Self-Explanation Tutor for Active Study of CS1 Worked Examples

• URL: http://arxiv.org/abs/2608.25180v1
• Date: Tue, 25 Aug 2026 21:54:47 GMT
• Title: Self-Explanation Tutor for Active Study of CS1 Worked Examples
• Abstract summary: We build a self-explanation tutor for introductory programming, ESSE, in which students explain lines of worked examples.<n>We ask whether the LLM judges student explanations well enough to serve as the engine of the tutor.<n>We find that its feedback leads students to persist and revise rather than abandon a line.
• Score: 1.7504194797252353
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Worked examples are a important part of introductory programming, but reading their expert explanations is passive. Self explanation, students explaining the problem and its solution to themselves with subgoal level analysis, turns that study into an active task, yet it is hard to scale because assessing free-text explanations and returning timely feedback has had no easy automated solution. We investigate whether a large language model (LLM) can fill that gap. We build a self-explanation tutor for introductory programming, ESSE, in which students explain lines of worked examples and receive immediate LLM feedback on the correctness and completeness of each explanation, and we pursue two goals. First, we ask whether the LLM judges student explanations well enough to serve as the engine of the tutor; we assess its judgments against two independent human reference standards of different kinds, a single domain expert and a crowd of non-expert raters, each with its own strengths and weaknesses, characterizing both where the LLM is reliable and the systematic tendencies in how it diverges. Second, we ask whether the LLM-based tutoring benefits students; deploying it in an introductory Java course, we find that its feedback leads students to persist and revise rather than abandon a line, that their explanations grow more complete and conceptually richer across attempts, and that students show evidence of learning. These indicate that LLM-based assessment is good enough to power a self-explanation tutor, and that the tutor positively shapes how students study worked examples.

Related papers

• LLM Pedagogical Behavior in AI Tutoring Interactions [2.7316402953904233]
  Little is known about the level of assistance LLMs provide when students use them as tutors in authentic learning interactions.<n>We operationalize this dimension as scaffolding level and develop a five-level scale, validated against human annotations.<n>We apply the scale to 14,637 LLM responses from 203 students in a university AI course.
  arXiv  Detail & Related papers  (2026-08-24T08:57:40Z)
• Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM [16.09740388389993]
  We present a framework that provides adaptive feedback by grounding Large Language Models with domain-specific expert knowledge.<n>We deploy this framework in a large-scale university course (N > 1000), where it improved student performance by over 80% compared to previous semesters.
  arXiv  Detail & Related papers  (2026-05-26T00:30:19Z)
• Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks [9.471800828517496]
  We study scenarios where students behave adversarially and aim to obtain the correct answer from the tutor.<n>We evaluate a broad set of LLM-based tutor models, including different model families, pedagogically aligned models, and a multi-agent design.<n>We present simple but effective defense strategies that reduce answer leakage and strengthen the robustness of LLM-based tutors.
  arXiv  Detail & Related papers  (2026-04-20T11:29:22Z)
• Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study [0.0]
  Large Language Models (LLMs) hold promise as dynamic instructional aids.<n>Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)
  arXiv  Detail & Related papers  (2025-04-07T23:57:32Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [106.45895712717612]
  Large language models (LLMs) have shown remarkable versatility in various generative tasks. This study focuses on the topic of LLMs assist NLP Researchers. To our knowledge, this is the first work to provide such a comprehensive analysis.
  arXiv  Detail & Related papers  (2024-06-24T01:30:22Z)
• Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models [54.21695754082441]
  We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions. A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations. Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-02-06T03:18:58Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• Learning by Self-Explaining [23.420673675343266]
  We introduce a novel workflow in the context of image classification, termed Learning by Self-Explaining (LSX) LSX utilizes aspects of self-refining AI and human-guided explanatory machine learning. Our results indicate improvements via Learning by Self-Explaining on several levels.
  arXiv  Detail & Related papers  (2023-09-15T13:41:57Z)
• Complementary Explanations for Effective In-Context Learning [77.83124315634386]
  Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts. This work aims to better understand the mechanisms by which explanations are used for in-context learning.
  arXiv  Detail & Related papers  (2022-11-25T04:40:47Z)

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.