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.
Becker, Andrew Luxton-Reilly, and James Prather
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
representative citing papers
Qualitative interview study of 13 students finds AI availability in programming assignments raises perceived costs and lowers motivation for independent effort, while students still prefer learning through struggle.
A study of student pairs finds that misalignment in perceptions of partners' AI use early in collaborative programming projects is associated with lower performance, especially among lower-performing teams.
Empirical analysis of AI help-seeking trajectories in programming courses finds reactive troubleshooting dominates and links to higher submission counts without score differences.
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.
Presents VISMATIC, a containerized system for secure process-oriented monitoring and anomaly detection in computer graphics education to preserve academic integrity against AI assistance.
GenAI produced larger self-efficacy gains but noticeably lower learning outcomes than live tutoring, with visualizations underused and GenAI facing barriers on advanced topics.
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
-
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.
-
"Why Put in This Much Effort?": How AI Availability Shapes Students' Motivation in Introductory Programming
Qualitative interview study of 13 students finds AI availability in programming assignments raises perceived costs and lowers motivation for independent effort, while students still prefer learning through struggle.
-
Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance
A study of student pairs finds that misalignment in perceptions of partners' AI use early in collaborative programming projects is associated with lower performance, especially among lower-performing teams.
-
AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective
Empirical analysis of AI help-seeking trajectories in programming courses finds reactive troubleshooting dominates and links to higher submission counts without score differences.
-
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.
-
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.
-
Securing the Sandbox: A Rootless Containerized Framework for Process-Oriented Monitoring in Computer Graphics Education
Presents VISMATIC, a containerized system for secure process-oriented monitoring and anomaly detection in computer graphics education to preserve academic integrity against AI assistance.
-
Characterization and Effects of CS2 Learning with GenAI, Visualization, and Human Support
GenAI produced larger self-efficacy gains but noticeably lower learning outcomes than live tutoring, with visualizations underused and GenAI facing barriers on advanced topics.
-
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.