Team approach to literature review

The most practical approach for structuring the work is to split it by thematic area. Assuming a team of five, divide sources according to the four refined key questions (temptations, dangers, responsible use, mitigation).

Person A: Temptations
Person B: Dangers – cognitive/learning impacts
Person C: Dangers – social/ethical impacts
Person D: Nature of responsible AI use
Person E: Mitigation strategies

This allows each reviewer to go deep into one theme. To avoid ending up with five differently structured sets of notes from our reviewers, we use:

a shared source capture template;
clear tagging rules so everyone uses the same temptation/danger/mitigation codes;
a source quality checklist so everyone applies the same credibility criteria.

For each source, the capture template might record:

citation + link
source type (academic, news, think tank, etc.)
summary (≤200 words)
relevant temptation(s)
relevant danger(s)
mitigation strategies mentioned
evidence strength (Strong / Mixed / Weak)
direct quotes worth preserving

Coordination and integration will be important. A shared repository will be needed. Periodic reviews will let people see each other’s work and adjust theirs as necessary. Cross-checking pairs could be set up, each person reviewing one other person’s entries for clarity, completeness, and tagging accuracy. Finally, all findings will be compiled into a temptation–danger–mitigation matrix for analysis.