# Synthetic SLR excerpt for peer review practice

> TRAINING DOCUMENT — fictional manuscript excerpt with deliberately planted problems. Do not cite.

## An Overview of Artificial Intelligence Feedback Tools in Programming Education

**Abstract.** Artificial intelligence (AI) is transforming programming education. This paper reviews the literature on AI feedback tools for novice programmers. A total of 38 studies were analysed. The findings prove that AI feedback improves students' programming performance and motivation. More research is needed in this area.

**3. Method.** Relevant articles were searched in Google Scholar using the keywords "AI feedback" and "programming". Articles published in English were included. The first author screened the titles, abstracts and full texts. After removing 112 duplicates from 412 records, 300 records were screened, 80 full texts were assessed and 42 were excluded, leaving the included studies. Data on the aim, method and findings were extracted into a table.

**4. Results.** In total, 41 studies were included. Ahmad et al. (2023) developed a hint tool and found that it was useful. Brown and Lee (2024) used ChatGPT in a Python course and students liked it. Chen (2022) found no significant difference in grades. Davis et al. (2025) interviewed 24 students who said explanations helped them. Overall, these studies show that AI feedback is effective. As Smith et al. (2025) concluded, AI tutors will replace teaching assistants within five years.

**5. Discussion.** The results show that AI feedback tools are useful and students like them. Ahmad et al. (2023) found the tool useful, and Davis et al. (2025) also found that explanations helped. This review is the first comprehensive review of this topic. Future research should study AI feedback more.

**Declarations.** None.

*Reference list (extract):* Ahmad et al. (2023); Brown & Lee (2024); Chen (2022); Davis et al. (2025).