LLM-Based Feedback System

• LLM-Based Feedback System is a framework that leverages generative language models to automate and customize feedback for diverse evaluative and educational applications.
• It incorporates modular components such as input processing, iterative refinement, and multi-agent orchestration to ensure accuracy, fairness, and scalability.
• Empirical studies demonstrate enhanced precision, engagement, and grading accuracy, making these systems valuable for improving learning outcomes.

A LLM-Based Feedback System is a computational infrastructure that leverages generative LLMs to automate, scaffold, or augment the collection, interpretation, and delivery of feedback across diverse educational, evaluative, and human-computer contexts. These systems instantiate LLMs—sometimes in orchestrated multi-agent pipelines—to provide individualized, scalable, and context-aware feedback on written responses, code, conceptual designs, reflections, or complex interactive tasks. LLM-based feedback architectures now span classroom assessment, programming education, self-learning, negotiation, reasoning, and optimization, offering both pointwise judgments and dialogic, pedagogically-grounded comments.

1. Canonical System Architectures and Core Design Patterns

LLM-based feedback systems are typically composed of modular components that map onto the lifecycle of educational or evaluative feedback:

• Input Processing: Student or user submissions (text, code, conceptual diagrams) are standardized, often via parsing or normalization (e.g., ERD→JSON in database design (Riazi et al., 23 Dec 2024)).
• Initial Feedback Generation: An LLM receives context-rich prompts combining submission content, rubric criteria, and, where relevant, supporting materials (e.g., mark schemes, curriculum texts, model solutions, or past examples) (Zhao et al., 6 Jul 2025, Maus et al., 11 Dec 2025).
• Feedback Evaluation or Verification: Automated “feedback-on-feedback” is delivered by an evaluator LLM or agent, often against a multi-dimensional rubric (e.g., 16-dimension framework in “Dean of LLM Tutors” (Qian et al., 8 Aug 2025); role-based critique in reflection assessment (Zhang et al., 14 Nov 2025)).
• Iteration/Refinement: Feedback may be regenerated after critique, using multi-agent generation-evaluation-regeneration (G-E-RG) cycles (Cao et al., 8 May 2025).
• Delivery and Personalization: Finalized feedback is routed to users, often with adaptation based on mastery, performance band, or educator intervention (Scholz et al., 1 Jul 2025, Herklotz et al., 6 Nov 2025).

Prominent architectural motifs include multi-agent role orchestration (Evaluator, Monitor, Coach, Aggregator, Reviewer in (Zhang et al., 14 Nov 2025)), ensemble voting/debate among multiple LLMs (Ensemble ToT in (Ito et al., 23 Feb 2025)), and conversational feedback delivery (OpineBot in (Tanwar et al., 28 Jan 2024)).

2. Feedback Protocols, Pedagogical Frameworks, and Prompt Engineering

Feedback protocol choice—absolute scoring (pointwise) vs. comparative (pairwise)—has substantial impact on quality and bias. Pointwise judgements, where LLMs assign scores or provide stepwise reasoning for a single response, resist superficial manipulations and maintain higher tie rates; pairwise preferences, by contrast, amplify distractor features (assertiveness, verbosity, sycophancy), leading to preference flipping and spurious leaderboard inflation (Tripathi et al., 20 Apr 2025). Recommendations include using absolute scoring for tasks requiring fine-grained or correctness-based feedback and restricting pairwise comparisons to large-quality-gap, high-signal ranking scenarios.

Prompt engineering is grounded in theoretical frameworks:

• Knowledge-Transmission and Learner-Centered Models: Feedback is explicitly structured to address correctness, process, self-regulation, and affect (Cao et al., 8 May 2025, Ippisch et al., 10 Nov 2025).
• Layered/Feedback-Ladder Approaches: Multi-tiered hints, from binary verdicts to code edits, are generated in a single LLM call with level-specific directives (Heickal et al., 1 May 2024).
• Curriculum-Grounded Generation: Chain-of-topic memory structures and K-concept mark schemes constrain LLM output to syllabus-aligned feedback (Zhao et al., 6 Jul 2025).

High-quality prompt design includes detailed enumeration of desired dimensions, explicit inclusion of rubrics or exemplars, role conditioning (“You are a patient math tutor”), and chain-of-thought scaffolding to improve depth and reduce hallucination rates (Herklotz et al., 6 Nov 2025, Maus et al., 11 Dec 2025). Retrieval-augmented generation and in-context learning can further enhance specificity and contextual grounding (Cao et al., 8 May 2025, Qian et al., 8 Aug 2025).

3. Multi-Agent and Self-Reflective Pipelines

Multi-agent systems explicitly instantiate distinct abstract feedback roles, such as scoring, bias detection, metacognitive coaching, synthesis, and adversarial review. The five-agent pipeline in “Scaling Equitable Reflection Assessment” (Zhang et al., 14 Nov 2025) exemplifies this form, yielding independently auditable outputs for each feedback dimension and enabling explicit fairness monitoring.

Iterative refinement, as in the G-E-RG scheme (Cao et al., 8 May 2025), leverages (1) candidate feedback generation via zero-shot or RAG_CoT with pedagogical prompting, (2) structured evaluation by a rubric-trained LLM agent, and (3) a final round of regeneration informed by explicit critique labels. This process boosts component completeness (improvement in inclusion of all rubric elements from ≈28% to ≈98%) and feature quality, providing significant gains over single-pass feedback generation for reliability, coverage, and conciseness.

Ensemble debate or “Grader by Ensemble ToT” approaches (Ito et al., 23 Feb 2025) synthesize multi-perspective LLM outputs (Expert, Teacher, TA) through conversational integration and policy lookup, increasing grading accuracy, macro-F1, and the explainability of feedback.

