Bloom's Taxonomy Classification

• Bloom’s Taxonomy is a hierarchical framework that organizes educational objectives into six cognitive levels, each defined by specific action verbs.
• It underpins automated systems using rule-based methods, traditional machine learning, and deep neural networks for mapping learning outcomes and generating questions.
• Applications include mastery learning, curriculum design, and real-time course analytics, enhancing both educational assessment and AI-augmented instructional systems.

Bloom’s Taxonomy is a hierarchical framework for classifying the complexity of educational objectives across six cognitive levels. Widely adopted in instructional design, assessment, and automated question classification, its explicit structure serves not only curriculum mapping and instructional scaffolding but increasingly guides the architecture of machine learning systems aimed at expediting educational analytics and question generation. Revised versions—most notably Anderson and Krathwohl’s—maintain the six-level structure and adapt terminology to modern pedagogic contexts. Recent research leverages Bloom’s taxonomy for deep learning–driven assessment design, automated question labeling, learning outcome similarity computation, and mastery-based pedagogies, establishing it as a central tool for both traditional and AI-augmented educational systems (Yaacoub et al., 19 Apr 2025, Kumar et al., 14 Nov 2025, Scaria et al., 8 Aug 2024, Waheed et al., 2021, Srinivas et al., 11 Jun 2025, M et al., 2021).

1. Cognitive Levels: Definitions and Action-Verbs

Bloom’s Taxonomy organizes intellectual activity from basic factual recall through the creation of novel syntheses. While minor terminological variants exist, most contemporary implementations recognize the following six levels (with typical action-verbs):

Level	Definition	Common Verbs
Remember	Retrieve or recognize facts, definitions, basic principles	list, define, recall
Understand	Grasp meaning, interpret or summarize material	explain, classify, describe
Apply	Use principles in new situations; solve concrete problems	use, solve, implement
Analyze	Break information into parts; examine relationships	analyze, differentiate
Evaluate	Make judgments; critique or defend solutions	judge, assess, justify
Create	Put elements together to form a novel, coherent whole	design, construct, invent

These categories underpin manual rubrics for educational item development and serve as explicit label-sets in automated systems. For instance, Premalatha et al. enumerate action-verb lists used in course syllabi mapping, and Diab & Sartawi provide verified cross-institutional verb sets for precise machine-annotated level assignment (M et al., 2021, Diab et al., 2017). Quality rubrics—such as those used in physics MCQ repositories—require independent coding, inter-rater calibration, and explicit assignment of verb-to-level mappings (Bates et al., 2013).

2. Mapping and Classification Methodologies

State-of-the-art automated systems for Bloom-level classification utilize three main paradigms: rule-based action-verb lookup, traditional machine learning, and transformer-based deep neural architectures.

Rule-Based Action Verb Extraction:

Diab & Sartawi's action-verb classification pipeline follows these steps: (1) extract and lemmatize the main verb from an item; (2) compute semantic similarity (e.g., Wu–Palmer or cosine-based) between the extracted verb and curated level-specific verb-lists; (3) assign the taxonomy level of the closest matching verb, breaking ties by highest aggregate similarity (Diab et al., 2017). Macro-average precision reaches 97%.

Traditional Machine Learning:

Recent implementations, such as SVMs and logistic regression, incorporate Bag-of-Words vectors and POS features. Augmentation via synonym replacement (10% of training data) increases lexical diversity, raising test accuracy to 94% in SVMs using a balanced six-class dataset of learning outcomes and exam questions (Kumar et al., 14 Nov 2025). Naive Bayes, logistic regression, and random forests are competitive for ≤1,000 samples and exhibit minimal overfitting compared to neural architectures.

