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This study aims to develop a Strategic Learning Enabler model by reconfiguring the role of Information and Communication Technology (ICT) units to enhance Generation Z student productivity in digital higher education. The research is motivated by the gap between digital transformation demands and the predominantly technical-operational role of ICT units. Using a quantitative explanatory design, this study examines the causal relationships among ICT role reconfiguration, adaptive learning systems, and student productivity. Data were collected through questionnaires distributed to Generation Z students engaged in digital learning environments and analyzed using Structural Equation Modeling (SEM). The findings indicate that Generation Z students exhibit high responsiveness to structured tasks but lack continuity in learning activities due to weak self-regulated learning. The study also reveals that adaptive learning systems significantly mediate the relationship between ICT transformation and student productivity. The proposed model emphasizes digital governance, learning analytics, and micro-tasking structures as key components in building sustainable academic productivity. This research contributes both theoretically and practically by offering an institutional framework for digital learning transformation in higher education.

The rapid advancement of Information and Communication Technology (ICT) has accelerated the digital transformation of public services, including land administration. However, public complaint services at the Land Office of Southwest Sumba Regency still encounter challenges such as unstructured complaint procedures, manual data processing, risk of data loss, and limited public access to clear information. These issues highlight the need for an innovative and accessible complaint information system. This study aims to design and implement a chatbot-based public complaint service information system to enhance accessibility, transparency, and service effectiveness. A qualitative research method with a system development approach was applied. Data were obtained through interviews, observations, and documentation. The system was developed using a rule-based approach with a Finite State Machine (FSM) algorithm and implemented through the Typebot.io platform. The findings indicate that the chatbot provides structured, consistent, and user-friendly information, reduces manual workload, and improves public readiness before submitting complaints directly, while supporting future integration and system enhancement.