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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Ben Rahman
Nasional University
Computer Science & IT

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OPTIMISING EXPLAINABLE AI IN EDUCATION: A DSPY-BASED FRAMEWORK WITH CHAIN-OF-THOUGHT REASONING FOR ADAPTIVE LEARNING Ben Rahman
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8319

Abstract

The application of artificial intelligence (AI) in education is often constrained by limited reasoning transparency, computational demands, and reproducibility challenges. This study proposes and conducts an exploratory evaluation of a modular AI education framework based on Declarative Structured Programming (DSPy) and Chain-of-Thought reasoning. The framework integrates typed input–output signatures with structured inference to support transparent question generation and adaptive feedback. A pilot study involved 10 participants with computer-science or education backgrounds; each completed three sessions, yielding 30 session-level observations. The framework used GPT-4o mini and was compared with prompt-based and rule-based baselines. It achieved 92.4% session-level learning accuracy and higher observed reasoning-clarity ratings than the baselines, with an average response time of 1.2 s. Because the sample was small, technically oriented, and evaluated over short sessions, the findings constitute preliminary evidence and should not be generalized to diverse learners or sustained learning outcomes. Larger, heterogeneous, longitudinal, and resource-instrumented studies are require.
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