Preply Adds AI-Generated Lesson Summaries and Follow-Up Exercises Powered by OpenAI

Preply, an online language tutoring platform, has built a feature using OpenAI's models that automatically generates summaries after tutoring sessions. The summaries include personalised feedback on what the learner covered, notes on errors or patterns the tutor observed, and follow-up exercises tied to that specific lesson. The feature is intended to reduce the administrative work tutors do after each session while giving learners a structured recap they can act on between classes.

Language tutors on the Preply platform who currently write post-session notes manually will save time on that task. Adult learners working toward a specific goal, such as business English for a new job or conversational Spanish before a relocation, benefit from exercises that connect directly to what they just practised. Corporate clients using Preply to train employees at scale may find the consistent post-session output useful for tracking progress across a cohort.

Tutors who previously spent five to fifteen minutes writing session notes can offload that to the model, freeing time for lesson preparation or taking on more students. For learners, the gap between sessions has historically been where momentum is lost. A summary with targeted exercises tied to that day's mistakes gives them something concrete to do rather than returning to generic course material. At scale, Preply also gains structured session data that could inform how it matches learners to tutors or measures outcomes over time.

The quality of the summary depends on what the tutor logs or flags during the session. If the input is thin, the AI output will be too. The feature also does not replace the tutor's judgment about a learner's progress, it produces a structured document, not a diagnostic assessment. Learners who struggle with self-directed study will still need human follow-up to stay on track. And for casual learners with no specific goal, personalised follow-up exercises add less value than the feature's framing implies.