Generative AI use in programming MOOCs varies by course length
Understanding Generative AI Use in Programming MOOCs: The Role of Course Context and Learner Characteristics
Artificial IntelligenceComputers and SocietyHuman-Computer Interaction
Summary
More people in longer programming courses tend to use AI tools like ChatGPT more often, especially to help fix bugs and come up with ideas. The study looked at two Estonian online programming courses and found that individual factors like age or prior experience didn’t change how people used AI. This means the type of course may matter more than who is taking it when it comes to using AI tools. The researchers also found little link between AI use and how much people learned.
What this means in practice
- •For online course designers: Adapt course materials and guidance based on course length to better integrate AI tools like ChatGPT for debugging and idea generation by learners.
- •For software development trainers: Provide tailored instructions on using AI assistants in programming tasks depending on course complexity and duration to enhance learner engagement.
Authors
Marina Lepp
Abstract
The increasing availability of generative artificial intelligence (GenAI) tools, such as ChatGPT and code-completion assistants, raises questions about how learners integrate these tools into learning activities, particularly in MOOCs that attract diverse participant populations. This study examines the use of GenAI in two programming MOOCs taught in Estonian that differ in duration, workload, topic complexity, and assignment volume: About Programming (4 weeks, 26 expected hours, n = 187) and Introduction to Programming (8 weeks, 78 expected hours, n = 182). Post-course questionnaire data were analyzed using non-parametric statistical methods to examine self-reported adoption, usage frequency, and purposes of GenAI use across courses and learner backgrounds. The results show that GenAI adoption was widespread in both MOOCs, with no statistically significant differences by gender, age, education level, or prior programming experience. However, participants in the longer, more extensive MOOC reported significantly higher usage frequency and were more likely to use GenAI for debugging and idea generation. Reported usage frequency for code explanation and debugging was also higher in the longer course. Exploratory analyses found limited relationships between GenAI use and learning-related outcomes. The findings suggest that course context may play a greater role than learner characteristics in shaping how GenAI tools are used. These results provide implications for instructional design and guidance in programming education.