Deep Neural and Transformer Models:

Transformer-based models—RoBERTa, DistilBERT, BloomNet—dominate in larger-scale, cross-domain evaluations. BloomNet, combining semantic (RoBERTa), linguistic (POS-tag, NER), and word-attention signals, achieved 87.5% IID and 70.4% OOD accuracy on six-class question datasets, outperforming all tested baselines (Waheed et al., 2021). DistilBERT reached 91% validation accuracy in classifying 3,691 AI-generated questions, successfully narrowing the gap between lower and higher taxonomy levels (Yaacoub et al., 19 Apr 2025). Multi-task and interactive-attention architectures further leverage label–input dependencies for better joint prediction of Bloom’s class and difficulty (V et al., 2022).

Zero-shot evaluations reveal competitive performance for frontier LLMs (OpenAI, Gemini) at ≈0.72–0.73 accuracy, with chain-of-thought and few-shot prompting recommended to augment reliability (Kumar et al., 14 Nov 2025, Scaria et al., 8 Aug 2024).

3. Rubrics, Datasets, and Evaluation Protocols

Best practice for rubric construction involves:

• Clearly defined, verb-anchored levels per curriculum domain (e.g., action verbs for “Create” include design, assemble, conjecture).
• Independent annotation by multiple experts, followed by inter-coder agreement statistics (Cohen’s κ > 0.9 standard in physics MCQ studies) (Bates et al., 2013).
• Explicit algorithmic conversion of ABET student-outcome criteria to quantitative difficulty indices, summing rubric values tied to Bloom levels, validated against historical grade distributions (MSE ≈ 0.2, ≈98% mapping accuracy) (M et al., 2021).
• Classification systems are trained using annotated datasets, balanced across taxonomy categories where feasible. Preprocessing includes tokenization, lemmatization, stopword removal, synonym augmentation (for classical ML), or embedding features for neural systems.

Typical evaluation metrics:

• Macro- and micro- averaged accuracy, precision, recall, F1.
• Confusion matrices, revealing that most misclassifications occur between adjacent levels (e.g., Analysis vs. Synthesis).
• Comparative analysis across architectures: classical ML, RNNs, transformers; OOD (out-of-distribution) robustness.

4. Applications: Automated Question Generation, Curriculum Design, and Assessment

Bloom’s Taxonomy is actively employed for:

Automated Educational Question Generation (AEQG):

LLMs (GPT-4, PaLM2, Mistral) prompted with definitions, examples, and skill-scaffolding reliably generate questions across Bloom levels when guided by concise instructions and human-crafted exemplars (Scaria et al., 8 Aug 2024). Prompt engineering involving chain-of-thought scaffolding and context-modelling not only increases rubric adherence but also supports pedagogical relevance and diverse coverage.

Difficulty Estimation and Course Planning:

Mapping Bloom’s verbs across ABET criteria enables quantitative prediction of course difficulty, guiding curriculum planners on student support needs by linking the density of high-order cognitive verbs to difficulty indices mirrored by mean grade outcomes (M et al., 2021). Bloom-index computations (semantic similarity of verb-level distances) provide a numerical measure of cross-course objective alignment (Pawar et al., 2018).

Assessment and Mastery Learning:

Six-module assessments explicitly aligned to taxonomic levels (e.g., in data visualization PCP literacy) facilitate mastery-based learning with operational mastery thresholds (≥80%) per formative module, yielding clear improvements in higher-order cognitive skill acquisition (Srinivas et al., 11 Jun 2025). In summative assessment repositories, taxonomy-aligned rubrics ensure item clarity and challenge, with scaffolding exercises promoting high-quality, higher-level student-authored content (Bates et al., 2013).

Hierarchical Multi-Attribute Text Classification:

In discussion forums and real-time course learning subsystems, hierarchical two-step models simultaneously assign sentiment (positive, neutral, negative) and Bloom-level epistemic labels to chat texts. Random Forests and LSTMs can be stacked for step-wise classification, reaching 84% overall accuracy on YouTube-derived chat data (Toba et al., 26 Jan 2024).

5. Inter-Level Dependencies, Limitations, and Robustness

Empirical analyses reveal partial independence between taxonomy levels in many applications. Burns et al. found that knowledge retrieval (Level 1) strongly predicts application accuracy (Level 3), but trends or analytic insight (Levels 4–5) can be missed even by successful recallers, arguing against a rigid hierarchical dependency (Burns et al., 2020). Multi-level confusion is most prevalent between adjacent classes; merging higher-order levels improves classifier accuracy for complex cognitive discrimination (Yaacoub et al., 19 Apr 2025).

