Investigating the Impact of Ai-Driven Feedback Systems on Student Autonomy and Self-Directed Learning

Abstract

This mixed-methods research examines the influence of AI-driven automated feedback systems on student autonomy and self-directed learning for the sample of 250 undergraduate students from the three biggest universities in Lahore, Karachi, and Islamabad, Pakistan. The researchers took a full academic semester and used a mixed-methods approach consisting of quantitative surveys and achievement tests and qualitative semi-structured interviews from students and faculty and used the pre and post intervention design which showed the clear improvement of students' self-directed learning readiness and autonomy levels corresponding to the use of artificial intelligence learning systems. The researchers performed quantitative analysis using SPSS which showed that there were changes on the scores and statistically significant differences on academic achievement and levels of learning independence of the students. On the qualitative side, the researchers performed thematic analysis in which the participants mostly pointed out improvement of personalized learning, enhancement of student motivation, interrupted technology adoption, over automation of feedback, need for human-AI balance. The participants' overall feedback showed that the AI automated feedback systems enabled enhancement of autonomous learning of students but cultural context, along with the degree of technological improvements available greatly influenced students’ outcomes. The study concluded that the purposeful design of educational settings using AI automated feedback tools in higher education requires consideration of the educational context, the students’ degree of digital literacy, the technological infrastructures of the educational institution and the pedagogical shifts expected, to optimize the use of artificial intelligence tools while engaging students in academic work.