Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces
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Abstract (arXiv)
Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend complex and ambiguous natural language prompts. However, calibrating LLM interactions is challenging for interface designers and end-users alike. A central issue is our limited grasp of how human cognitive processes begin with a goal and form intentions for executing actions, a blindspot even in established interaction models such as Norman's gulfs of execution and evaluation. To address this gap, we theorize how end-users 'envision' translating their goals into clear intentions and craft prompts to obtain the desired LLM response. We define a process of Envisioning by highlighting three misalignments: (1) knowing whether LLMs can accomplish the task, (2) how to instruct the LLM to do the task, and (3) how to evaluate the success of the LLM's output in meeting the goal. Finally, we make recommendations to narrow the envisioning gulf in human-LLM interactions.
Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2023.
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Citing papers per year: 2024: 6; 2025: 33; 2026: 32.
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- 2025CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experimentscited by 8
- 2026AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wildcited by 7
- 2025ALLOY: Generating Reusable Agent Workflows from User Demonstrationcited by 3
- 2025Understanding User Experiences of Computer Use Agents: Design Space and Opportunities for Building Agent UX Prototypescited by 2
- 2026AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Usecited by 2
- 2025Psychologically Enhanced AI Agentscited by 1
- 2025VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agentscited by 0
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- 2026AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysiscited by 0
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- 2026Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation16 Apr 2026
- 2026IntentWeave: A Progressive Entry Ladder for Multi-Surface Browser Agents in Cloud Portals24 Mar 2026
- 2026AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wild12 Feb 2026
- 2025ALLOY: Generating Reusable Agent Workflows from User Demonstration11 Oct 2025
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- 2025Psychologically Enhanced AI Agents4 Sep 2025
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- 2026Towards Cognitive Process-Aware Proactive Writing Support31 Aug 2026
- 2026Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents26 Aug 2026
- 2026Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments14 Jun 2026
- 2026Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery7 May 2026
- 2026U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning4 May 2026
- 2026Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning23 Apr 2026
- 2026Alignment has a Fantasia Problem23 Apr 2026
- 2026Auditing and Controlling AI Agent Actions in Spreadsheets22 Apr 2026
- 2026From Fuzzy to Formal: Scaling Hospital Quality Improvement with AI21 Apr 2026
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12 newest of 56 All 56 (JSON)