AI Assistant Use and Perceived Cognitive Skills in Programming: Extending the Technology Acceptance Model - Fathahillah; Ayu Lestari; Muhammad Yahya; Muhammad Fardan; Putri Nanda Sari (Journal of Vocational, Informatics and Computer Education, 2026)
Purpose - This study investigates the relationship between students’ use of AI assistants and their perceived cognitive abilities in programming by extending the Technology Acceptance Model (TAM) to incorporate perceived Computational Thinking (CT) and perceived Problem-Solving Skills (PSS).Methods - A quantitative survey was administered to 320 undergraduate students with prior experience using AI assistants for programming-related tasks. The data were gathered through a four-point Likert-scale questionnaire and subsequently analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.Findings - The results showed that Perceived Usefulness was positively associated with Behavioral Intention to Use (β = 0.329, p < 0.001), while Perceived Ease of Use was positively associated with Perceived Usefulness (β = 0.680, p < 0.001) and Behavioral Intention to Use (β = 0.346, p < 0.001). Behavioral Intention to Use was positively associated with Actual Use (β = 0.741, p < 0.001). Actual Use was also positively associated with perceived CT (β = 0.672, p < 0.001) and perceived PSS (β = 0.754, p < 0.001).Research Implications - The findings suggest that AI assistants may support students’ perceived cognitive engagement in programming when used reflectively for debugging, comparing solutions, and understanding programming logic. However, the results should be interpreted as perceived cognitive support rather than objective evidence of cognitive skill improvement.Originality - This study extends TAM by linking AI assistant acceptance with perceived cognitive outcomes in AI-assisted programming.