Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.
and Luxton-Reilly, Andrew and Prather, James , month = feb, year =
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Among novice programmers using AI code generators, trust did not predict compliance with suggestions, while performance correlated with both compliance and increased subsequent trust.
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
citing papers explorer
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Teaching and Learning under Deductive Errors
Extends PAC machine teaching to handle deductive errors by requiring teachers to select sets that lead to approximately correct hypotheses with high probability despite learner mistakes, with complexity results and LLM experiments.
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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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Teaching Astronomy with Large Language Models
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.