Holistic Grading Overview

• Holistic grading is an integrative assessment approach that assigns a single overall score by synthesizing diverse evaluative perspectives and contextual factors.
• Modern implementations utilize AI models like SBERT and BERT for rubric-free scoring that mirrors expert human judgment with high reliability.
• Emerging LLM-based systems dynamically refine grading criteria through iterative feedback, supporting diverse applications from short-answer to video-based assessments.

Holistic grading is an evaluative paradigm that assigns a single integrative judgment to an artifact, response, or performance, capturing its overall quality as perceived by human experts. Distinguished from analytic grading—which decomposes performance into discrete, independently scored traits—holistic grading synthesizes multiple dimensions, perspectives, and contextual factors into an indivisible score. In modern AI and educational assessment research, holistic grading is both a practical necessity—enabling scalable, rubric-free evaluation and feedback—and a methodological goal, aiming to mirror the deliberative aggregation performed by expert human raters.

1. Conceptual Foundations and Definitions

Holistic grading is defined as an approach that “accommodates diverse perspectives” by deliberately integrating multiple expert judgments, often regarding academic achievement, growth, creativity, or collaboration, to yield a unified, balanced assessment. This contrasts sharply with unstructured averaging of scores, which fails to model the interactions and relative weights different facets contribute to overall quality (Ishida et al., 2024). In practice, holistic grading may be implemented via ordinal scales (e.g., poor/fair/good), numerical scores (e.g., 0–3, 1–10), or categorical judgments (e.g., “correct,” “incomplete,” “incorrect”), but always reflects the answer as a whole rather than a vector of subcomponent scores (Yoon, 2023, Agarwal et al., 1 Dec 2025).

In faculty settings, holistic grading assumes the integration of divergent, domain-specific evaluative lenses—motivation, technical understanding, teamwork—each potentially foregrounded by different raters, demanding a process that can systematically reconcile these perspectives (Ishida et al., 2024).

2. Formalization and Model Architectures

Modern AI-based holistic grading systems utilize diverse model architectures and pipelines across domains, encompassing short-answer grading, programming assignment evaluation, and visual data (e.g., embryo quality videos).

Short-Answer Holistic Grading

Yoon (Yoon, 2023) operationalizes holistic grading as follows:

• Given $m$ sub-questions within a short-answer item, for each sub-question $i$ the model extracts a “justification key” span from the student response. Using a Sentence-BERT (SBERT) bi-encoder, the model computes cosine similarities $s_{i,j}$ between the extracted span and each reference answer $r_j$.
• Analytic (binary) scores $a_i$ are assigned:

$$a_i = \begin{cases} 1 & \text{if } s_i = \max_j s_{i,j} > \tau\ (\tau = 0.5)\text{ and } r_{j^*} \text{ labeled "correct"} \ 0 & \text{otherwise} \end{cases}$$

• The holistic score $H$ is given by $H = \sum_{i=1}^m a_i$, yielding an interpretable overall grade in $\{0,\ldots,m\}$.

Rubric-Free Classification Models

A distinct framework, “AI-Enabled grading with near-domain data,” formalizes holistic grading as learning a function $G : A \rightarrow S$, mapping any student answer $x\in A$ to a holistic label $s\in S$ (e.g., “Correct,” “Incomplete,” “Incorrect”):

• BERT-based classifiers are sequentially fine-tuned using “near-domain” data—student responses to conceptually similar past questions—allowing end-to-end holistic judgment without explicit analytic decomposition or pre-defined rubrics (Agarwal et al., 1 Dec 2025).
• This enables rapid transfer to new questions, substantially decreasing labeled data requirements while retaining human-level agreement ($\kappa \approx 0.80$–$0.90$).

LLM-Mediated Human-Style Holistic Grading

Holistic grading is further extended in the context of LLM pipelines that replicate expert grading procedures:

• The “Grade Like a Human” architecture segments evaluation into iterative rubric generation (incorporating student responses to refine criteria), automated scoring with feedback, and post-grading review (detecting and correcting outlier assessments). This encapsulates the reflective, whole-work focus of expert grading (Xie et al., 2024).
• Chain-of-Thought prompting systems such as StepGrade for programming assignments deploy multi-stage LLM prompts, sequentially evaluating functionality, code quality, and efficiency so that each criterion’s reasoning is contextually informed by prior assessments, culminating in an integrated, holistic grade (Akyash et al., 26 Mar 2025).

Visual Holistic Grading

In video-based biomedical settings, holistic grading maps complex temporal and spatial patterns to an overall label (e.g., embryo quality). The CoSTeM model fuses morphological and morphokinetic branches, producing a softmax prediction over {poor, fair, good}, directly aligning with the clinical holistic grade (Sun et al., 5 Jun 2025).

3. Consensus Building and Integration of Multiple Perspectives

A core challenge in holistic grading is the integration of heterogeneous, potentially conflicting evaluator perspectives. Traditional committee-based approaches suffer from variability and superficial aggregation (simple averaging), which can obscure intended value trade-offs (Ishida et al., 2024).

LLMs can act as facilitators, abstracting individual faculty grades and rationales into a distilled set of “evaluation perspectives” (e.g., P₁: Motivation, P₂: Technical Understanding, etc.). The LLM then computes a weighted average of the numeric equivalents of letter grades, potentially assigning increased weight to perspectives with more epistemic authority:

$$\text{score}_{\text{holistic}} = \frac{\sum_{i=1}^N w_i g_i}{\sum_{i=1}^N w_i}$$

Justifications are explicitly linked to educational theories such as triangulation, holistic assessment, or developmental evaluation—the facilitating LLM thus provides both the integrative outcome and the theoretical rationale underlying grade assignment (Ishida et al., 2024).

