AI in Education Research

New studies, surveys and reports on AI in teaching and learning, summarised in plain English with what they found and what they did not. Updated every day.

By Dan Fitzpatrick, teacher, AI education specialist and bestselling author. Last updated .

The latest AI in education research: a preprint posted on 22 October 2026 followed 153 students in a university maths course and linked heavier chatbot use to lower exam scores, while a feedback tool that made students attempt proofs first showed no such link. Other new preprints cover cognitive offloading, AI feedback on handwritten maths and student AI skills. Each summary notes its limits.

We include peer-reviewed papers, preprints, government and agency reports and large surveys from credible organisations. Each summary names the authors or publisher, the date and the sample, and says plainly when a paper has not yet been peer reviewed. Newest first.

22 Oct 2026: AI in education research: CRA report compares four US state models for sharing AI computing with colleges

· Computing Research Association

The Computing Research Association published its report From Vision to Practice on 22 October 2026, written by Erik Deumens, Ian Fisk, Manish Parashar, Barr von Oehsen and Helen Wright. It profiles efforts in Florida, Pennsylvania, Utah and New York to give colleges shared AI computing, and finds that funding, governance and staffing matter as much as hardware.

Why it matters for schools: Universities and trusts planning shared AI infrastructure or training can see that no single model fits every region, and that faculty development and governance need planning from the start.

Limits: A descriptive report on four US state initiatives, not a controlled evaluation, summarised here from the CRA's own bulletin article rather than the full report.

Topics: ai in education research, ai in higher education, ai in education report, ai computing access, nairr, state ai policy

22 Oct 2026: AI in education research: chat logs show an LLM-aided maths submission can hide how much thinking a student did

· arXiv (preprint)

Igor Kontorovich and Miriam Wallach posted a preprint on 22 October 2026 analysing 429 prompts and 59 chat logs from 23 first-year linear algebra students across three homework tasks. Most final answers closely tracked the model's output, even when the logs showed students explaining, conjecturing and checking their own ideas first.

Why it matters for schools: Judging cognitive offloading from finished work alone can mislead, so teachers who want to see student thinking may need process evidence such as chat logs, drafts or a short oral explanation.

Limits: This is a preprint that has not been peer reviewed, and a qualitative study of 23 students in one course illustrates patterns rather than showing how common they are.

Topics: ai in education research, cognitive offloading, ai and critical thinking, maths education ai, student prompts, generative ai students, academic integrity ai, preprint

22 Oct 2026: AI in education research: survey of 313 university students maps how generative AI skills grow

· arXiv (preprint)

Lei Fan and Fangxue Liu posted a preprint on 22 October 2026 combining a retrospective questionnaire of 313 university students with 18 interviews. A third (34.2%) reported fast early gains then a plateau and 28.4% steady gains. Students found judging AI output the hardest skill, and reported gains more often for assignments than exams.

Why it matters for schools: Evaluating AI output is the skill students find hardest, which supports teaching verification explicitly and building it into assignments rather than assuming it develops by itself.

Limits: This is a preprint that has not been peer reviewed, and the figures rely on students' own recollections, so they show perceptions rather than measured learning.

Topics: ai in education research, generative ai students, ai literacy, ai in higher education, student ai skills, ai survey, preprint

21 Oct 2026: AI in education research: interviews with 23 Indian engineering students find AI-free exams sit beside AI-first workplaces

· arXiv (preprint)

Samhitha Goutham and Ziwei Gu posted a preprint on 21 October 2026 reporting interviews with 23 engineering and computing students and recent graduates in India. They describe a gap between exams and campus placement screens that mostly exclude AI and workplaces that expect it, and ask what grades certify when AI can help produce them.

Why it matters for schools: The question applies beyond India: if assessments ban tools that employers expect, leaders need to say what the assessment is meant to prove, and teach permitted AI use explicitly.

Limits: This is a preprint that has not been peer reviewed, and a small qualitative sample of 23 people in India is not representative.

Topics: ai in education research, ai assessment research, ai in higher education, generative ai students, india, ai and jobs, academic integrity ai, preprint

20 Oct 2026: AI feedback research: GPT-5.1 comments on handwritten maths rated useful for practice, but exam gains unproven

· arXiv (preprint)

Arne Vanhoyweghen and colleagues posted a preprint on 20 October 2026 testing GPT-5.1 feedback and practice exercises on handwritten university maths answers, with 343 students in an English cohort and 236 in a Dutch one. Reviewers gave strong ratings to 95.5% of practice exercises, but diagnostic specificity and clarity scored lower, and no exam comparison was significant after adjustment.

