254 citations · 344 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
AI Should Challenge, Not Obey
Advait Sarkar
Let's transform our robot secretaries into Socratic gadflies.
"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…
"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…
Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu +10
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often…
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…
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…
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…