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AI-powered automated feedback for online tutors helps them self-monitor and understand platform expectations, despite initial negative perceptions.

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Supporting Tutors in the Gig Economy with Automated Feedback: A Case Study on Ringle

Yeon Su Park, Sieun Kim, Keighley Overbay, Seoyoung Kim +3 more

In plain terms

Online tutoring platforms in the gig economy often struggle to provide useful feedback to tutors. Traditional feedback from learners can be limited and makes it hard to monitor tutor quality consistently. To address this, researchers explored using AI-powered automated feedback. They created a special tool that analyzed tutors' lessons on Ringle, a popular online English tutoring platform, and then provided feedback. They surveyed 36 tutors, finding that while tutors initially perceived automated feedback more negatively than learner feedback, they still found it useful for checking their own performance and understanding platform expectations. However, discrepancies between the AI's feedback and their own views sometimes caused confusion. Based on these insights, the paper proposes design ideas for future AI feedback systems in education.

Why it matters · This paper is crucial for newcomers interested in using AI to support educators and improve the quality of online learning. It highlights the challenges and benefits of AI-driven feedback for human professionals in educational settings.

About this work · This research explores how artificial intelligence can provide automated feedback to human tutors working on online educational platforms. The study was conducted as a case study on Ringle, a platform for learning English.

automated feedbackonline tutoringAI in educationtutor supportlearning analytics