Why it matters for schools: AI can draft practice questions from student work at scale, but feedback for the lowest-scoring answers was rated less actionable, so teachers still need to target support at the weakest students.

Limits: This is a preprint that has not been peer reviewed, only some tests used a crossover design, and the authors say feedback effects cannot be separated from attendance and practice.

Topics: ai in education research, ai feedback, ai assessment research, ai marking, maths education ai, llm feedback, ai in higher education, preprint

19 Oct 2026: AI in education research: Indonesian teachers welcome AI for efficiency but understand it poorly

· Aji et al., arXiv

Aji, Amriani, Chevi, Purwarianti and Wijaya posted a survey of Indonesian K-12 teachers to arXiv on 19 October 2026, coding open responses with semi-automated thematic analysis. Teachers broadly accept AI because it saves effort, yet worry about shallower learning and cheating, and many hold misconceptions about how AI works.

Why it matters for schools: Schools rolling out AI tools should pair them with basic AI literacy training, because enthusiasm alone does not mean teachers understand the technology.

Limits: A self-reported survey in one country with the sample size not confirmed from the abstract page; it is an arXiv preprint, listed as accepted at AIED 2026.

Topics: ai in education research, teacher ai literacy, ai misconceptions, k-12 teachers ai survey, indonesia, academic integrity

14 Oct 2026: AI in education research: Gallup and Walton survey finds most teachers using AI say it frees time

· Gallup and Walton Family Foundation

Gallup and the Walton Family Foundation surveyed 1,952 US K-12 educators between 28 July and 14 August 2026 and published on 14 October. About 59% used AI at work in 2025-26. Among users, 64% said it gave them more time for valued work, 7% said it cost time and 29% saw no effect.

Why it matters for schools: Leaders can point to credible national data that AI mostly helps with time, and should aim the saved time at pupil contact rather than extra admin.

Limits: Self-reported views from a Gallup Panel sample of US educators with a 2.7 point margin of error, not a measure of learning, and not peer reviewed.

Topics: ai in education research, teachers using ai, gallup teacher survey, teacher workload ai, k-12 ai adoption, walton family foundation

14 Oct 2026: Preprint proposes a method for simulating individual students to test AI tutors

· arXiv (preprint)

Researchers Yilin Jiang, Fei Tan, Xuanyu Yin, Jing Leng and Aimin Zhou posted a preprint on arXiv on 14 October 2026 describing HACHIMI-II, which models a learner from their history to simulate student behaviour. Across three item-response datasets and two dialogue datasets, it matched real students' ability and reproduced their errors more often than the strongest baseline.

Why it matters for schools: Simulated students could let developers test AI tutoring before it reaches pupils, but schools should wait for evidence from real classrooms before treating any tutor as proven.

Limits: This is an arXiv preprint that has not been peer reviewed, it tests simulation against datasets rather than live pupils, and the abstract page gives no learner counts.

Topics: ai tutoring research, student simulation, llm tutors, ai in education research, preprint, learner modelling

14 Oct 2026: Preprint puts test questions and AI tutoring dialogues on one scale to track what students know

· arXiv (preprint)

Zhangqi Duan, Simon Woodhead, Maya Miller-Vedam and Andrew Lan posted an arXiv preprint on 14 October 2026 proposing one item response theory model for both test questions and tutoring dialogues. On two real-world maths datasets of human-tutored and AI-tutored dialogues, it beat strong baselines at predicting question difficulty and answer correctness.

Why it matters for schools: If it holds up, an AI tutor's conversations could reveal what pupils know without a separate test, but this is early technical work, not evidence that pupils learn more.

Limits: This is an arXiv preprint that has not been peer reviewed, the abstract does not give dataset sizes, and it does not test the method with pupils in live classrooms.

Topics: ai in education research, ai tutoring research, ai tutors, student modelling, item response theory, maths education, preprint

13 Oct 2026: Preprint proposes a cost framework for comparing automated essay scoring systems

· arXiv (Gui)

Yi Gui posted an arXiv preprint on 13 October 2026 arguing that the cost of automated essay scoring should be treated as a measurement design question, not just a speed problem. The paper proposes a four-layer framework covering metering, allocation, unit economics and decision, and a measure called cost per defensible score for comparing systems that meet the same quality requirements.

Why it matters for schools: Exam boards and trusts considering AI marking need to count validation, monitoring and governance as well as licence fees, and this offers a vocabulary for asking suppliers about total cost.

