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.
34 matching papers. Design labels are screening aids; verify methods before drawing causal conclusions. Clear filters
Embodied learning agents
Rinat B. Rosenberg-Kima; Yaacov Koren; Goren Gordon
Frontiers in Robotics and AI · 2020-01-10 · CC BY 4.0
Higher education
Qualitative / mixed methods
Learning design & outcomes
Embodied learning agents
Kevin Sandeman; Sami Blom; Ville Koponen; Anniina Manninen; Juuso Juhila; Antti Rannikko; Tuomas Ropponen; Tuomas Mirtti
Diagnostics · 2022-04-20 · CC BY 4.0
Higher education
Design requires full-text appraisal
Learning design & outcomes
Embodied learning agents
Georgios Karalekas; Stavros Vologiannidis; John Kalomiros
Sensors (Basel, Switzerland) · 2020-04-27 · CC BY 4.0
Secondary school
Design requires full-text appraisal
Learning design & outcomes
Embodied learning agents
Alyssa M. Alcorn; Eloise Ainger; Vicky Charisi; Stefania Mantinioti; Sunčica Petrović; Bob R. Schadenberg; Teresa Tavassoli; Elizabeth Pellicano
Frontiers in Robotics and AI · 2019-11-01 · CC BY 4.0
Children · stage unspecified
Evidence synthesis
Motivation & engagement · Accessibility & inclusion
Embodied learning agents
Tony Belpaeme; Paul Vogt; Rianne van den Berghe; Kirsten Bergmann; Tilbe Göksun; Mirjam de Haas; Junko Kanero; James Kennedy; Aylin C Küntay; Ora Oudgenoeg-Paz; Fotios Papadopoulos; Thorsten Schodde; Josje Verhagen; Christopher D Wallbridge; Bram Willemsen; Jan de Wit; Vasfiye Geçkin; Laura Hoffmann; Stefan Kopp; Emiel Krahmer; Ezgi Mamus; Jean-Marc Montanier; Cansu Oranç; Amit Kumar Pandey
International Journal of Social Robotics · 2018-01-25 · CC BY 4.0
Children · stage unspecified
Survey / observational study
Learning design & outcomes
Embodied learning agents
Wing-Chee So; Miranda Kit-Yi Wong; Wan-Yi Lam; Chun-Ho Cheng; Jia-Hao Yang; Ying Huang; Phoebe Ng; Wai-Leung Wong; Chiu-Lok Ho; Kit-Ling Yeung; Cheuk-Chi Lee
Molecular Autism · 2018-05-24 · CC BY 4.0
Early childhood · preschool · Children · stage unspecified
Randomized study reported
Cognitive load & attention · Retention & transfer · Assessment & reasoning · Accessibility & inclusion
Embodied learning agents
Joachim de Greeff; Tony Belpaeme
PLoS ONE · 2015-09-30 · CC BY 4.0
Children · stage unspecified
Design requires full-text appraisal
Learning design & outcomes
Embodied learning agents
Alexander Borg; Jonathan Schiött; William Ivegren; Cidem Gentline; Viking Huss; Anna Margareta Hugelius; Benjamin Jobs; Mini Ruiz; Samuel Edelbring; Carina Georg; Gabriel Skantze; Ioannis Parodis
JMIR Medical Education · 2026-03-25 · CC BY 4.0
Higher education
Randomized study reported
Feedback & scaffolding · Assessment & reasoning
Embodied learning agents
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
Embodied learning agents
Alexander Borg; Jonathan Schiött; William Ivegren; Cidem Gentline; Viking Huss; Anna Hugelius; Benjamin Jobs; Fabricio Espinosa; Mini Ruiz; Samuel Edelbring; Carina Georg; Gabriel Skantze; Ioannis Parodis
Journal of Medical Internet Research · 2025-11-27 · CC BY 4.0
Higher education
Qualitative / mixed methods
Retention & transfer · Motivation & engagement · Assessment & reasoning
Generative AI education leadership collection · Full leadership collection