AI wrote strong maths practice exercises—but did it improve exam results?
Six reviewers rated most generated exercises positively, but the Belgian crossover study found no statistically significant adjusted exam advantage in either language cohort.
The short version
- What happened
- The study followed 343 English-course and 236 Dutch-course students taught by the same professor, with a crossover between AI-feedback and conventional-feedback sessions.
- Who it affects
- Students may receive rapid, individualized practice, but an unchecked mathematical error can teach the wrong method with persuasive wording.
- What remains unproven
- This is an unreviewed, non-randomized study at one Belgian university; attendance and feedback use were self-selected, and the language cohorts differed in ways that complicate comparison.
Keep in mind
This is an unreviewed, non-randomized study at one Belgian university; attendance and feedback use were self-selected, and the language cohorts differed in ways that complicate comparison.
Source analysis · 1 linked source. Primary analysed source date: 20 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
Reviewers liked the practice tasks more than every explanation
Belgian researchers tested a system that reads handwritten mathematics and returns a new practice exercise with feedback. Six reviewers assessed 157 messages twice, producing 314 ratings. The exercise itself received the strongest rating in 300 cases, or 95.5%. Mathematical accuracy was rated strongest in 195 cases, while 26 ratings, or 8.3%, identified a deficiency. The gap matters: a useful-looking task can still contain reasoning that needs checking.
The classroom study involved 343 students in an English-language course and 236 in a Dutch-language course taught by the same professor. For the first feedback session, 331 English-course and all 236 Dutch-course students were observed. In the crossover analysis, 199 English-course and 147 Dutch-course students took part in both conditions, receiving AI feedback in one session and conventional feedback in another.[1]
The exam analysis did not establish a learning gain
The researchers compared later exam scores for four combinations of course language and feedback sequence, adjusting for earlier marks and several student characteristics. None of the four estimated differences was statistically significant. Attendance and engagement were associated with stronger results in some analyses, but students were not randomly assigned to attend or use feedback, so those patterns do not establish causation.
For teachers, the sensible use is formative support with review: check the mathematics, let students question the explanation and keep consequential grading separate. The cohorts differed in background and teaching-assistant arrangements, the study came from one institution and the preprint is unreviewed. A larger randomized trial with independently marked outcomes, error tracking and comparable implementation across languages would change the assessment.[1]
The distinction that matters
What the study measured—and what it did not
Quality review and later exam performance answer different questions.
Message review
Experts rate exercises and explanations
Identifies content strengths and errors
Crossover
Students receive two feedback conditions
Allows within-course comparison
Exam model
Later scores are adjusted statistically
No significant advantage was found