Limits: A conceptual framework in a single-author preprint that arXiv does not peer review, with no empirical sample.

Topics: ai marking, automated essay scoring, ai assessment research, llm grading, ai costs, arxiv preprint

9 Oct 2026: US coalition urges federal investment in research on safe and effective AI in education

· Alliance for Learning Innovation

The Alliance for Learning Innovation, a US education research coalition, published federal policy recommendations on 9 October 2026. It asks Congress and the administration to build the evidence base for safe and effective AI in education, fund research infrastructure, coordinate a unified research agenda and use AI to make federal research spending more transparent.

Why it matters for schools: Leaders weighing AI tools are working with thin evidence, and this paper argues for the research needed to fix that. Education Week, which covered it, cites a Stanford SCALE review in which only 20 of more than 800 K-12 AI papers gave strong causal evidence.

Limits: A policy recommendations paper from an advocacy coalition, not a study, so it reports no new data and no sample.

Topics: ai in education research, ai research funding, us education policy, edtech evidence, ai policy for schools, ai in education report

8 Oct 2026: Preprint finds that optimising an AI tutor against a teaching metric made experts rate it worse

· arXiv

Researchers Daniel Dominguez Figaredo and Rafael Fernandez De la Cruz audited an AI tutor on 2,000 learner scenarios using a new teaching-decision metric. After fine-tuning to raise the score, 31 educators rated the tutor lower, because it repeated one decision. The authors warn against training tutors on a single metric.

Why it matters for schools: Vendors often quote benchmark scores for AI tutors. This work suggests a high score may not mean better teaching, so ask suppliers for evidence from real classrooms and teacher review.

Limits: It is an arXiv preprint (2610.12125) that has not been peer reviewed, and it tests a model and scenarios rather than pupils.

Topics: ai tutoring research, llm tutors, ai tutor evaluation, arxiv preprint, teacher ratings, edtech evidence

7 Oct 2026: AI tutoring research: voice AI practice assigned before class lifts participation

· Wang et al., arXiv

Wang and colleagues ran a preregistered field experiment with 759 MBA students across ten sections of one course, posted to arXiv on 7 October 2026. Students prepared for some sessions with a voice-based AI discussion partner. After two sessions they made roughly 31% more voluntary contributions in class.

Why it matters for schools: Teachers can try short AI rehearsal tasks before discussion lessons to help hesitant pupils speak, without replacing the class conversation.

Limits: One course at one business school with adult learners; an arXiv preprint with no journal reference.

Topics: ai in education research, ai tutoring research, voice ai, class participation, field experiment, mba students

7 Oct 2026: Preprint compares AI marking with teachers on 3,041 computer science answers

· arXiv

Xi Zhao, Xinyue Jiao and Zhen Xu compared three commercial AI models with human instructors on 3,041 student answers to 50 computer science questions. The AI feedback spotted misconceptions in about 5 to 7% of answers against 15 to 31% for teachers, stayed positive whatever the quality, and was most reliable on procedural topics.

Why it matters for schools: AI marking can look detailed while adding little diagnostic insight. Teachers should keep control of feedback that shapes teaching, and treat AI scores as a first pass rather than a verdict.

Limits: It is an arXiv preprint (2610.09460), not yet peer reviewed, based on one university computer science dataset.

Topics: ai marking, ai feedback, ai assessment research, llm grading, higher education, arxiv preprint

5 Oct 2026: Preprint finds expert-checked AI study materials lifted marks most for the weakest university students

· arXiv (Dang and Nguyen)

Canh Thien Dang and An Nguyen posted an arXiv preprint on 5 October 2026 comparing two cohorts of a compulsory first-year economics course. One cohort received AI-generated podcasts, FAQs and quizzes that a graduate teaching assistant had checked first. Access was linked to a 2.34-mark advantage on a 50-mark component, with about three-quarters of the average effect coming from the bottom fifth of students.

Why it matters for schools: It suggests that AI study materials checked by a teacher may help struggling learners more than strong ones, so verification before release could matter as much as the tool itself.

Limits: A preprint not yet peer reviewed, from one course with 170 students and 340 exam marks, using a two-cohort difference-in-differences design, so causal claims are cautious.

Topics: ai study materials, ai in higher education, ai learning gains, expert verified ai, arxiv preprint, economics course, ai tutoring research

5 Oct 2026: EEF trial finds Oak's Aila cut primary planning time by about 49 minutes a week

· Education Endowment Foundation

The Education Endowment Foundation published an NFER-run randomised trial of Oak National Academy's Aila lesson planning tool with Year 3 to 6 teachers in England. Over ten weeks in autumn 2025, Aila users spent about 2.5 hours a week on preparation against 3.3 hours for the comparison group, and the resources were judged no worse in quality.

