Artificial intelligence (AI) could help teachers identify pupils with potential learning difficulties such as dyslexia or Attention Deficit Hyperactivity Disorder (ADHD).

The study published in Learning and Instruction found that AI could enhance teachers’ ‘diagnostic reasoning’, which is the ability to collect and assess evidence about a pupil, and draw appropriate conclusions so they can be given tailored support.

During the trial, trainees were asked to assess six fictionalised ‘simulated’ pupils with potential learning difficulties. They were given examples of their schoolwork, as well as other information such as behaviour records and transcriptions of conversations with parents. They then had to decide whether or not each pupil had learning difficulties such as dyslexia or ADHD, and explain their reasoning.

Riikka Hofmann, Associate Professor at the Faculty of Education, University of Cambridge, said: “Teachers play a critical role in recognising the signs of disorders and learning difficulties in pupils and referring them to specialists. Unfortunately, many of them also feel that they have not had sufficient opportunity to practise these skills. The level of personalised guidance trainee teachers get on German courses is different to the UK, but in both cases it is possible that AI could provide an extra level of individualised feedback to help them develop these essential competencies.”

AI-generated feedback helped trainee teachers identify learning difficulties

The study, with 178 trainee teachers in Germany, was carried out by a research team led by academics at the University of Cambridge and Ludwig-Maximilians-Universität München (LMU Munich). 

Immediately after submitting their answers, half of the trainees received a prototype ‘expert solution’, written in advance by a qualified professional, to compare with their own. This is typical of the practice material student teachers usually receive outside taught classes. The others received AI-generated feedback, which highlighted the correct parts of their solution and flagged aspects they might have improved.

After completing the six preparatory exercises, the trainees then took two similar follow-up tests – this time without any feedback. The tests were scored by the researchers, who assessed both their ‘diagnostic accuracy’ (whether the trainees had correctly identified cases of dyslexia or ADHD), and their diagnostic reasoning: how well they had used the available evidence to make this judgement.

The average score for diagnostic reasoning among trainees who had received AI feedback during the six preliminary exercises was an estimated 10 percentage points higher than those who had worked with the pre-written expert solutions.

Dr Michael Sailer, from LMU Munich, said: “Obviously we are not arguing that AI should replace teacher-educators: new teachers still need expert guidance on how to recognise learning difficulties in the first place. It does seem, however, that AI-generated feedback helped these trainees to focus on what they really needed to learn. Where personal feedback is not readily available, it could be an effective substitute.”

The study used a natural language processing system: an artificial neural network capable of analysing human language and spotting certain phrases, ideas, hypotheses or evaluations in the trainees’ text.