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

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Τίτλος: Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools.
Συγγραφείς: Ma, Boxuan1 (AUTHOR) boxuan@artsci.kyushu-u.ac.jp, Xie, Yinjie2 (AUTHOR), Li, Huiyong3 (AUTHOR), Li, Gen4 (AUTHOR), Chen, Li5 (AUTHOR), Shimada, Atsushi4 (AUTHOR), Konomi, Shin’ichi1 (AUTHOR)
Πηγή: International Journal of Human-Computer Interaction. Sep2026, p1-22. 22p. 5 Illustrations.
Θεματικοί όροι: *Tutors & tutoring, *Academic support programs, *Stakeholder analysis, Computer programming education, Artificial intelligence in education, Educators, Instructional systems design
Περίληψη: AbstractAI-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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Design Implications for Student and Educator Needs in AI-Supported Programming Learning Tools.
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  Data: AbstractAI-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 (<italic>N</italic> = 50) and students (<italic>N</italic> = 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2026.2730083
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      – SubjectFull: Stakeholder analysis
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              Text: Sep2026
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