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Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools

Boxuan Ma, Yinjie Xie, Huiyong Li, Gen Li, Li Chen, Atsushi Shimada, Shin'Ichi Konomi

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 in education settings. To ground the design of learning-oriented AI coding assistants for both sides' needs, we conducted parallel surveys of educators (N=50) and students (N=90) to compare preferences about (i) how students should request help, (ii) how AI should respond, and (iii) who should control. Our results show that educators generally favored indirect scaffolding that preserves students' reasoning, whereas students were more likely to prefer direct, actionable help. Educators further highlighted the need for course-aligned constraints and instructor-facing oversight, while students emphasized timely support and clarity when stuck. Based on these findings, we discuss the interaction-focused design space and derive design implications for learning-oriented AI coding assistants, highlighting scaffolding and control mechanisms that balance students' agency with instructional constraints.

Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools

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 in education settings. To ground the design of learning-oriented AI coding assistants for both sides' needs, we conducted parallel surveys of educators (N=50) and students (N=90) to compare preferences about (i) how students should request help, (ii) how AI should respond, and (iii) who should control. Our results show that educators generally favored indirect scaffolding that preserves students' reasoning, whereas students were more likely to prefer direct, actionable help. Educators further highlighted the need for course-aligned constraints and instructor-facing oversight, while students emphasized timely support and clarity when stuck. Based on these findings, we discuss the interaction-focused design space and derive design implications for learning-oriented AI coding assistants, highlighting scaffolding and control mechanisms that balance students' agency with instructional constraints.
Paper Structure (25 sections, 4 figures)

This paper contains 25 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Design Space for AI Coding Assistants
  3. Exploiting Unique Advantages of AI (D1):
  4. Designing the AI Querying Interface (D2)
  5. Balancing the Directness of AI Responses (D3)
  6. Supporting Trust, Transparency and Control (D4)
  7. Method
  8. Survey Design and Measures
  9. Procedure and Recruitment
  10. Participants
  11. Educator participants
  12. Student participants
  13. Results
  14. AI-Policy and Perceptions
  15. Learner Input and Context
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Design considerations and trade-offs within the design space of AI-powered assistants for educational settings. Each consideration is based on a key stage in students’ help-seeking process.
  • Figure 2: Survey results overview: educator and student preferences for AI coding assistants for educational setting.
  • Figure 3: (a) Educators’ and (b) students’ perceived learning value ratings by question type. Items are ordered by the sum of High and Very High responses.
  • Figure 4: (a) Educators' and (b) students' perceived helpfulness of indirect scaffolding. Items are ordered by the sum of High and Very High responses.