International Journal of Human-Computer Interaction· 2026Q1
Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools
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- Q1SCImago
- 2026year
Short summary
Educators prefer indirect AI scaffolding that preserves student reasoning (N=50), while students prefer direct, actionable help (N=90) in AI coding assistants.
AI-generated from the title and abstract; the full text is not read.
Key points
- Educators (N=50) prefer indirect AI scaffolding to maintain student reasoning processes.
- Students (N=90) prefer direct, actionable AI assistance when encountering difficulties.
- Educators emphasize the need for course-specific AI constraints and instructor oversight.
- Students prioritize timely support and clear guidance when stuck.
- Students' prior AI experience is linked to their perceived learning value from AI tools.
AI-generated from the title and abstract; the full text is not read.
Abstract
AI-powered coding assistants can support students in programming courses by providing on-demand explanations and debugging help. However, existing research often focuses on individual tools, leaving a gap in evidence-based design recommendations that reflect both educator and student perspectives. To address this gap, we surveyed educators (N = 50) and students (N = 90) to compare preferences regarding acceptable use boundaries, learner requests and context provision, AI responses and scaffolding, and control over assistance. Educators generally favored indirect scaffolding that preserves students’ reasoning, whereas students preferred direct, actionable help. Educators highlighted the need for course-aligned constraints and instructor-facing oversight, while students emphasized timely support and clarity when stuck. An exploratory analysis suggests that students’ prior AI experience was associated with perceived learning value, while structural preferences remained broadly similar across experience groups. We derive stakeholder-grounded design implications for learning-oriented AI coding assistants that balance students’ agency with instructional constraints.
The authors' abstract, as published at the source. International Journal of Human-Computer Interaction, 2026 · DOI ↗
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Field: Computer Science Applications
Computer Science ApplicationsComputer Science