Why it matters for schools: This is a realistic, independent benchmark for AI planning tools. The gains were largest for early-career and less confident teachers, so leaders could target support there instead of relying on vendor claims.

Limits: Aila was used in only about 7% of lessons in an average week and use fell during the trial, both groups could use other AI tools, and the EEF page gives differing sample figures.

4 Oct 2026: Preprint interviews 42 undergraduates in China, Japan and Mongolia about generative AI use

· arXiv (Li, Xie and colleagues)

Ming Li, Qin Xie and colleagues posted an arXiv preprint on 4 October 2026 based on interviews with 42 undergraduates at one university each in China, Japan and Mongolia. Students used generative AI to explore, produce and refine academic work, chose tools by language and access, and judged them on accuracy, privacy, dependence and effects on critical thinking.

Why it matters for schools: Having access to a tool does not make it equally useful for every student, and language support matters. Schools with multilingual pupils should check how well AI tools work in pupils' home languages.

Limits: A preprint not yet peer reviewed; a small qualitative study of 42 students at three universities, so it describes experiences rather than measuring learning.

Topics: generative ai students, multilingual ai, ai equity, university students ai use, arxiv preprint, china japan mongolia, critical thinking ai

Oct 2026: Cambridge interviews 15 early-adopter English teachers in 13 countries about AI policy

· Cambridge University Press & Assessment

Cambridge University Press & Assessment published a paper by Evelina Galaczi and colleagues, based on interviews with 15 English language teachers and trainers in 13 countries who were early adopters of generative AI. It sets out eight recommendations, including involving teachers in AI decisions, sustained training, critical AI literacy and assessment that values process.

Why it matters for schools: It works as a checklist for school AI policy, arguing for teacher voice, protected training time and assessment that rewards the learning process rather than a blanket ban or unchecked adoption.

Limits: The sample is 15 self-described early adopters interviewed in early 2025, the work is qualitative, and it does not measure effects on pupil learning.

Oct 2026: EDUCAUSE Top 10 puts finding real value from AI first for 2027

· EDUCAUSE

EDUCAUSE released its 2027 Top 10 issues for higher education technology around its annual conference at the start of October 2026. For the first time an AI item leads the list: working out where AI adds real value. Four of the ten issues concern AI. The list was shaped by a panel of 25 leaders and a member vote with 882 complete responses.

Why it matters for schools: University technology leaders are moving from trying AI tools to asking which ones serve their mission. School and college leaders can borrow the same test and ask for evidence of value before renewing AI licences.

Limits: This is a ranking of priorities from a member survey held between 15 May and 1 June 2026, not a study of whether AI improves learning.

30 Sep 2026: Randomised trial finds university AI tutor access lowered grades

· EdWorkingPapers

A University of Maryland team led by Jing Liu published a working paper on 30 September on a randomised trial with 2,379 undergraduates and 30 instructors in autumn 2025. Sections given an AI study assistant saw final grades fall by 0.37 standard deviations, and course platform activity dropped. Only about 15% of students used it, and most requests asked for direct answers.

Why it matters for schools: How an AI tutor is set up matters more than whether it is available. Tools left in a default answer-giving mode may replace effort rather than support it, so teachers should switch on tutoring modes and teach pupils how to ask for feedback.

Limits: This is a single-university working paper that has not been peer reviewed, it measured course grades rather than an independent test, and only 18% of students answered the survey.

30 Sep 2026: Harvard report finds AI suspicion is eroding trust between teachers and students

· Harvard Graduate School of Education

The Center for Digital Thriving at Harvard Graduate School of Education published An AI Policy Isn't a Playbook on 30 September 2026. It combines a nationally representative survey of over 1,000 US public school teachers and principals with interviews with 12 educators and 31 people aged 15 to 19. Of staff facing AI dilemmas, 74% of teachers and 69% of principals described cheating concerns.

Why it matters for schools: The authors describe two-way suspicion, where neither side can tell who used AI, and suggest teachers act less as referees and more as coaches through questions and reflection. Leaders revising AI policies can use this to keep detection from crowding out the conversations that show what pupils understand.

Limits: This is a report rather than a peer-reviewed paper, the survey dates from spring 2025, and the interview sample is small and US-only.

