Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Human–AI interaction — how people work with a model through an interface — prompting and steering controls, editing and co-writing tools, assistive and co-adaptive interfaces, how such interfaces are scored, and what lab studies of them can and cannot show.
3 of 737 LIT documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| LIT-503 v2 | First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization | Reddy, Levine and Dragan (2022), ARXIV-2205.12381. Proposes scoring a control interface by the mutual information between the operator’s command and the state transition it induces — no labels, no reward, no knowledge of the task. Validated on 540K examples (Spearman ρ = 0.43, in 4 of 5 domains) and used to learn an interface from scratch in under 30 minutes. The fifth domain correlated negatively, and fixing it needs a horizon hyperparameter whose setting requires the task knowledge the method assumes away. | Active |
| LIT-717 | Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task | Kosmyna et al. (2025), ARXIV-2506.08872. In a small four-month EEG study, participants who wrote essays with ChatGPT showed the weakest brain connectivity, the lowest sense of ownership and the worst recall of their own essays. Those switched from ChatGPT to no tools stayed under-engaged, which the authors call accumulated “cognitive debt”. | Deferred — seeded from the abstract and a skim on 2026-09-25; not read in full |
| LIT-741 | Textoshop: Interactions Inspired by Drawing Software to Facilitate Text Editing | Masson, Kim and Chevalier (2024), ARXIV-2409.17088 (CHI 2025). A GPT-4o-backed text editor that replaces typed prompts with drawing-software operations, each of which sends an engineered prompt. Resizing a selection shortens or expands it, rotating it reorders the words, a colour picker sets tone, boolean operations merge passages, and layers hold versions. In a within-subject study with 12 participants, against a text editor plus a ChatGPT replica on the same model, participants rated themselves more successful (4.6 vs 3.9), gave a SUS of 90 against 69, and finished tasks 55 s faster. The quality of the resulting text was not measured. | Active |