4. Training Paradigms and Data Utilization

Holistic grading systems utilize several data-centric approaches:

• Reference Augmentation and Domain Adaptation: Annotated gold standards and large auto-labeled “silver” datasets are combined to fine-tune encoders for accurate semantic similarity and holistic grading (Yoon, 2023).
• Near-Domain Transfer: Prior labeled data from semantically related questions is leveraged to rapidly attain human-level holistic accuracy on new questions with minimal annotation, conferring both “data advantage” (less labeling needed for the same accuracy) and “accuracy advantage” (high accuracy without target-question labels) (Agarwal et al., 1 Dec 2025).
• Iterative Rubric Generation: Iteratively refining grading rubrics using student responses and human “gold” scores captures grader adaptation to emergent answer patterns, ensuring rubric coverage aligns with the actual distribution of student work (Xie et al., 2024).

5. Evaluation Metrics and Empirical Results

Standard metrics for holistic grading evaluation include:

• Exact-match Accuracy: Percentage of student answers where the model’s holistic score exactly matches the human-assigned score (Yoon, 2023).
• Quadratic Weighted Kappa ($\kappa$): Measures grader-model agreement adjusted for chance and degree of score difference (Yoon, 2023, Agarwal et al., 1 Dec 2025).
• Mean Absolute Error (MAE): Quantifies the absolute difference between model and human holistic scores, especially in programming assessment (Xie et al., 2024, Akyash et al., 26 Mar 2025).
• Human-Like Concordance: $\kappa \approx 0.8$–$0.9$ is typical for strong agreement (Agarwal et al., 1 Dec 2025).
• Inter-annotator Style Consistency and Outlier Detection: Review modules using LLMs as secondary checkers improve the reliability of holistic assessments by flagging anomalous or inconsistent grades for review (Xie et al., 2024).

Empirical results demonstrate:

• One-shot LLM+SBERT achieves $0.68$ accuracy and QWK of $0.73$, +0.15 over majority, though below fully supervised BERT ($0.77$, QWK $0.88$) for short-answer grading (Yoon, 2023).
• Near-domain BERT matches per-question fine-tuned accuracy ($91.3\%$) using only $62.5\%$ labeled data for new questions (Agarwal et al., 1 Dec 2025).
• In programming exercises, CoT-prompted LLMs show lower MAE and improved feedback quality versus regular prompting (Akyash et al., 26 Mar 2025).
• For video-based medical grading, the CoSTeM model achieves higher accuracy ($0.8606$) and F1 relative to baselines (Sun et al., 5 Jun 2025).

6. Theoretical Groundings and Pedagogical Implications

Holistic grading system justifications are increasingly grounded in educational and cognitive theory. Key cited frameworks include:

• Triangulation: Validating grading decisions through synthesis of multiple viewpoints (Ishida et al., 2024).
• Holistic Assessment Theory: Emphasizing integrative, context-sensitive scoring (Ishida et al., 2024).
• Developmental Evaluation and Constructive Alignment: Ensuring scores reflect both learning outcomes and developmental progress (Ishida et al., 2024).
• Reliability and Multiple Intelligences: Explicitly considering rater calibration and diverse dimensions of student achievement (Ishida et al., 2024).

LLM-based facilitators cite theories in their narrative justifications, offering transparency and professional development opportunities for human faculty (Ishida et al., 2024). The iterative rubric and feedback mechanisms in LLM pipelines reflect authentic expert practice in holistic grading.

7. Limitations, Open Challenges, and Future Directions

Outstanding issues include:

• Performance Gap to Humans: Even advanced holistic models (e.g., LLM+SBERT) fall short of human inter-rater accuracy ($0.68$ vs $0.92$) (Yoon, 2023).
• Dependence on Heuristic Thresholds: Fixed similarity thresholds or hand-crafted aggregation weights may not generalize across domains or question types (Yoon, 2023, Akyash et al., 26 Mar 2025).
• Bias and Social Acceptability: LLM- and AI-mediated holistic grading may encounter resistance without transparent reporting and oversight from human graders (Ishida et al., 2024).
• Rubric Adaptivity vs. Fixedness: Balancing the comprehensiveness of data-driven rubric refinement with stability necessary for fairness remains a key tension (Xie et al., 2024).
• Scalability and Data Cost: Achieving rapid, fair, and reliable holistic grading at scale with minimal annotation remains an open research area, especially outside narrow domains (Agarwal et al., 1 Dec 2025).
• Generalization to New Modalities: The ability to transfer holistic grading pipelines across question types (short-answer, programming, video) and subject areas is under active investigation (Akyash et al., 26 Mar 2025, Sun et al., 5 Jun 2025).

Potential research extensions include joint analytic–holistic scoring models, adaptive thresholding, prompt-sensitivity analysis, narrative-only (score-free) evaluation, and richer feedback generation leveraging extracted textual or visual spans (Yoon, 2023, Ishida et al., 2024, Xie et al., 2024).

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References:

• (Yoon, 2023) Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model
• (Agarwal et al., 1 Dec 2025) AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy
• (Ishida et al., 2024) Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments
• (Xie et al., 2024) Grade Like a Human: Rethinking Automated Assessment with LLMs
• (Akyash et al., 26 Mar 2025) StepGrade: Grading Programming Assignments with Context-Aware LLMs
• (Sun et al., 5 Jun 2025) Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining