Glossary

AI-Based Student Feedback

What is AI-Based Student Feedback?

AI-based student feedback refers to comments on essays, assignments or projects generated by an AI system against defined criteria. Learners receive pointers on strengths, structure, argument or language more quickly, and teachers are relieved of repetitive marking. Assessment should be transparent and criteria-based; grades and decisions about performance remain the responsibility of teachers. Example: a draft essay receives suggestions for improvement before submission.

How KI.ordo helps

KI.ordo's feedback template names strengths and suggestions for improvement against given criteria and does not award a grade without criteria.

Learn more →: AI-Based Student Feedback

Topics

  • Education
  • Feedback
  • Learning

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Related terms

Adaptive learning

Adaptive learning refers to learning programmes that continuously adjust to a learner's knowledge, pace and mistakes. Software analyses answers and then selects suitable exercises, explanations or revision. It is used in schools, universities and corporate training – for instance, when a platform automatically offers an easier intermediate step after a task was answered incorrectly. In machine learning, the term also describes models that keep adapting to new data.

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Natural language generation (NLG)

Natural language generation (NLG) is the area of language processing that produces understandable text from data, bullet points or other inputs. Earlier NLG systems relied on templates and rules; today most use large language models. Typical uses include weather and stock market reports, product descriptions, summaries of key figures and chatbot answers. It is important that figures and facts from the inputs are carried over correctly and unchanged.

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Readability

Readability describes how easily a text can be read and understood. It depends on sentence length, word length, technical terms, sentence structure and layout, and can be approximated with formulas such as the Flesch index or LIX. Good readability matters for customer communication, instructions, websites and accessible content. Formulas only offer guidance, however; whether a text is clear ultimately depends on its audience.

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Human-in-the-Loop

Human-in-the-loop describes workflows in which people step into an AI process at defined points: they review results, approve or correct them, or take over difficult cases. The principle reduces the risk of faulty output and keeps accountability with people, especially for decisions affecting individuals, money or legal matters. Example: an AI agent drafts replies to customer enquiries and a member of staff checks them before they are sent.

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