Designing an AI-Assisted Self-Evaluation Framework to Support Multi-Accreditation Study Program in Indonesian Higher Education

Voice Esther Ticoalu

doi:10.24036/jtip.v19i1.1110

Authors

Voice Esther Ticoalu BINUS University

DOI:

https://doi.org/10.24036/jtip.v19i1.1110

Keywords:

Artificial Intelligence, self-evaluation, internal quality assurance system, higher education, multi-accreditation

Abstract

The adoption of multi-accreditation schemes in Indonesian higher education has increased the complexity of self-evaluation processes at the study program level. Study programs must align internal quality assurance practices with multiple national and professional accreditation standards, often leading to overlapping indicators, fragmented evidence management, and increased administrative burden. This study aims to design an AI-assisted self-evaluation framework to support structured and coherent self-assessment in multi-accreditation contexts. A design-based qualitative approach was employed through document analysis of the BAN-PT IAPS 5.1 instrument and selected Lembaga Akreditasi Mandiri (LAM) accreditation frameworks, followed by indicator harmonization and framework development. The results reveal a strong convergence of core quality indicators across accreditation instruments, enabling the formulation of unified indicators linked to shared evidence sources. Based on these findings, an AI-assisted analytical layer supporting indicator-evidence mapping, evidence completeness checking, and performance trend identification, while maintaining human-in-the-loop validation. The framework enhances efficiency, consistency, and evidence traceability in self-evaluation under multi-accreditation condition and provides a practical foundation for future empirical implementation and integration with international accreditation and ISO-based education quality standards.

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