Autoedut

Autoedut is a term used in educational technology to refer to automated, adaptive editing and tutoring workflows that assist learners and educators. It may denote automated generation and editing of educational content, assessment items, and feedback. The exact definition varies by context; there is no single standardized definition. It emerged in discussions around AI-driven edtech in the 2020s.

Systems labeled as autoedut typically combine natural language processing, learning analytics, and rule-based personalization to tailor content, provide feedback, and generate practice tasks. Typical features include content generation, automated evaluation and feedback, adaptive sequencing, performance dashboards, and integration with learning management systems. Applications span language learning, STEM practice, coding education, and teacher planning. Efficacy evidence is mixed, with some studies showing improved engagement or efficiency, while others highlight risks such as quality variation, biases, and over-reliance on automated feedback. As a practice, autoedut emphasizes transparency about automated decisions and alignment with pedagogical goals, and often requires human oversight to validate content and monitor student progress. The term remains informal and context-dependent, sometimes overlapping with related concepts like automated tutoring, adaptive learning, and AI-assisted instruction.