The article discusses how machine learning exercises can be designed for automated assessment tools, framing them as deterministic input-output tasks. It emphasizes that this approach does not create a new grading system but enables existing platforms (e.g., VPL for Moodle, Codeforces, MOJ) to support AI education more effectively. The authors argue that integrating theory with practice through such exercises can foster dynamic, interactive AI courses.
By Artur Jordao
The paper presents a risk‑adaptive, evidence‑constrained framework that uses learning analytics to provide personalized feedback in introductory programming. By training models on 2,993 failed‑submission states from 215 students, the authors predict persistent failure and generate four tailored feedback conditions for 136 cases. A calibrated risk policy selects interventions for 17.8% of eligible states, capturing 25.2% of persistent failures, and the framework ensures that generated messages contain all required components after evidence gating.
The paper presents a risk‑adaptive, evidence‑constrained framework for providing feedback in introductory programming courses. Using data from 2,993 failed submissions by 215 students, the authors built models that predict persistent failure and generate four tailored feedback conditions for 136 cases. The framework employs calibrated risk to decide when to intervene, evidence gating to limit feedback content, and a progressive assistance strategy that moves from self‑checks to localized hints.
By Shihao Wang
arXiv:2507.12674v3 Announce Type: replace-cross
Abstract: Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through...
By Rose Niousha, Mihran Miroyan, Abigail O'Neill, Joseph E. Gonzalez, Gireeja Ranade, John DeNero, Narges Norouzi
The paper presents ESSE, a self‑explanation tutor that uses a large language model to give immediate feedback on students’ line‑by‑line explanations of introductory programming worked examples. It evaluates the LLM’s judgments against a domain expert and a crowd of non‑experts, finding that the model is reliable enough to serve as the tutor’s assessment engine. In an introductory Java course, the tutor’s feedback encourages students to persist, improves the completeness and conceptual depth of their explanations, and shows evidence of learning.
By Arun-Balajiee Lekshmi-Narayanan, Mohammad Hassany, Kamil Akhuseyinoglu, Rully Hendrawan, Peter Brusilovsky
arXiv:2607. 10674v1 Announce Type: cross Abstract: As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification.
By Nasser Giacaman, Valerio Terragni, Paul Denny, Viraj Kumar