Learning design & outcomes
Embodied learning agents
Yang Feng; Xiya Wang
Frontiers in Psychology · 2023-10-04 · CC BY 4.0
Primary school · Children · stage unspecified
Design requires full-text appraisal
Feedback & scaffolding · Motivation & engagement · Assessment & reasoning
Embodied learning agents
Zhanel Zhexenova; Aida Amirova; Manshuk Abdikarimova; Kuanysh Kudaibergenov; Nurakhmet Baimakhan; Bolat Tleubayev; Thibault Asselborn; Wafa Johal; Pierre Dillenbourg; Anna CohenMiller; Anara Sandygulova
Frontiers in Robotics and AI · 2020-10-07 · CC BY 4.0
Primary school · Children · stage unspecified
Design requires full-text appraisal
Learning design & outcomes
Supporting AI learning science
Hongling Wu; Xingting Wang
Frontiers in Psychology · 2026-03-09 · CC BY 4.0
Primary school
Survey / observational study
Feedback & scaffolding · Self-regulation & metacognition
Supporting AI learning science
Meng Sun; Jiutong Luo
Journal of Intelligence · 2026-08-01 · CC BY 4.0
Primary school · Children · stage unspecified
Design requires full-text appraisal
Learning design & outcomes
Supporting AI learning science
Ahmet Güneyli; Nazım Serkan Burgul; Sonay Dericioğlu; Nazan Cenkova; Sinem Becan; Şeyma Elif Şimşek; Hüseyin Güneralp
European Journal of Investigation in Health, Psychology and Education · 2024-08-12 · CC BY 4.0
Primary school · Secondary school · Higher education · Adult & professional learning
Survey / observational study
Assessment & reasoning
Supporting AI learning science
Duhong Peng; Junying Feng; Mingfeng Wang
Frontiers in Psychology · 2026-02-03 · CC BY 4.0
Primary school · Children · stage unspecified
Survey / observational study
Assessment & reasoning
Supporting AI learning science
Minsih; Coiriyah Widyasari; Ratnasari Diah Utami; Imam Mujahid; Alfi Laila; Saiful Afandi; Hanum Salsabila
F1000Research · 2026-03-11 · CC BY 4.0
Primary school
Qualitative / mixed methods
Feedback & scaffolding · Motivation & engagement · Accessibility & inclusion
Supporting AI learning science
Rina Putri Noer Fadilah; Rasmi Rikmasari; Saiful Akbar; Arlette Suzy Setiawan
BMC Oral Health · 2025-12-20 · CC BY 4.0
Primary school · Children · stage unspecified
Survey / observational study
Assessment & reasoning
Supporting AI learning science
Haoran Zha; Wenye Li; Weihao Wang; Jian Xiao
Behavioral Sciences · 2025-02-19 · CC BY 4.0
Primary school
Evidence synthesis
Learning design & outcomes
Supporting AI learning science
Soroosh Shalileh; Dmitry Ignatov; Anastasiya Lopukhina; Olga Dragoy
PLOS ONE · 2023-11-22 · CC BY 4.0
Primary school
Design requires full-text appraisal
Accessibility & inclusion
Supporting AI learning science
Muneera A Rasheed; Prem Chand; Saad Ahmed; Hamza Sharif; Zahra Hoodbhoy; Ayat Siddiqui; Babar S Hasan
PLoS ONE · 2021-02-08 · CC BY 4.0
Early childhood · preschool · Primary school · K–12 · mixed / unspecified · Children · stage unspecified
Design requires full-text appraisal
Learning design & outcomes
Supporting AI learning science
Sheng-Yi Wu; Kuay-Keng Yang
Frontiers in Psychology · 2022-06-03 · CC BY 4.0
Primary school
Comparative / pre-post study reported
Motivation & engagement · Assessment & reasoning
Supporting AI learning science
Johan Lundin; Antti Suutala; Oscar Holmström; Samuel Henriksson; Severi Valkamo; Harrison Kaingu; Felix Kinyua; Martin Muinde; Mikael Lundin; Vinod Diwan; Andreas Mårtensson; Nina Linder
PLOS Neglected Tropical Diseases · 2024-04-11 · CC BY 4.0
Primary school · K–12 · mixed / unspecified · Children · stage unspecified
Survey / observational study
Assessment & reasoning
Supporting AI learning science
Seongsoon Kim; Donghyeon Park; Yonghwa Choi; Kyubum Lee; Byounggun Kim; Minji Jeon; Jihye Kim; Aik Choon Tan; Jaewoo Kang
JMIR Medical Informatics · 2018-01-05 · CC BY 4.0
Primary school
Design requires full-text appraisal
Cognitive load & attention
Generative AI education leadership collection · Full leadership collection