Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking
This work aims to improve educational tools for teaching computational thinking, though it is incremental as it focuses on design insights rather than implementation.
The paper addresses the challenge of learning computational thinking by involving educators in design fiction sessions to envision a conversational agent that provides personalized, stepwise guidance tailored to students' backgrounds and preferences.
Computational thinking, and by extension, computer programming, is notoriously challenging to learn. Conversational agents and generative artificial intelligence (genAI) have the potential to facilitate this learning process by offering personalized guidance, interactive learning experiences, and code generation. However, current genAI-based chatbots focus on professional developers and may not adequately consider educational needs. Involving educators in conceiving educational tools is critical for ensuring usefulness and usability. We enlisted nine instructors to engage in design fiction sessions in which we elicited abilities such a conversational agent supported by genAI should display. Participants envisioned a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring conversational agents oriented toward teaching computational thinking and computer programming.