AI Dependency and Academic Vulnerability in Higher Education A Survey Dataset from Bangladesh

This dataset contains responses from 1,104 university students representing 22 public and private universities across Bangladesh. Data were collected through a structured online survey administered using Google Forms between February 10 and June 15, 2026. The questionnaire was distributed through multiple digital platforms, including WhatsApp, Facebook, and LinkedIn, to ensure broad participation and institutional diversity.

The dataset was developed to investigate the relationship between artificial intelligence (AI) dependency and academic vulnerability among university students. It includes demographic variables such as gender, age, university affiliation, academic level, cumulative grade point average (CGPA), and daily AI usage time. In addition, the survey captures students’ perceptions and behaviors related to AI-assisted learning, including reliance on AI tools for academic tasks, verification of AI-generated information, independent learning ability, critical thinking, academic confidence, information retention, and understanding of AI-generated content. The study also examines students’ usage of widely adopted AI tools, including ChatGPT and Google Gemini, within academic contexts.

All attitudinal items were measured using a five-point Likert scale ranging from Strongly Disagree to Strongly Agree. The questionnaire was developed based on previously validated survey instruments and adapted to the educational and technological context of higher education in Bangladesh while maintaining content validity.

Participation in the survey was voluntary, and respondents were informed about the academic purpose of the study before completing the questionnaire. To ensure compliance with ethical research standards, all personally identifiable information was removed prior to publication, and participant anonymity was strictly maintained throughout the research process.

The dataset is intended to support future research in artificial intelligence in education, learning analytics, educational data mining, academic behavior analysis, predictive modeling, explainable machine learning, and AI-driven educational decision-making. Researchers may use this dataset for statistical analysis, machine learning model development, and the investigation of factors associated with AI dependency and academic vulnerability among university students.