The paper presents SC2R, a semantics‑constrained counterfactual recourse framework designed to support educational decision making. SC2R integrates a calibrated predictive model, integer‑programming recourse generation, an RDF vocabulary for intervention plans, and SHACL validation to enforce constraints such as timing, budget, and availability. Evaluation on the OULAD dataset shows that the framework can generate compact, feasible intervention plans at scale and that semantic validation filters out infeasible recommendations that would otherwise be accepted by simpler optimization approaches.
arXiv:2606. 08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model.
By Yasuo Tabei
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
By Pavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch, Fadi Al Machot
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz
The paper proposes a structural causal modelling framework for student competency assessment, moving beyond traditional probabilistic models like item response theory. It introduces a protocol for constructing such models, emphasizing the explicit representation of interventions (e.g., hints) and counterfactual analysis. The authors illustrate the approach with data from an assessment of compulsory school students’ algorithmic skills.
By Francesca Mangili, Alessandro Antonucci, Rafael Caba\~nas
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. E...
arXiv:2606. 16113v1 Announce Type: new Abstract: Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable model decision.
By Zahra Khotanlou, Hashir Ahmed, Chenghao Tan, Ahmed Abdelaal, Amir-Hossein Karimi
arXiv:2606. 29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction.
By Craig Atkinson
arXiv:2609.39608v1 Announce Type: new
Abstract: Rule-based reasoning, as in eligibility checks and contract reviews, requires language models to assess evidence against individual conditions and comb...
By Haoyang Zhang, Jianpeng Zhao, Qi Hao, Pengyang Wang
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng
The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.
By Vinura Galwaduge, Jagath Samarabandu
Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This offline-to-online RL (O2O-RL) paradigm is particularly promising in nonstationary domains where interaction is costly or potentially hazardous.