Criteria for selecting artificial intelligence tools and systems for educational purposes

A quality AI tool or system must be efficient, reliable, and accurate. If these tools frequently provide incorrect information or inaccurate results, they can confuse or discourage students, negatively affecting their understanding of the material and their trust in the technology. It is important to emphasise that any error in an educational context also has pedagogical consequences, as it can impact students’ motivation and sense of achievement.

Examples:

1. An automated essay grading system, such as Gradescope, uses AI to analyse student work and assign grades. If such a system provides inaccurate or unreliable assessments – for example, due to poorly defined metrics, an inappropriate training dataset, or inadequate rubric management – students may become confused or discouraged because they perceive the grades as unfair. Therefore, the AI model must be thoroughly trained and its results regularly reviewed.
2. An automated originality checking tool, such as Turnitin, uses AI to detect plagiarism in student work. If the system frequently generates false positives or false negatives – for example, unfairly accusing a student because their writing style is similar to another’s, or missing a real case of plagiarism – trust in the process and the sense of fairness can be significantly undermined. Therefore, such tools must ensure a high level of accuracy, regular review, and clear communication about the system's potential limitations.