Aug 2026: Two-year trial finds Khanmigo AI tutor gave small maths gains in Tennessee

· NBER

Economists Philip Oreopoulos and Nina Low released an NBER working paper in August 2026 on a two-year randomised trial in 18 middle schools in Hamilton County, Tennessee. Using Khanmigo in remedial maths sessions raised scores by about 1.3 national percentile ranks per term. Although 96% of pupils tried the AI tutor, most of their messages were bare answers or clicks on suggested prompts.

Why it matters for schools: Giving pupils access to an AI tutor is not the same as getting them to use it well. Schools buying tutoring tools should plan how teachers will model and check meaningful use, and expect modest gains rather than transformation.

Limits: A working paper that has not yet been peer reviewed, covering one US district and remedial maths only, with 53 randomised grade-level groups and 6,902 student-term observations.

Aug 2026: Fraser Institute survey finds 68% of Canadian teachers use AI, with many unsure about student use

· Fraser Institute

The Fraser Institute published a Leger survey of 756 Canadian teachers of Grade 6 and above, fielded between November 2025 and January 2026. It found 67.7% use AI in teaching, mostly for lesson plans, and 75.1% of those users say it makes them more effective. Teachers also reported concern about pupils using AI to do their work.

Why it matters for schools: The training figures stand out: 63.6% had no training on teaching students to use AI sensibly. School leaders could prioritise staff development on student use, not only on planning tools.

Limits: It is a non-probability online panel survey of self-reported views, weighted by age, gender and region, and it shows correlation rather than cause.

Topics: teacher ai survey, ai in education research, canada, ai teacher training, ai homework, generative ai in schools

22 Sep 2026: Preprint offers an auditable way to code classroom dialogue with AI, revised on 13 Oct

· arXiv (preprint)

Julian Bernado, Ana Trindade Ribeiro, Xander Beberman and Susanna Loeb posted the EduBehaviors preprint on arXiv on 22 September 2026, revised on 13 October. It combines generative AI with encoders and machine learning to label educational dialogue in a checkable way. On the TalkMoves dataset the best set-up reached a macro-F1 of 0.764.

Why it matters for schools: Trusts and researchers judging AI tools that analyse classroom talk need labelling they can check, and this work releases an open-source toolkit that shows one way.

Limits: This is an arXiv preprint with no peer review stated, it was tested on one dataset, and the abstract does not report how many transcripts or utterances were used.

Topics: ai in education research, llm classroom dialogue, educational dialogue analysis, talkmoves, teacher talk, auditable ai, preprint

21 Sep 2026: AI tutoring research: tutor structure improved learning, but voice versus text made no difference

· Yang, Van Alstyne and Dellarocas, arXiv

Yang, Van Alstyne and Dellarocas ran a preregistered randomised experiment with 86 online MBA students, posted to arXiv on 21 September 2026. A structured AI tutor beat a holdout using free consumer AI by 6.63 points, mostly in written reasoning. Voice versus text made no learning difference, and voice cost 2.8 times more.

Why it matters for schools: When choosing AI tutors, prioritise pedagogical design over voice features, which pupils may like but which did not improve learning here.

Limits: A small sample in one graduate finance course; an arXiv preprint with no peer-review status shown.

Topics: ai in education research, ai tutoring research, structured ai tutor, voice versus text ai, randomised experiment, pedagogical design

18 Sep 2026: AI in education research: ChatGPT lifted coding scores but students remembered less and felt less ownership

· Bergh et al., arXiv

Bergh, Tag, Vassar and Renzella randomised 55 analysed computer science undergraduates to ChatGPT or web search for three C programming tasks, posting on 18 September 2026. The ChatGPT group scored higher (89% versus 69%) but recalled less, including after 48 hours (39% versus 52%), and claimed less ownership of their code.

Why it matters for schools: Programming teachers should expect strong task performance with AI to mask weaker retention, and may want recall checks or AI-free practice.

Limits: A small between-subjects lab study of 55 students with heavy loss of physiological data; an arXiv preprint not confirmed as peer reviewed.

Topics: ai in education research, chatgpt programming students, learning retention ai, cognitive offloading, computer science education, controlled experiment

18 Aug 2026: Four in five UK teenagers now use generative AI, National Literacy Trust finds

· National Literacy Trust

The National Literacy Trust published findings on 18 August from 40,543 young people aged 13 to 18, 2,567 teachers and 3,073 parents in the UK. It found 79% of young people and 81% of teachers use generative AI, up from 58% of teachers in 2025. About 24% of pupils said they copy AI output for homework, and only 33% of teachers had any AI training.

