Learner and Instructor Needs in AI-Supported Programming Learning Tools: Design Implications for Features and Adaptive Control
Project Overview
The document explores the integration of generative AI in education, particularly through the development of AI-supported programming learning tools that offer adaptive assistance to learners. It underscores the necessity of striking a balance between learner autonomy and system guidance, revealing that students and instructors prefer help features that include personalized feedback, encouragement, and visual aids. Through participatory design sessions, the study identifies key user preferences and proposes guidelines for creating effective AI tools that address the varied needs of learners. The findings suggest that when AI tools are thoughtfully designed, they can significantly enhance the learning experience in programming by providing tailored support, thereby fostering greater engagement and understanding among students.
Key Applications
AI-supported programming learning tools
Context: Undergraduate programming courses for novice learners
Implementation: Participatory design sessions with learners and instructors to gather insights and preferences for help features and control dynamics.
Outcomes: Improved design guidelines for creating user-centered AI tools that balance learner autonomy and system guidance, enhancing the programming learning experience.
Challenges: Determining the appropriate balance of control between learners and the system, and addressing individual differences in control preferences.
Implementation Barriers
Design Challenge
Balancing learner control with system-driven guidance can be difficult, with risks of overwhelming learners or misaligning system support with learner needs.
Proposed Solutions: Develop design guidelines based on user input to identify optimal levels of control and types of help, and ensure flexibility to adapt to individual learner preferences.
Project Team
Zihan Wu
Researcher
Yicheng Tang
Researcher
Barbara Ericson
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zihan Wu, Yicheng Tang, Barbara Ericson
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai