What Is The Current Evidence Into AI Tutoring And The Impact On Learners In School?

June 3, 2026

Is AI tutoring a transformative way to scale up one to one support in school? The government thinks so. But the truth is, up until very recently, there’s been little evidence specifically around AI tutoring. As you’d expect this is changing fast and research is now under way into the efficacy of different models of AI tutoring.

Here we look at what we already know from the research about effective tutoring, and to what degree that is now being replicated and evidenced in AI tutoring.

For a longer piece addressing critics of AI tutoring — and the equity case for the DfE pilot — see our companion response to the debate.

Key takeaways

  • One to one tutoring is one of the most effective interventions available to schools – the challenge has always been delivering it at scale and at an affordable cost.
  • The research on what makes tutoring effective is clear: high-dosage, curriculum-aligned, conversational programmes with human oversight produce the strongest gains.
  • Generic AI tools used for studying can harm learning – one RCT found students using ChatGPT performed around 17% worse in exams.
  • Purpose-built AI tutoring systems designed around proven tutoring principles are showing measurable within-session learning gains.
  • The DfE’s generative AI safety standards and the research evidence now point in the same direction – progressive disclosure, human oversight, and curriculum alignment are both regulatory requirements and markers of effective design.
  • School leaders should evaluate any AI tutoring system against five practical questions before adopting.

The DfE’s stance on AI tutoring

In 2025, the Department for Education announced plans to trial AI tutoring tools with up to 450,000 disadvantaged students by 2027. This is one of the most significant EdTech commitments any government has made. The ambition is to close the achievement gap for students from disadvantaged backgrounds who cannot access private tutors.

The evidence base on which this decision was made is clear – one to one tutoring is one of the most effective and well tested methods at closing the achievement gap for students. Until now, however, the challenge has been implementing one to one tutoring at scale and at an affordable cost. Using AI tutoring instead of traditional tutors makes this a real possibility.

Defining AI tutoring

One of the issues we have when looking at AI tutoring evidence is that the term currently embraces a few different forms of AI assisted instruction, from homework help chatbot type tutoring to adaptive learning systems that personalise the content delivered based on assessment of learning needs. These artificial intelligence tools use generative AI to support students who, crucially, have high self-regulation and are seeking the help to improve.

Our approach at Third Space Learning has been slightly different. We define AI tutoring very simply as seeking to replicate as closely as possible all those elements that have made traditional tutoring so effective at closing the attainment gap for all students, particularly those who have struggled with maths or those who are less likely to find their own routes to improvement.

We know what makes traditional tutoring effective. Our challenge as educators is to create an AI tutoring model built on the same principles and best practices.

What makes traditional tutoring effective

The evidence on what makes tutoring effective is well established, and it is the foundation for evaluating any AI tutoring system.

The EEF identifies one-to-one and small-group tutoring as one of the most effective interventions available to schools, with an average impact of five additional months’ progress. The characteristics that drive those gains are consistent across the research:

  • High-dosage programmes
  • One-to-one delivery
  • Curriculum alignment
  • Human oversight throughout

There is also a practical constraint that the research addresses directly. Human tutoring cannot scale without quality dropping. As programmes grow, impact tends to diminish – finding, training and retaining enough high-quality human teachers is the binding constraint.

The promise of AI tutoring is not that it replaces human tutoring, but that it may be able to deliver the conversational, structured, curriculum-aligned support the evidence calls for – at a scale human programmes have not been able to sustain.

What effective AI tutoring needs to look like

Given what the evidence tells us about effective tutoring, the requirements for AI tutoring become clear. An AI tutoring system needs to do more than deliver personalised content. It needs to hold a conversation.

Adaptive learning platforms have personalised content pathways for years. But they are fundamentally non-conversational – they lack the reasoning and feedback loop that makes human tutoring effective. A student working through an adaptive platform is receiving individualised instruction, but they are not being tutored.

On-demand chat tools present a different problem. They can converse, but they require students to self-regulate – to know what to ask, when to push deeper, and when to stop. Effective AI tutoring systems need to meet the same bar as effective human tutoring. They should be conversational, structured, curriculum-aligned, and built around human expertise. They need to scaffold reasoning rather than supply answers, sustain productive struggle rather than shortcut it, and keep teachers at the centre of the design.

Where the AI tutoring research stands

The one to one tutoring evidence is settled. The AI-specific evidence is not, and school leaders need to understand why.

Much of the scepticism around AI tutoring stems from studies examining generic large language models in educational settings, not purpose-built tutoring systems designed around the principles the evidence calls for. Most studies look at students using generative AI in self-directed settings, often with no deliberate curriculum alignment. The research that does exist, however, is beginning to point in a consistent direction: when AI tutoring is designed around the same principles that make human tutoring effective, it can produce measurable learning gains.

Why the evidence on AI tutoring is still developing

Rigorous peer-reviewed academic research typically takes around 18 months from design to publication. In a field moving as fast as generative AI, most research is already outdated by the time it reaches schools. That alone explains why emerging evidence on AI tutoring remains thin, but the quality of the research that does exist is an equally significant problem.

Most studies on AI and student learning are methodologically weak. Many allow students to use the AI tool in the final assessment itself, which tells you nothing meaningful about whether real learning gains have taken place.

Where carefully designed AI tutoring systems show promise

Not all the evidence points to failure. When AI tutoring is built with careful engineering and grounded in proven pedagogical best practices, the results look very different. What matters is that this tutoring system was built to facilitate active engagement, foster advanced cognitive skills and manage cognitive load rather than optimise for task completion.

Evidence of within-session learning gains: Educate Ventures Research evaluation of Skye

The most credible AI tutoring evidence is independent evidence, not vendor-produced data. Educate Ventures Research found individual students improved from 34% accuracy on diagnostic check-in questions to 92% on check-out assessments within a single session. A substantial within-session learning gain.

Students also rated the learning experience with AI maths tutor Skye positively across multiple dimensions:

  • Helpful for understanding maths (3.45/5)
  • Enjoyable to use (3.37/5)
  • Helpful in preparing for SATs (3.57/5)

5 questions every school leader should ask before adopting AI tutoring

  1. Does it guide students through their thinking, or does it provide direct answers?
  2. Is human expertise comprehensively built into the design?
  3. Does it meet the DfE’s Generative AI Product Safety Expectations?
  4. Is there independent, third-party evidence of impact on student learning and student outcomes?
  5. Is the AI tutor designed specifically for your students’ age group, curriculum and educational settings?

The verdict on AI tutoring: what the evidence actually says

Where AI tutoring has been built to guide students, manage cognitive load and keep human expertise at the centre, the emerging evidence shows real improvements in student learning and improved learning outcomes. The path forward is not about whether the technology is ready. It is about whether the tutoring systems schools adopt have been designed with the same rigour the evidence demands.