Enterprise Knowledge-Grounded Human–AI Collaborative Feedback Generation Using Retrieval-Augmented Models

Abstract

Adaptive feedback generated by large language models often suffers from limited auditability and inconsistent pedagogical intent, which constrains trust in real learning deployments. This study proposes an explainable adaptive feedback framework that combines retrieval-augmented generation with pedagogical rationale tracing, linking learner-state signals, retrieved evidence, and instructional move sequencing into a traceable rationale artifact. Across four conditions, the full model improves faithfulness to evidence from 0.66 to 0.88 and reduces scope violations from 7.4% to 1.9%. Learning proxies improve concurrently, with next-item accuracy increasing from 71.2% to 78.9% and revision uptake rising from 34.6% to 47.8%. Human rubric scores confirm higher instructional usefulness, where actionability increases from 3.6 to 4.3 on a 5-point scale while tone remains stable from 4.1 to 4.2. Ablation results show retrieval as the dominant driver of grounding, with faithfulness dropping to 0.62 and scope violations rising to 8.1% when retrieval is removed, whereas removing rationale tracing mainly degrades actionability from 4.3 to 3.7 and revision rate from 47.8% to 39.5%. Stratified analysis indicates the strongest benefits for low mastery and high frustration learners, where next-item accuracy improves from 61.0% to 70.0% and revision rate increases from 45.0% to 58.0%, alongside persistence gains from 68.3% to 79.5%. Mitigation controls further reduce evidence mismatch from 9.6% to 4.1% and rationale incoherence from 6.8% to 2.9%. The findings indicate that grounded generation and explicit pedagogical rationale tracing jointly improve effectiveness, accountability, and deployment readiness of adaptive feedback systems.