Alli Abdurrazaq, Omar Camara, Saidu Conteh, Ebrima Kanteh
Purpose: This study evaluated the effects of the traditional lecture method and three CTCA 2.0-based modalities (Monolingual AI-supported CTCA, Strategic Multilingual AI-supported CTCA, and Heavy Multilingual AI-supported CTCA) on students' academic performance and attitudes toward learning the OSI model. Methodology: A quasi-experimental design involving four intact groups (N = 75) was adopted. Pre-test and post-test data were analysed using MANCOVA, with pre-test scores serving as covariates. Results: Findings revealed a significant overall difference among the instructional approaches (Wilks' Λ = .129, p < .001). Although the CTCA 2.0 groups recorded higher achievement means than the control group, this difference in post-test achievement was not statistically significant, F(3, 69) = 2.49, p = .07. However, instructional approach exerted a very large and significant effect on students' attitudes, F(3, 69) = 130.87, p < .001, η² = .851, with all CTCA modalities yielding positive outcomes. Novelty and Contributions: This study advances computing education by integrating AI-supported multilingual CTCA 2.0 strategies for teaching abstract computing concepts, and by demonstrating their strong influence on learners' affective engagement. Practical and Social Implications: The findings suggest that culturally responsive, technology-enhanced instructional approaches can improve learners' attitudes toward challenging computing topics, thereby fostering engagement, inclusion, and sustained learning.