Dataset on When AI Teaches Through Humans: How Generative AI-Mediated Instruction Shapes Students’ Cognitive Engagement, Metacognitive Regulation, and Learning Motivation

The dataset further measures cognitive dependence on generative AI, including students’ perceived difficulty completing academic activities without AI assistance, reliance on AI before independent thinking, perceived necessity of AI tools in academic routines, and confidence when working without AI support. Additional constructs include trust in AI-generated information, students’ verification behaviors, perceived reliability of AI recommendations, and confidence in AI-supported academic decisions.

The dataset also evaluates potential cognitive implications of AI adoption through measures of critical thinking behavior, including reduced analytical effort, decreased independent problem-solving, and preference for AI-generated explanations over self-directed exploration. Furthermore, academic decision-making items assess how AI tools influence students’ study choices, research topic selection, academic strategies, and perceived academic performance improvement.

This dataset provides a comprehensive resource for researchers investigating the educational implications of generative AI adoption, cognitive reliance on AI technologies, AI-assisted learning behaviors, and the transformation of student decision-making processes in higher education. Dataset Components File 1: AI_Cognitive_Dependence_Survey_Data_BD_StudentsResponses.csv Contains: Demographic variables AI usage indicators Cognitive dependence measures Trust in AI measures Critical thinking measures Academic decision-making measures File 2: Questionnaire Structure.pdf Contains the survey instrument structure and measurement items used for data collection.