Integrating generative pre-trained transformer into a differentiated learning management system for counselor education - Arbin Janu Setiyowati; Riskiyana Prihatiningsih; Khairul Bariyyah; Hengki Tri Hidayatullah (Research and Social Study Institute, 2025)
Differentiated learning has become a critical approach in counselor education, as it allows instructional processes to be tailored to individual learner needs. However, most existing Learning Management Systems (LMS) have yet to fully support this pedagogical principle. With the rapid advancement of artificial intelligence, particularly Generative Pre-trained Transformer (GPT) models, new opportunities have emerged to develop more adaptive and personalized LMS platforms. This study aims to identify the initial design needs of a differentiated learning LMS in counselor education and explore the potential integration of GPT as a supportive technology. Employing an exploratory qualitative approach, data were collected from 34 pre-service teachers in a Guidance and Counseling Teacher Professional Education Program using a combination of open- and closed-ended questionnaires and semi-structured interviews. The analysis revealed eight key areas of student need within the LMS, including media variation, contextualization, interactivity, accessibility, and automated feedback. GPT’s features were found to align with these needs, particularly its ability to deliver case-based simulations, personalized content recommendations, and adaptive feedback. These findings provide a conceptual foundation for the development of LMS platforms that are more contextualized, human-centered, and capable of supporting differentiated learning in counselor education.