CodeOwl: Automatic Generation of Tiered Parsons Problems for Introductory Programming

Abstract

Addressing learner heterogeneity in programming education is challenging due to variations in student speed, prior knowledge, and motivation. While differentiated instruction, such as tiered sequences, allows students to engage at appropriate difficulty levels, manually creating these resources is labour-intensive. This paper introduces CodeOwl, an AI-driven tool that automates the generation of tiered Parsons problems. Starting from a sample task or specific programming concepts, CodeOwl produces tiered sequences of Parsons problems automatically. We evaluated CodeOwl with a mixed-method framework comprising complexity analysis, expert ratings, and user studies. Analysis of 297 tiered sequences (three tiers each) revealed that 98.7% achieved a positive complexity increase, successfully rising in difficulty from Tier 1 to Tier 3. Experts rated the generated problem statements as highly clear. While teachers praised the tool's utility, they identified a need for greater control over curriculum alignment. Similarly, students reported positively but requested enhanced feedback mechanisms and alternative interaction modes.