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Gulf of Envisioning framework as a design lens for prompting instruction

Design · All designs
Evidence · no claims cited

Description

The article adopts the Gulf of Envisioning framework (Subramonyam et al.) to explain why learners may fail even when using powerful AI tools, and uses it to organize its intervention. "By scaffolding learners' ability to describe the problem (capability), phrase prompts effectively (instruction), and assess output quality (intentionality), we aim to reduce the cognitive gap between intent and implementation." The lesson's five learning objectives are aligned with the framework's three gaps, framing prompt-based programming as a learnable CS-specific skill.

Design Implications

Context

Requirements

  • Instruction and practice addressing each of the three dimensions: capability, instruction, and intentionality

Constraints

  • Applied in this article to prompt-based programming tasks where learners specify computational intent in natural language

Target Learners

  • end-user programmers
  • novices using LLMs for programming

Learning Goals

  • expressing computational intent clearly to LLMs
  • evaluating LLM-generated code

Claims

Examples

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Key Sources

  • Keith Tran, Samiha Marwan, and Thomas Price. (2026). Teaching Prompt-Based Programming with LLMs: A 45-Minute Lesson with Guided Practice for End-User Programmers. https://arxiv.org/abs/2606.30547
Page metadata
Field Value
type design
id gulf-of-envisioning-framework-prompting
title Gulf of Envisioning framework as a design lens for prompting instruction
description The article adopts the Gulf of Envisioning framework (Subramonyam et al.) to explain why learners may fail even when using powerful AI tools, and uses it to organize its intervention.
status review
generated by process:settle-candidates on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — keith-tran-2026

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