To model the impact of ChatGPT and other online platforms on collocation learning and emotional engagement among EFL students using PLS-SEM, you can structure a model that includes key latent variables such as collocation knowledge gain, platform engagement, perceived usefulness, learner self-efficacy, and satisfaction. This approach enables you to examine both direct and indirect relationships, and incorporating a mediator or moderator like digital literacy, learner autonomy, or motivation could reveal deeper nuances in how technology influences learning behavior. Emotional engagement—defined by interest and attitude—can be measured through established scales that capture affective and cognitive involvement. As the use of AI in language learning evolves, integrating these constructs into a robust PLS-SEM framework offers a comprehensive lens through which to analyze learner responses and educational outcomes.

Maham Muzamil Thanks a million, for your input. While your suggestions about including constructs like self-efficacy and satisfaction are useful ingeneral PLS-SEM modeling, they seem more suited for broad technology acceptance studies. In my researchh case, the focus is specifically on collocation learning and emotional engagement (interest and attitude) among secondary EFL learners using ChatGPT during extensive reading, either im print or onlinr. So, more targeted variables and tools grounded in L2 vocabulkary acquisition and engagement theory are needed. The constructs you mentioned feel a bit too general for my current context.

Best regards, Your Excellency🎩.

Amine,