AI Assistance Widens the Performance-Competence Gap

Generative AI might boost essay quality during use but undermine actual learning—the classic performance-competence gap. Does letting students use ChatGPT while learning teach them anything, or just make them dependent?

Method: Proctored experiment with undergraduates writing analytical essays. AI access raised immediate test scores by 0.27 standard deviations, and gains persisted one week later. Essay quality showed little change during AI use but improved in style and relevance one week later when writing unaided—but only for augmentation users who used AI to explain concepts. Automation users' quality gains vanished once AI was removed. Students shifted time from drafting toward reading and searching.

Caveats: Single topic, proctored setting. Real-world use without supervision may differ. Augmentation vs automation distinction relies on self-report.

Reflections: Does the augmentation advantage hold across disciplines with different knowledge structures (STEM vs humanities)? · What interface design patterns encourage augmentation over automation use? · Do the learning gains compound over a semester, or do students eventually regress to automation?

Conversational AI could collapse multi-step search into single exchanges—or scaffold longer journeys. Which happens, and does it displace traditional search? We lack episode-level behavioral data linking assistant use to search and browsing.

Method: Cross-surface panel linking real conversations with major assistants to users' searches and browsing. Most AI episodes (72% for one-to-three word asks, 48% beyond twenty words) terminate in place with no onward search. Roughly two-fifths are workbench use (drafting, coding) not information seeking. Search remains woven through three-quarters of within-episode transitions; after reading a page, users return to search over assistant 70/30. Verification is rare: explicit verification language follows roughly 1% of episodes.

Caveats: Observational panel data without demand counterfactual. Cannot isolate causal effect of assistant availability on search volume.

Reflections: What interface interventions could raise verification rates above 1%? · Why does articulation length predict collapse more than task type? · Do users develop different search strategies over time as they learn assistant capabilities?

Point of Sale fraud detection systems misinterpret rural network timeouts as fraudulent intent—discrimination laundering that excludes rural accounts from the digital economy. Infrastructure noise masquerades as behavioral signal.

Method: Hierarchical human-AI triage model with three-tier routing: calibrated ensemble as primary filter, epistemic uncertainty cases (cold start accounts) to specialist analysts, high stakes to senior supervisor. Dynamic shadow price rations human attention; random audit prevents skill atrophy. Achieved 1.88% complementarity gap and 24.79 percentage point gain in fraud recall over autonomous baseline. Reduced regional performance gap from 19.43 to 2.88 percentage points.

Caveats: Nigerian FinTech context. Infrastructure patterns and fraud base rates differ across regions. Human capacity constraints may not generalize.

Reflections: How do shadow prices need to adjust as infrastructure improves and aleatoric noise decreases? · What audit frequency prevents skill atrophy without overwhelming analysts? · Can the routing policy adapt to seasonal infrastructure patterns (e.g., rainy season network degradation)?