RECAP captures, replays, and analyzes AI-assisted programming sessions by linking prompts, edits, and developer actions in a single timeline.
Is ai the better programming partner? human-human pair programming vs
6 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Pith papers citing it
11
external citations · external index
citation-role summary
other 1
citation-polarity summary
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2026 6roles
other 1polarities
unclear 1representative citing papers
Randomized classroom trial of 215 students shows natural language LLM feedback improves completion rates and convergence speed over test-case feedback or none, with test-case effects varying by validity.
Multimodal feedback on joint gaze and mental-effort synchrony improves pair programming debugging performance, with proactive ML-based forecasts outperforming reactive interventions.
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
A classroom evaluation with 45 high school students finds that conversational agents can aid CSP learning by delivering context-appropriate information, comparing general and custom agent approaches for effectiveness and engagement.
citing papers explorer
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RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
RECAP captures, replays, and analyzes AI-assisted programming sessions by linking prompts, edits, and developer actions in a single timeline.
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A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming
Randomized classroom trial of 215 students shows natural language LLM feedback improves completion rates and convergence speed over test-case feedback or none, with test-case effects varying by validity.
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Can providing feedback on gaze and mental-effort synchrony improve pair programming performance?
Multimodal feedback on joint gaze and mental-effort synchrony improves pair programming debugging performance, with proactive ML-based forecasts outperforming reactive interventions.
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Relationships Between Trust, Compliance, and Performance for Novice Programmers Using AI Code Generation
Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
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Investigating Conversational Agents to Support Secondary School Students Learning CSP
A classroom evaluation with 45 high school students finds that conversational agents can aid CSP learning by delivering context-appropriate information, comparing general and custom agent approaches for effectiveness and engagement.
- Human agency in initial human-AI proof formalization workflows