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.

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Co-Writing and Collaboration

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Can process data distinguish AI delegation from ordinary collaboration?

When students or writers use AI tools, their work leaves traces in keystroke logs and editor telemetry. Can these process signatures reliably separate wholesale delegation from permitted collaborative use?

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.

Does Figma Make speed up design task completion?

Does access to a prompt-to-design tool reduce the time needed to complete structured design work, and does the effect differ between professional designers and product managers?

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.

Can source traceability make AI writing trustworthy?

If every claim in machine-generated text traces back to a verifiable source, does that fundamentally change whether human professionals will actually use AI as a collaborator rather than a curiosity?

What tasks do expert data storytellers trust to LLMs?

Expert visual data storytellers make strategic choices about which narrative work to delegate to LLMs and which to protect. Understanding these boundaries reveals how human judgment and automation can coexist in knowledge work.

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.

Do university AI policies actually protect what credentials mean?

Universities are getting better at stating what AI use is allowed, but do their policies explain what evidence proves a student's actual competence? This matters because a credential's value depends on what work the student actually did.

Can writers benefit from configuring AI writing partners in advance?

This exploratory study asks whether writers can effectively set up proactive AI assistants by pre-planning their roles and behavior, then use them during actual writing work for idea generation and self-monitoring.

Do writers want to see each other's AI prompts in shared editors?

This study explores whether revealing AI prompting activity to collaborators in text editors affects how writers work together. Understanding prompt visibility matters because it shapes trust, learning, and awareness of AI's role in collaborative writing.

Workplace Applications

9 notes

Does AI turn freelance work into validation instead of creation?

Does shifting freelancers from producing original work to validating AI output undermine their ability to build skills through paid practice? This matters because freelancers rely on client work as their primary learning mechanism.

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?

Where have workers actually delegated tasks to AI?

Existing AI-exposure measures predict where AI could work, not where workers have actually adopted it. This research asks which occupations have embedded AI into real workflows, and whether that pattern matches technical capability or conversational tool use.

Does generative AI shift knowledge workers away from communication?

When knowledge workers adopt generative AI heavily, do they spend proportionally more time on individual documentation and less on coordination with colleagues? Understanding this matters because it suggests AI may reshape not just productivity but the social fabric of how teams work together.