Thailand

Active formulation of subgoals in problem-solving is an effective learning strategy for programming learners, allowing the transfer of knowledge across similar problems. Although proper guidance and feedback for learners are crucial to correct mistakes and misconceptions during subgoal formulation, providing them at scale is challenging and costly. With recent advances in generative AI, we investigate the practicality of using generative AI as TAs in programming education by examining their effectiveness in a subgoal learning environment. We explore whether programming learners can distinguish AI TAs from humans and whether the subgoal learning workflow with AI TAs yields learning gains and assess their capability to assist in coding the subgoals into executable programs. Our study shows that learners can distinguish AI TAs from human TAs based on response length and accuracy. Learners show learning gains over sessions with AI TAs in formulating subgoals and can produce code solutions faster with comparable satisfaction scores with AI TAs as human TAs.

ACL 2024

Learning from Teaching Assistants to Formulate Subgoals for Programming Tasks: Exploring the Potential for AI Teaching Assistants

subgoal learning

generative ai

human-ai interaction

cs education

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Vision-Language Models under Cultural and Inclusive Considerations

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Learning from Teaching Assistants to Formulate Subgoals for Programming Tasks: Exploring the Potential for AI Teaching Assistants

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