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

3,639 / 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

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

Supporting AI learning science

Case-based Learning and Artificial Intelligence-based Gamification to Improve Undergraduate Students’ Motivation for One Health and Climate Change: A Pilot Study

Cihan Papan; Esther Sib; Christiane Schreiber; Alexander D. Wollkopf; Marek Landsberg; Steffen Engelhart; Katharina Last; Timo Falkenberg; Carsten Felder; Dogus Darici; Tobias Raupach; Nico T. Mutters

Medical Science Educator · 2026-04-06 · CC BY 4.0

Higher education
Design requires full-text appraisal

Motivation & engagement

Read sourceActionable companionPaper agent

Supporting AI learning science

Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan

Weam Mohamed Meargni Ahmed; Malaz M Abdalmotalib; Mohamed H Elbadawi; Galia Tajelsir Fadulelmula Mohammed; Waad Mohamed Ibrahim Mohamed; Fatima Salih Babiker Mohammed; Hajar Saad Salih; Hiba Omer Yousif Mohamed

PLOS One · 2025-05-23 · CC BY 4.0

Higher education
Survey / observational study

Assessment & reasoning

Read sourceActionable companionPaper agent

Supporting AI learning science

Higher education students’ perceptions of ChatGPT: A global study of early reactions

Dejan Ravšelj; Damijana Keržič; Nina Tomaževič; Lan Umek; Nejc Brezovar; Noorminshah A Iahad; Ali Abdulla Abdulla; Anait Akopyan; Magdalena Waleska Aldana Segura; Jehan AlHumaid; Mohamed Farouk Allam; Maria Alló; Raphael Papa Kweku Andoh; Octavian Andronic; Yarhands Dissou Arthur; Fatih Aydın; Amira Badran; Roxana Balbontín-Alvarado; Helmi Ben Saad; Andrea Bencsik; Isaac Benning; Adrian Besimi; Denilson da Silva Bezerra; Chiara Buizza; Roberto Burro; Anthony Bwalya; Cristina Cachero; Patricia Castillo-Briceno; Harold Castro; Ching Sing Chai; Constadina Charalambous; Thomas K F Chiu; Otilia Clipa; Ruggero Colombari; Luis José H Corral Escobedo; Elísio Costa; Radu George Crețulescu; Marta Crispino; Nicola Cucari; Fergus Dalton; Meva Demir Kaya; Ivo Dumić-Čule; Diena Dwidienawati; Ryan Ebardo; Daniel Lawer Egbenya; MoezAlIslam Ezzat Faris; Miroslav Fečko; Paulo Ferrinho; Adrian Florea; Chun Yuen Fong; Zoë Francis; Alberto Ghilardi; Belinka González-Fernández; Daniela Hau; Md Shamim Hossain; Theo Hug; Fany Inasius; Maryam Jaffar Ismail; Hatidža Jahić; Morrison Omokiniovo Jessa; Marika Kapanadze; Sujita Kumar Kar; Elham Talib Kateeb; Feridun Kaya; Hanaa Ouda Khadri; Masao Kikuchi; Vitaliy Mykolayovych Kobets; Katerina Metodieva Kostova; Evita Krasmane; Jesus Lau; Wai Him Crystal Law; Florin Lazăr; Lejla Lazović-Pita; Vivian Wing Yan Lee; Jingtai Li; Diego Vinicio López-Aguilar; Adrian Luca; Ruth Garcia Luciano; Juan D Machin-Mastromatteo; Marwa Madi; Alexandre Lourenço Manguele; Rubén Francisco Manrique; Thumah Mapulanga; Frederic Marimon; Galia Ilieva Marinova; Marta Mas-Machuca; Oliva Mejía-Rodríguez; Maria Meletiou-Mavrotheris; Silvia Mariela Méndez-Prado; José Manuel Meza-Cano; Evija Mirķe; Alpana Mishra; Ondrej Mital; Cristina Mollica; Daniel Ionel Morariu; Natalia Mospan; Angel Mukuka; Silvana Guadalupe Navarro Jiménez; Irena Nikaj; Maria Mihaylova Nisheva; Efi Nisiforou; Joseph Njiku; Singhanat Nomnian; Lulzime Nuredini-Mehmedi; Ernest Nyamekye; Alka Obadić; Abdelmohsen Hamed Okela; Dorit Olenik-Shemesh; Izabela Ostoj; Kevin Javier Peralta-Rizzo; Almir Peštek; Amila Pilav-Velić; Dilma Rosanda Miranda Pires; Eyal Rabin; Daniela Raccanello; Agustine Ramie; Md Mamun ur Rashid; Robert A P Reuter; Valentina Reyes; Ana Sofia Rodrigues; Paul Rodway; Silvia Ručinská; Shorena Sadzaglishvili; Ashraf Atta M S Salem; Gordana Savić; Astrid Schepman; Samia Mokhtar Shahpo; Abdelmajid Snouber; Emma Soler; Bengi Sonyel; Eliza Stefanova; Anna Stone; Artur Strzelecki; Tetsuji Tanaka; Carolina Tapia Cortes; Andrea Teira-Fachado; Henri Tilga; Jelena Titko; Maryna Tolmach; Dedi Turmudi; Laura Varela-Candamio; Ioanna Vekiri; Giada Vicentini; Erisher Woyo; Özlem Yorulmaz; Said A S Yunus; Ana-Maria Zamfir; Munyaradzi Zhou; Aleksander Aristovnik

PLOS ONE · 2025-02-05 · CC BY 4.0

Higher education
Design requires full-text appraisal

Assessment & reasoning

Read sourceActionable companionPaper agent

Conversational & pedagogical agents

Attitudes of medical and life sciences university students and postdoctoral fellows toward AI chatbots in education: an international cross-sectional survey

Jeremy Y Ng; Aimun Qadeer Shah; Emi Roni; Madiha Asna; Jasleen Brar; Sylvia Kathirkamanathan; Wendy Li; Cynthia Lokker; Alfonso Iorio; R Brian Haynes; David Moher

Scientific Reports · 2026-03-14 · CC BY 4.0

Higher education
Survey / observational study

Learning design & outcomes

Read sourceActionable companionPaper agent

Embodied learning agents

AI-Enhanced Social Robotic Versus Computer-Based Virtual Patients for Clinical Reasoning Training in Medical Education: Observational Crossover Cohort Study

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

Read sourceActionable companionPaper agent

Supporting AI learning science

Navigating opportunities and challenges of generative AI in higher education: towards responsible, equitable, and human-centered integration

Claudiu Coman; Vasile Gherheș; Anna Bucs; Ecaterina Coman; Cioca Victoria-Rodica; Diana-Cristina Bódi; Raluca-Maria Șerbănescu

Frontiers in Artificial Intelligence · 2026-03-23 · CC BY 4.0

Higher education
Qualitative / mixed methods

Feedback & scaffolding · Self-regulation & metacognition

Read sourceActionable companionPaper agent

Generative AI education leadership collection · Full leadership collection