2026-10-06T08:30:51Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33518512 2026-10-01T11:49:48Z category_29761 category_27706 category_28864 item_type_12 month_year_10_2026

An M&E time machine: Using AI to measure changes in a system across a time period on features which only emerge during it. Steve Powell (17114023) Gabriele Caldas Cabral (20658284) Hannah Mishan (25156366) Complex systems Social program evaluation Artificial intelligence not elsewhere classified complex systems evaluation artificial intelligence <p dir="ltr">Preprint first posted in 2024 on Sage's preprint server, which has since closed. Deposited on Figshare in September 2026.</p><p dir="ltr">People involved in project monitoring and evaluation of complex projects are familiar with what we call the ”time machine” problem: the things we want to measure (drivers, outcomes, intervening factors) may emerge and change unpredictably during a project’s lifespan and so cannot be fully specified until project end: but we need to know about them at baseline so we can design appropriate measurement instruments for tracking change. We demonstrate a novel workflow to help solve this problem which uses an AI-controlled chatbot to interview respondents, and then uses AI to code the transcripts and identify ”causal links” where stakeholders said that one thing influences another. We analyse the resulting causal information for differences across time: tracking evidence for emerging trends on emerging variables. The approach is reproducible, scalable and cost-effective. Further work is needed, especially to address the bias in the language models which drive the AI’s responses. Motivation and background: the “time machine” problem and an AI-assisted causal mapping pipeline as a solution to it</p> 2026-10-01T11:49:48Z Text Preprint 10.6084/m9.figshare.33518512.v2 https://figshare.com/articles/preprint/An_M_E_time_machine_Using_AI_to_measure_changes_in_a_system_across_a_time_period_on_features_which_only_emerge_during_it_/33518512 CC BY 4.0