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Lightweight UI for instructor-driven Bloom classification built on the selected-questions-verbs prompt

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 causal), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

A lightweight tool built on the article's best-performing prompting strategy. Instructors provide a small set of example questions per Bloom level for their context; the tool "extracts key action verbs from these examples" and uses them with the examples to construct the classification prompt. Users upload question sets in CSV/Excel and receive Bloom-level predictions in structured output. A user study with N=50 Prolific participants reported low workload (NASA-TLX means: mental demand 2.32, effort 2.46, frustration 1.68) and mean SUS of 78.2 (SD 14.07).

Design Implications

Context

Requirements

  • Instructors must supply example questions per Bloom level for their specific course, topic, or learning objectives

Constraints

  • The article notes feedback from real instructors would provide more precise insights for practical deployment, as the UI was tested with pilot users

Target Learners

  • instructors classifying large question banks

Learning Goals

  • automatic Bloom-level classification of assessment questions

Claims

Examples

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Key Sources

  • Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani, Mohammadreza Tavakoli, and Gábor Kismihók. (2026). Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs. https://arxiv.org/abs/2606.13684
Page metadata
Field Value
type design
id bloom-classification-ui-selected-questions-verbs
title Lightweight UI for instructor-driven Bloom classification built on the selected-questions-verbs prompt
description A lightweight tool built on the article's best-performing prompting strategy.
status review
generated by process:settle-candidates on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — abdolali-faraji-2026

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