Exploring the Effects of the CER Model-Based GenAI Learning System to Cultivate Elementary School Students' Computational Thinking Core Skills in Science Courses

Background

Computational thinking (CT) is a fundamental ability required of individuals in the 21st-century digital world. Past studies show that generative artificial intelligence (GenAI) can enhance students' CT skills. However, GenAI may produce inaccurate output, and students who rely too much on AI may learn little and be unable to think independently. Besides, most research on CT mainly focused on Scratch or programming classes, but incorporating it into the K-12 science curriculum is better for students' deep learning and CT core skills development.

Objectives

This study proposed a causal explanation and reflection (CER) model-based GenAI learning system in science courses to cultivate students' CT core skills.

Sample

One hundred and eighteen elementary school students in three different classes participated in this study.

Methods

A quasi-experiment was conducted in Fujian, China. Students in the experimental group learned with the CER model-based GenAI learning system; students learned with the CER model-based learning system in control group 1; students in control group 2 used the causal-explanation-based GenAI learning system. Students' learning achievement and CT core skills were examined.

Results

The results showed that the CER model-based GenAI learning system significantly improved students' science learning and CT core skills. Interview results further showed some students complained that GenAI only provided answers without encouraging them to comprehend the material.

Conclusions

CT should not exist only in computer courses. Instead, it is an approach to problem-solving that applies to all disciplines. Also, over-reliance on GenAI may hinder learning ability. The effectiveness of GenAI-based learning depends on its judicious use.