4. Evaluation Metrics, Error and Bias Analysis

LLM feedback system evaluation employs multi-faceted rubrics and quantitative metrics:

• Coverage of Feedback Dimensions: Binary presence of aspects such as right/wrong, response-orientation, process, self-regulation, self across generated feedback (Ippisch et al., 10 Nov 2025, Herklotz et al., 6 Nov 2025).
• Rubric Scoring Accuracy: Mean Absolute Error (MAE), Quadratic Weighted Kappa (QWK), and inter-rater reliability (Cohen’s κ, ICC) benchmark AI-human and AI-AI agreement (Zhang et al., 14 Nov 2025).
• Feedback Usefulness: Human Likert ratings for alignment, actionability, empathy, and insightfulness (Maus et al., 11 Dec 2025, Zhang et al., 14 Nov 2025).
• Content, Effectiveness, and Hallucination Detection: 16-dimension rubrics (6 content, 7 effectiveness, 3 hallucination) enable comprehensive automated vetting prior to user delivery; fine-tuned LLMs can reach human-expert level (e.g., GPT-4.1, F1-score ≈79.4%, matching human average ≈82.6%) (Qian et al., 8 Aug 2025).
• Equity and Fairness Gaps: Differential error analysis across high/low-proficiency learner bands ($\Delta_{\mathrm{MAE}}$), with dashboarded alerts for observed disparities (Zhang et al., 14 Nov 2025).
• Task-Specific Gains: In domain tasks such as Diplomacy lie detection, LLM-feedback-bootstrapped modification yields a 39% improvement in lying-F₁ score compared to zero-shot (Banerjee et al., 25 Aug 2024).

Analysis of failure modes reveals sensitivity to prompt calibration, limitations in “self” or metacognitive coverage, and the inability of many systems to adapt feedback to dynamic, multimodal, or live classroom contexts (Scholz et al., 1 Jul 2025, Zhang et al., 14 Nov 2025).

5. Empirical Findings and Practical Application Domains

Empirical deployments and user studies consistently report positive impacts:

• Richer, Dialogic Feedback: Conversational chatbots (OpineBot (Tanwar et al., 28 Jan 2024)) and mid-course LLM dialog systems (Maram et al., 13 Aug 2025) drive greater engagement, reflection, and actionable data compared to static surveys.
• High Precision, Usability, and Perceived Value: Systems such as curriculum-aligned programming feedback (Zhao et al., 6 Jul 2025) and fine-grained ERD feedback (Riazi et al., 23 Dec 2024) deliver high precision (e.g., cardinalities, attributes: F₁>0.85) and strong instructor/student adoption (Likert ≥4/5; 84%+ report helpfulness).
• Automated Grading and Self-Learning Support: Ensemble approaches provide explainable, debate-style grading pipelines with macro-F1 improvement (GET: macro-F1 0.67 vs. 0.63 baseline) and transparent reasoning (Ito et al., 23 Feb 2025).
• Physics Problem Solving: Evidence-centered LLM feedback yields high usefulness and accuracy perception (usefulness mean 3.6/5, perceived accuracy 4.4/5) but a nontrivial 20% error rate in complex domains (Maus et al., 11 Dec 2025).
• Feedback at Scale: Synthetic Educational Feedback Loops (SEFL) generate large-scale, diverse, high-quality feedback datasets without real student data, enabling smaller models to approach or surpass baseline LLMs in accuracy/actionability (Zhang et al., 18 Feb 2025).

Limitations include 7–20% error rates depending on domain complexity, prompt leakage of spurious stylistic bias (especially in pairwise protocols), and reduction in depth or pedagogical nuance for high-performing students or complex conceptual errors (Herklotz et al., 6 Nov 2025, Heickal et al., 1 May 2024, Maus et al., 11 Dec 2025).

6. Design Guidelines, Limitations, and Future Directions

Best practices for designing robust LLM-based feedback systems are as follows:

• Prefer absolute, calibrated scoring protocols where distractor features are uncontrolled (Tripathi et al., 20 Apr 2025).
• Ground feedback in explicit, educationally-validated frameworks, ensuring prompt clarity for each required feedback dimension (Ippisch et al., 10 Nov 2025, Stamper et al., 7 May 2024).
• Adopt multi-agent or iterative evaluation–regeneration pipelines for tasks requiring high coverage, critique, and revision (Cao et al., 8 May 2025, Zhang et al., 14 Nov 2025).
• Continuously monitor for error, bias, and hallucination, using automated dashboards and periodic human-in-the-loop audits (Qian et al., 8 Aug 2025, Zhang et al., 14 Nov 2025).
• Integrate retrieval-augmented and curriculum-grounded context retrieval to maximize feedback specificity and avoid irrelevant or generic advice (Zhao et al., 6 Jul 2025).
• Expose explainable reasoning and feedback debates to users for meta-cognitive transparency and learning (Ito et al., 23 Feb 2025, Banerjee et al., 25 Aug 2024).

Critical limitations remain in domain generalizability, real-time adaptation, student modeling, and engineering for edge cases or open-ended, multi-modal tasks. Open research questions include causal assessment of learning gains, cross-lingual equity, ethical and privacy frameworks for synthetic data, and theory-grounded refinement of feedback taxonomies for evolving LLM capabilities (Zhang et al., 14 Nov 2025, Zhang et al., 18 Feb 2025, Stamper et al., 7 May 2024).

LLM-based feedback systems have rapidly matured to the point of providing substantial empirical improvements in engagement, feedback quality, and scalability. High technical standards—anchored in prompt design, agent orchestration, rigorous evaluation, and fairness monitoring—are indispensable for realizing their pedagogical and operational potential across both educational and broader evaluative domains.