LLM Chatbots in High School Programming: Exploring Behaviors and Interventions

• URL: http://arxiv.org/abs/2511.18985v1
• Date: Mon, 24 Nov 2025 10:58:06 GMT
• Title: LLM Chatbots in High School Programming: Exploring Behaviors and Interventions
• Authors: Manuel Valle Torre, Marcus Specht, Catharine Oertel,
• Abstract summary: This study uses a Design-Based Research cycle to refine the integration of Large Language Models (LLMs) in high school programming education.<n>The initial problem was identified in an Intervention Group where, in an unguided setting, a higher proportion of executive, solution-seeking queries correlated strongly and negatively with exam performance.
• Score: 0.6308539010172308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study uses a Design-Based Research (DBR) cycle to refine the integration of Large Language Models (LLMs) in high school programming education. The initial problem was identified in an Intervention Group where, in an unguided setting, a higher proportion of executive, solution-seeking queries correlated strongly and negatively with exam performance. A contemporaneous Comparison Group demonstrated that without guidance, these unproductive help-seeking patterns do not self-correct, with engagement fluctuating and eventually declining. This insight prompted a mid-course pedagogical intervention in the first group, designed to teach instrumental help-seeking. The subsequent evaluation confirmed the intervention's success, revealing a decrease in executive queries, as well as a shift toward more productive learning workflows. However, this behavioral change did not translate into a statistically significant improvement in exam grades, suggesting that altering tool-use strategies alone may be insufficient to overcome foundational knowledge gaps. The DBR process thus yields a more nuanced principle: the educational value of an LLM depends on a pedagogy that scaffolds help-seeking, but this is only one part of the complex process of learning.

Related papers

• Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact [3.437656066916039]
  LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks.<n>This study evaluates the performance of leading foundation models with out-of-distribution tasks of the teaching and learning of schoolchildren.
  arXiv  Detail & Related papers  (2026-03-01T03:05:46Z)
• Experiential Reinforcement Learning [22.545003569634982]
  Experiential Reinforcement Learning (ERL) is a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process.<n>ERL consistently improves learning efficiency and final performance over strong reinforcement learning baselines.<n>These results suggest that integrating explicit self-reflection into policy training provides a practical mechanism for transforming feedback into durable behavioral improvement.
  arXiv  Detail & Related papers  (2026-02-15T01:23:48Z)
• Learning Affordances at Inference-Time for Vision-Language-Action Models [50.93181349331096]
  In robotics, Vision-Language-Action models (VLAs) offer a promising path towards solving complex control tasks.<n>We introduce Learning from Inference-Time Execution (LITEN), which connects a VLA low-level policy to a high-level VLM that conditions on past experiences.<n>Our approach iterates between a reasoning phase that generates and executes plans for the low-level VLA, and an assessment phase that reflects on the resulting execution.
  arXiv  Detail & Related papers  (2025-10-22T16:43:29Z)
• CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs [53.749193998004166]
  Curriculum learning plays a crucial role in enhancing the training efficiency of large language models.<n>We propose CurES, an efficient training method that accelerates convergence and employs Bayesian posterior estimation to minimize computational overhead.
  arXiv  Detail & Related papers  (2025-10-01T15:41:27Z)
• EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making [9.228654390917123]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including programming, planning, and decision-making.<n>We propose a novel self-evolve framework, EvoCurr, in which a dedicated curriculum-generation LLM constructs a sequence of problem instances with gradually increasing difficulty.<n>We show that our method significantly improves task success rates and solution efficiency compared to direct-solving baselines.
  arXiv  Detail & Related papers  (2025-08-13T07:59:29Z)
• Retrieved In-Context Principles from Previous Mistakes [55.109234526031884]
  In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to improve model performance through principles derived from mistakes. We propose Retrieved In-Context Principles (RICP), a novel teacher-student framework.
  arXiv  Detail & Related papers  (2024-07-08T07:32:26Z)
• Progress or Regress? Self-Improvement Reversal in Post-training [26.051637877066327]
  We propose a comprehensive evaluative framework to scrutinize the underlying enhancements of post-training paradigms for self-improvement. We show that models showing improved performance across benchmarks will paradoxically exhibit declines in broader, essential capabilities. These findings indicate that current self-improvement practices through post-training are inadequate for equipping models to tackle more complex problems.
  arXiv  Detail & Related papers  (2024-07-06T09:07:11Z)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models.<n>It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Assessor-Guided Learning for Continual Environments [17.181933166255448]
  This paper proposes an assessor-guided learning strategy for continual learning. An assessor guides the learning process of a base learner by controlling the direction and pace of the learning process. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner.
  arXiv  Detail & Related papers  (2023-03-21T06:45:14Z)
• Persistent Reinforcement Learning via Subgoal Curricula [114.83989499740193]
  Value-accelerated Persistent Reinforcement Learning (VaPRL) generates a curriculum of initial states. VaPRL reduces the interventions required by three orders of magnitude compared to episodic reinforcement learning.
  arXiv  Detail & Related papers  (2021-07-27T16:39:45Z)
• Bridging the Imitation Gap by Adaptive Insubordination [88.35564081175642]
  We show that when the teaching agent makes decisions with access to privileged information, this information is marginalized during imitation learning. We propose 'Adaptive Insubordination' (ADVISOR) to address this gap. ADVISOR dynamically weights imitation and reward-based reinforcement learning losses during training, enabling on-the-fly switching between imitation and exploration.
  arXiv  Detail & Related papers  (2020-07-23T17:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.