Affordances, constraints, and implications of ChatGPT in education from a social-ecological perspective: A data mining approach

Abstract This study investigated the affordances, constraints, and implications of ChatGPT in education using the affordance theory and social-ecological systems theory. We employed a data mining approach that blends social media analytics including sentiment analysis and topic modelling and qualitative analysis to extract viewpoints from a collection of datasets consisting of 33,456 tweets. Key findings indicate that 42.1% of analysed tweets conveyed a positive sentiment, 39.6% were neutral, and only 18.3% conveyed a negative sentiment. We also identified five categories of ChatGPT properties (e.g., text and data analysis, AI and machine learning) and an array of affordances of ChatGPT in education (e.g., facilitating student personalised learning, classroom instruction, provision of educational resources, curriculum changes, and assessment). Meanwhile, the findings revealed key concerns, including academic dishonesty, bias, and ethics that warrant attention. This study contributes to a real-time understanding of the impact of ChatGPT on education and informs researchers, educators, and policymakers to take a holistic approach to evaluating ChatGPT in educational practices.