Human-in-the-loop checking of every AI output before use in teaching
Teachers describe subjecting every AI output to a check for academic precision, pedagogical alignment and ethical standards before use, and describe the tool as a partner for refining ideas rather than an author. The framing that dominates the third round is augmentation rather than replacement, and it recurs regardless of the purpose of use.
Record
- Level
- Micro
- Domain
- Teaching Practice
- Country
- Multiple countries
- Context
- Higher education
- Maturity
- Established
- Scalability
- Piloted
- Transferability
- Embedded
- Source report
- R3
Source trail
The source on which this record rests, the place in the document and the sentence found there. Quotations are searched for mechanically in the source file and the page on which they were located is recorded.
- 01R3 · Report on trends in policies and practices on the use of LLMs and generative AI in the partnership: Third report (draft)ADMIT (Erasmus+, EADTU)Section 3.3.3Quotation located in the source
Rather than using the raw output, I always apply a 'human-in-the-loop' approach to ensure the final content meets professional standards.
The quotation states the practice as a routine rather than an aspiration, which is what allows it to be recorded as a micro-level practice.
- 02R3 · Report on trends in policies and practices on the use of LLMs and generative AI in the partnership: Third report (draft)ADMIT (Erasmus+, EADTU)Section 3.3.3Quotation located in the source
the dominant framing is augmentation, not replacement
The report's own characterisation establishes that this is the prevailing pattern rather than an isolated position.
Evidence
The reported evidence on the effect of the practice. Evidentiary strength varies between practices; it is reproduced here as the source reports it.
The report identifies this as the theme covering most answers to the question on use, and supports it with quotations from several stakeholder groups.