Assessing Student Needs for AI-based Adaptive Learning in Higher Education

Abstract

This study examines undergraduate Information and Communications Technology (ICT) students’ needs, expectations, and concerns regarding Artificial Intelligence (AI)-supported adaptive learning for programming education in Kazakhstan. Using a mixed-methods descriptive design, data were collected from 134 students at two universities via a multilingual online questionnaire that combined Likert-scale items with open-ended questions. Quantitative results indicate high awareness of AI and strong readiness to use AI-supported learning tools, with the highest-rated needs focusing on adaptive content, automated practice/task generation, progress tracking, and mobile access. At the same time, students reported moderate concerns about the accuracy of AI-generated materials and the privacy of learning data, and they preferred models where teachers remain actively involved in interpreting and guiding AI feedback. Qualitative findings reinforced these patterns, identifying technical reliability, language/localization, transparency, and integration with existing Learning Management System (LMS) as key barriers, while emphasizing stability, explainable feedback, multilingual support, and teacher-in-the-loop functionality as priorities for improvement. This study should be interpreted as a pre-implementation needs assessment based on students’ responses to a hypothetical AI-supported learning scenario and does not provide evidence of actual adoption or instructional effectiveness. The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.