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20232026
most citedThe Metacognitive Demands and Opportunities of Generative AI

254 citations · 344 across the 9 of their papers we have counts for

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cs.HC2026

Metacognitive Scaffolds Create Productive Friction for AI-Assisted Knowledge Work

Viktor Kewenig, Lev Tankelevitch, Sean Rintel +2

Generative AI imposes new metacognitive demands on knowledge workers as their work shifts from production to planning, evaluation, and orchestration. In a randomized, between-subje…

cs.HC2026

From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output

Advait Sarkar, Christian Poelitz, Viktor Kewenig

Generative AI tools often answer questions using source documents, e.g., through retrieval augmented generation. Current groundedness and hallucination evaluations largely frame th…

cs.HC2025★ 1 cited

Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop

Lev Tankelevitch, Elena L. Glassman, Jessica He +7

Generative AI (GenAI) radically expands the scope and capability of automation for work, education, and everyday tasks, a transformation posing both risks and opportunities for hum…

cs.HC2024★ 37 cited

AI Should Challenge, Not Obey

Advait Sarkar

Let's transform our robot secretaries into Socratic gadflies.

cs.HC2024★ 28 cited

"It's like a rubber duck that talks back": Understanding Generative AI-Assisted Data Analysis Workflows through a Participatory Prompting Study

Ian Drosos, Advait Sarkar, Xiaotong Xu +3

Generative AI tools can help users with many tasks. One such task is data analysis, which is notoriously challenging for non-expert end-users due to its expertise requirements, and…

cs.HC2024

"My toxic trait is thinking I'll remember this": gaps in the learner experience of video tutorials for feature-rich software

Ian Drosos, Advait Sarkar, Andrew D. Gordon

Video tutorials are a popular medium for informal and formal learning. However, when learners attempt to view and follow along with these tutorials, they encounter what we call gap…

cs.HC2023★ 3 cited

Participatory prompting: a user-centric research method for eliciting AI assistance opportunities in knowledge workflows

Advait Sarkar, Ian Drosos, Rob Deline +5

Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current re…

cs.HC2023★ 254 cited

The Metacognitive Demands and Opportunities of Generative AI

Lev Tankelevitch, Viktor Kewenig, Auste Simkute +4

Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on…

cs.HC2023★ 21 cited

Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?

Advait Sarkar

The research field of end-user programming has largely been concerned with helping non-experts learn to code sufficiently well in order to achieve their tasks. Generative AI stands…