Robust cross-domain generalization remains a challenge. BloomNet’s fusion architecture demonstrates immunity to distributional shift, losing only 17 percentage points in OOD tests compared to 20+ for baselines (Waheed et al., 2021). Classical ML methods, properly augmented and balanced, also show minimal overfitting, but deep RNNs severely overfit on small-scale training sets (Kumar et al., 14 Nov 2025). Domain- or context-specific vocabulary coverage is essential for broad generalization in forum and essay analysis systems (Toba et al., 26 Jan 2024).

Current LLMs, even when provided with explicit prompt engineering and gold-standard rubrics, still exhibit systematic misclassification bias and require manual validation for high-stakes assessment settings (Scaria et al., 8 Aug 2024).

6. Quantitative Indices and Mathematical Formulations

Several studies formalize taxonomy alignment and difficulty:

• Bloom Index:

$$\text{Bloom\_Index} = \frac{1}{n} \sum_{i=1}^{n} \Big( -0.20 \cdot d_i + 1 \Big)$$ where $d_i$ is the absolute distance between Bloom levels assigned to verb pairs from two learning objectives (Pawar et al., 2018).

• Course Difficulty:

$$D_{\text{course}} = \sum_{c \in C} \sum_{\ell \in L(c)} R_\ell$$ where $C$ is the set of ABET criteria addressed, $L(c)$ is the Bloom levels mapped to each criterion, and $R_\ell$ is the rubric score for level $\ell$ (M et al., 2021).

• Mastery-Learning Threshold:

$M = 0.80$ All students must reach $S_{FA} \geq M$ per module before advancing (Srinivas et al., 11 Jun 2025).

• Classifier Objective (Cross-Entropy Loss):

$L = -\sum_{i=1}^N \sum_{c=1}^C y_{i,c}\log p_{i,c}$ with $y_{i,c}$ the ground-truth one-hot and $p_{i,c}$ the predicted class probability (Laddha et al., 2021, Waheed et al., 2021, Toba et al., 26 Jan 2024).

• Inter-rater Agreement:

$\kappa = \frac{p_o - p_e}{1 - p_e}$ for assessing reliability in manual Bloom-level annotation (Bates et al., 2013).

Classification model performance is summarized in tables (F1 scores, confusion matrices) and benchmarked for micro/macro-averaged accuracy across all levels.

7. Future Directions and System Recommendations

Contemporary research suggests several paths for further work:

• Development of hybrid architectures combining symbolic pedagogical rules with deep learning (Yaacoub et al., 19 Apr 2025).
• Extension to additional cognitive frameworks: Webb’s DOK, SOLO taxonomy (Yaacoub et al., 19 Apr 2025, V et al., 2022).
• Large-scale multi-domain training to improve OOD robustness and minimize vocabulary drift (Waheed et al., 2021, Toba et al., 26 Jan 2024).
• Incorporation of explicit Bloom-verb lexicons into encoder-decoder LLM architectures.
• Real-time integration into Learning Management Systems for automated feedback and mastery tracking (V et al., 2022).
• Enrichment of pre-trained embeddings with targeted domain-specific lexicons and co-occurrence patterns for educational forum and chat systems.
• Human-in-the-loop calibration for operational rubrics, particularly in critical or summative assessment settings (Scaria et al., 8 Aug 2024, Bates et al., 2013).
• Advanced prompt engineering, including chain-of-thought and example-guided scaffolding, for LLM-driven AEQG (Scaria et al., 8 Aug 2024).

By operationalizing Bloom’s Taxonomy through rigorous definition, robust annotation, and precise model selection, educators, researchers, and AI system designers can reliably structure, classify, and evaluate educational content at scale—ensuring fine-grained cognitive profiling and pedagogical alignment across diverse instructional stacks.