What Educators Need to Know in the Wake of AI Infiltration in Education?

The current study examines the role of individual differences in the context of AI (ChatGPT) supported STEM learning. The results showed working memory could influence individual learners’ cognitive process in math-related problem solving. It was found high WM learners performed better than low WM learners in terms of performance and cognitive load experienced during the learning. INTRODUCTION: Digital technology such as AI has increasingly played a critical role in learners’ learning in terms of their cognitive, metacognitive and affective processes (Amab et al., 2012; Echeverri and Sadler, 2011). Regardless of the promises of new digital technologies in education, researchers and educators have cautioned that digital technology may become less beneficial if the design of digital learning fails to take into consideration individual differences in learning. Greenberg et al. (2021) studied the individual differences in working memory capacity in multimedia learning and found that dual modality (visual and auditory) supports individuals with low visuospatial working memory more than single modality (visual or auditory). They thus concluded that the design of digital learning should take into consideration the individual differences in working memory. Gupta and Zheng (2020) examined the individual differences in math learning by looking into cognitive load experienced by learners during problem solving. Their study revealed that learners varied in their cognitive load and performance when studying the same subject (e.g., math). The researchers attributed the learners’ performance variation to individual differences in working memory as learners may experience different cognitive load due to their differences in working memory capacity. Given the individual differences in working memory capacity pertaining to difference in cognitive load in learning, Zheng (2018) proposed a framework for examining individual differences and knowledge acquisition in digital learning. The framework outlines three components which include (a) individual traits, (b) digital technology with enhanced cognitive support (DTECS), and (c) highly intelligent digital technology (HIDT). Zheng (2018) suggests extra cognitive support is needed to improve learners’ deep learning skills.