Why it matters for schools: Teacher use has outpaced training, and copying was more common among pupils on free school meals. Schools could prioritise staff training and teach pupils explicitly how to check and build on AI output, with particular attention to disadvantaged pupils.

Limits: The data is self-reported, the report page does not describe sampling or weighting, and it measures use and attitudes rather than effects on learning.

18 Aug 2026: Common Sense Media survey finds 70% of US teens use AI for schoolwork

· Common Sense Media

Common Sense Media published a nationally representative survey of more than 1,000 US teenagers on 18 August 2026. It found 70% use AI for schoolwork and 63% of users get answers directly from it, while only 27% said a teacher had talked to them about using AI safely. Of users whose school blocked AI tools, 59% switched to personal devices.

Why it matters for schools: Blocking tools on school networks appears to move use onto personal devices rather than stop it. Teaching pupils when AI helps learning and when it replaces thinking is likely to do more than filters alone.

Limits: A self-reported survey of US teens by an advocacy organisation, and the summary page gives no fieldwork dates or margin of error.

23 Jul 2026: Preprint on 138,386 students finds small effects of generative AI on university grades

· arXiv (preprint)

James Dumlao, Meng Wang, Misha Teplitskiy and colleagues posted an arXiv preprint on 23 July 2026, revised on 14 October, using data on 138,386 students and 72,730 course offerings from 2016 to 2025. Comparing courses before and after ChatGPT's release, they found small effects on grades and satisfaction, depending on how COVID-19 is modelled.

Why it matters for schools: It tempers fears that generative AI has inflated university grades, but it covers one large university, not schools, so sixth-form and college leaders should read it as context only.

Limits: This is an arXiv preprint with no peer review stated, from a single university, and it estimates changes after ChatGPT's release rather than measuring each student's AI use.

Topics: ai in education research, generative ai and grades, chatgpt university study, ai in higher education, grade inflation, ai assessment, preprint

9 Jul 2026: Randomised experiment finds AI used as a tutor kept learning gains while AI-written essays lost them

· arXiv (Contractor and Reyes)

Zara Contractor and German Reyes posted an arXiv preprint on 9 July 2026, updated on 4 October, reporting a proctored in-person randomised experiment with undergraduates. Students learned an unfamiliar topic and wrote an essay with or without generative AI. AI access raised test scores and part of the gain lasted a week, but only for students who used AI as a tutor rather than having it write for them.

Why it matters for schools: It supports setting AI tasks where pupils explain, question and practise instead of receiving finished text, because the same tool produced lasting gains or none depending on how it was used.

Limits: A preprint that arXiv does not peer review, with undergraduates rather than school pupils and no sample size given on the abstract page.

Topics: ai tutoring research, generative ai students, ai learning gains, randomised controlled trial, ai in higher education, arxiv preprint, cognitive offloading

24 Jun 2026: Microsoft AI in Education Report finds 88% of educators have used AI for school

· Microsoft

Microsoft published its third annual AI in Education Report on 24 June, based on a PSB Insights survey of 3,345 students, educators and leaders in six countries. It found 88% of educators and 92% of students and leaders have used AI for school, yet 53% of educators and 77% of students had no formal AI training.

Why it matters for schools: Use is ahead of training, and about 41% of students and 42% of educators name academic integrity as a leading worry. Training and clear assessment rules look like the gaps to close.

Limits: It is vendor-commissioned survey research with self-reported answers, covering the US, UK, Australia, Brazil, Japan and Saudi Arabia.

Topics: ai in education report, ai adoption survey, educator ai training, academic integrity ai, psb insights, microsoft education

Frequently asked questions

Where can I find the latest research on AI in education?

This page summarises new studies, surveys and reports on AI in teaching and learning, newest first, with a link to each original paper or report.

What is a preprint, and can I rely on it?

A preprint is a paper shared publicly before peer review. It can be valuable early evidence, but its methods and conclusions have not yet been checked by independent reviewers, so treat findings as provisional. We say clearly when an item is a preprint.

How should school leaders use AI research?

Look at the sample size, the setting and the design before applying a finding. A large randomised trial in a similar setting is stronger evidence than a small survey, and one study rarely settles a question on its own.

Does AI tutoring improve learning?

The evidence is mixed. Some studies find gains when a tutor makes students attempt the work first, while others find lower exam scores when students lean on chatbots for answers. Subject, age and design matter, so check the sample size and whether the study is peer reviewed.

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