Investigating the factors affecting the intention to use AI Chatbots in STEM education app: a hybrid structural equation modelling and artificial neural network

Abstract

This study explores the impact of Artificial Intelligence (AI)-powered applications in Science, Technology, Engineering, and Mathematics (STEM) education, emphasizing their role in improving students’ problem-solving skills and self-efficacy. It examines how personal factors such as ICT self- efficacy and self-directed learning (SDL), along with technological aspects like perceived ease of use (PEU) and perceived convenience (PC), shape students’ engagement with AI-driven tools. Using a quantitative method, survey data were collected from 117 students during February–March 2025. The research employed the Structured Predictive Latent Semantic System (SPLSS) model and Artificial Neural Network (ANN), validated through Structural Equation Modeling (SEM), to ensure reliability and predictive accuracy. Results show that AI tools significantly enhance problem-solving and self-efficacy. PC and intention to use were strong predictors of chatbot utilization, while ICT self-efficacy and PEU influenced attitudes and behavioral intentions. Importance-Performance Map Analysis (IPMA) revealed convenience as most impactful, and self-efficacy as least. All hypotheses were supported, confirming the model’s robustness. The study concludes that AI-driven applications create personalized, engaging, and confidence-boosting STEM learning experiences, highlighting the need for user-friendly, contextually adaptive AI tools and suggesting future research on long-term impacts and scalability for inclusive STEM education.