Need Analysis for the Development of Artificial Intelligence-Based Learning Models to Improve Indonesian Correspondence Skills

A major challenge in correspondence learning is the limited development of effective learning models, primarily due to the lack of innovation in instructional design and insufficient integration of digital technology, which in turn constrains dynamic interaction between lecturers and students and reduces the alignment of learning content with learners’ characteristics. This study aims to analyze the need for developing an effective Indonesian correspondence learning model in the digital era. A descriptive qualitative approach was employed, with data collected through interviews, observations, and documentation studies involving 85 students and two lecturers. The findings reveal three main requirements for model development: (1) the integration of digital technology into the learning environment, (2) the enhancement of dynamic interaction between lecturers and students, and (3) the development of adaptive learning content that reflects learners’ characteristics. Furthermore, 78% of the respondents expressed a preference for a blended format combining printed materials with digital platforms, and 85% emphasized the need for responsive feedback mechanisms. This needs analysis provides a sound foundation for designing correspondence learning models that systematically incorporate integrated digital technologies, align with learners’ needs, and support the improvement of students’ academic skills.