Applied AI and Human Collaboration

Research on how AI systems are deployed in real-world professional, creative, and educational contexts. Covers human-AI co-writing, domain-specific applications, and the social and cognitive dynamics that emerge when language models interact with users.

46 notes (primary) · 104 papers · 3 sub-topics

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Co-Writing and Collaboration

6 notes

Can AI generate hundreds of fake academic papers automatically?

Explores whether language models can industrialize academic fraud by retroactively constructing theoretical justifications for data-mined patterns, complete with fabricated citations and creative signal names.

Does AI writing make authors seem more privileged than they are?

When writers use AI assistance, do readers perceive them as more educated, wealthier, and whiter? This matters because it could mask or erase the actual diversity of voices in public discourse.

Do writers actually edit AI-generated text before publishing?

This research tests whether the "human-in-the-loop" safeguard against AI text quality issues actually works in practice. It examines how often writers revise AI-generated paragraphs and how substantially they change them.

How do writers use AI through different creative stages?

This study explores whether writers deploy large language models differently depending on their creative needs—from generating initial ideas to organizing thoughts to drafting final text. Understanding these patterns reveals how humans and AI can complement each other's strengths.

Does ownership framing change how much writers rely on AI?

When writers believe they own the final output versus composing for themselves, do they use AI suggestions differently? Understanding this matters because it reveals whether reliance is driven by tool capability or by how tasks are framed.

Can structured pipelines make LLM novelty assessment reliable?

Explores whether breaking novelty assessment into extraction, retrieval, and comparison stages helps LLMs align with human peer reviewers and produce more rigorous, evidence-based evaluations.

Workplace Applications

5 notes

Why does AI default to coaching instead of doing?

In workplace conversations, users often want AI to execute tasks like writing or gathering information, but AI tends to explain and advise instead. What drives this systematic mismatch between what users need and what AI provides?

Do LLM research ideas actually hold up when experts try to execute them?

Explores whether LLM-generated ideas maintain their apparent novelty advantage when expert researchers spend 100+ hours implementing them. Matters because ideation-stage evaluation may not capture real-world feasibility barriers.

Does concentrated AI exposure enable workers to adapt and reallocate?

When AI displaces specific tasks rather than spreading across many, workers may shift effort to non-displaced tasks within their occupation. Does this reallocation mechanism actually offset employment losses?

What happens to human wages in an AGI economy?

Does human labor retain economic value when AGI can replicate most work? This explores whether wages would reflect the computational cost of replacement rather than the value workers actually produce.

What collaboration level do workers actually want with AI?

Explores whether workers prefer full automation, equal partnership, or continuous human control across different tasks. Understanding worker preferences could reshape how organizations deploy AI systems.

AI in Education

1 note

Does AI assistance weaken our brain's ability to think independently?

Can using language models for cognitive tasks reduce neural connectivity and learning capacity? New EEG evidence tracks how external AI support may systematically degrade our cognitive networks over time.