Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
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arXiv preprint arXiv:2601.20245 , year =
15 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
Three pre-registered studies with 2691 participants show people underestimate their AI usage rate and overestimate efficiency gains on simple tasks, with prior use entrenching further adoption.
Freelancers use generative AI to support exploratory skill acquisition but not as their main resource due to reliability issues, leading to a shift toward survival-oriented upskilling and the emergence of invisible competencies that lack market validation.
Claude Code answers recurring agent design questions with a thin model loop wrapped in dense safety, context, extensibility, and persistence harnesses, and those same questions get different answers in OpenClaw and Hermes.
RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.
The paper introduces six design principles for embedding incidental learning into AI-assisted software development and presents SHIELD, a multi-agent system that surfaces contextual learning moments from AI coding agents' reasoning traces.
A two-period model shows worker mobility reverses firm engagement from least-skilled to highest-skilled workers below the AI benchmark, with capability raising engagement and reliability having ambiguous effects.
Introduces Augment Engineering as a six-phase multi-tool orchestration methodology, supported by exploratory statistics from a single-practitioner case study across seven domains.
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.
AI safety literature overlooks cognitive deskilling and addiction risks from generative AI despite public concern about them.
citing papers explorer
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Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software
Study of 930k+ agent PRs shows repository explains ~50% of integration friction variance, with agents concentrating it twice as much as humans (ICC 0.30 vs 0.16) after controls.
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The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning
Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.
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The efficiency-gain illusion: People underestimate the rate of AI use and overestimate its benefits on simple tasks
Three pre-registered studies with 2691 participants show people underestimate their AI usage rate and overestimate efficiency gains on simple tasks, with prior use entrenching further adoption.
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Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers
Freelancers use generative AI to support exploratory skill acquisition but not as their main resource due to reliability issues, leading to a shift toward survival-oriented upskilling and the emergence of invisible competencies that lack market validation.
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Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
Claude Code answers recurring agent design questions with a thin model loop wrapped in dense safety, context, extensibility, and persistence harnesses, and those same questions get different answers in OpenClaw and Hermes.
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RelianceScope: An Analytical Framework for Examining Students' Reliance on Generative AI Chatbots in Problem Solving
RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.
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Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
The paper introduces six design principles for embedding incidental learning into AI-assisted software development and presents SHIELD, a multi-agent system that surfaces contextual learning moments from AI coding agents' reasoning traces.
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Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility
A two-period model shows worker mobility reverses firm engagement from least-skilled to highest-skilled workers below the AI benchmark, with capability raising engagement and reliability having ambiguous effects.
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Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains
Introduces Augment Engineering as a six-phase multi-tool orchestration methodology, supported by exploratory statistics from a single-practitioner case study across seven domains.
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Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
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A meta-analysis of the effect of generative AI on productivity and learning in programming
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
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Aleena: Alignment Agent for Research Software Engineering Collaborations
Aleena is an open-source AI agent that ingests multi-modal research software collaboration artifacts and transforms them into structured GitHub records to maintain continuous stakeholder alignment across the project lifecycle.
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Brainrot: Deskilling and Addiction are Overlooked AI Risks
AI safety literature overlooks cognitive deskilling and addiction risks from generative AI despite public concern about them.
- Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
- Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap