Leveraging AI Learning Analytics to Support CRT-Based Creative Writing Strategies for Quality Education

Ernalida Ernalida; Hani Atus Sholikhah; Izzah Izzah; Zahra Alwi; Chrisnatama Tangguh Prasetyo

Authors

Ernalida Ernalida Indonesian Language and Literature Education, Faculty of Teacher Training and Education (FKIP), Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
Hani Atus Sholikhah Indonesian Language and Literature Education, Faculty of Teacher Training and Education (FKIP), Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
Izzah Izzah Indonesian Language and Literature Education, Faculty of Teacher Training and Education (FKIP), Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
Zahra Alwi Indonesian Language and Literature Education, Faculty of Teacher Training and Education (FKIP), Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
Chrisnatama Tangguh Prasetyo Indonesian Language and Literature Education, Faculty of Teacher Training and Education (FKIP), Universitas Sriwijaya, Palembang, South Sumatra, Indonesia

Keywords:

AI Learning Analytics; Creative Writing; Culturally Responsive Teaching; Quality Education; Library Research

Abstract

Creative writing instruction in multicultural classrooms requires balancing inclusive, culturally grounded pedagogy with timely, individualized feedback. Culturally Responsive Teaching (CRT) validates students' cultural identities in narrative expression, yet its assessment has traditionally relied on subjective, time-consuming teacher judgment. Advances in AI Learning Analytics, including natural language processing (NLP) and automated writing evaluation (AWE), offer the capacity to monitor writing processes and outcomes at scale. This study employs a qualitative library research design, a form of systematic literature synthesis, to examine how AI Learning Analytics can be conceptually integrated into CRT-based creative writing strategies and how this integration may contribute to Sustainable Development Goal 4 (Quality Education). Data comprised peer-reviewed journal articles, conference proceedings, and policy documents published between 2015 and 2025, with emphasis on recent work, analyzed through thematic synthesis. Three converging themes emerged. First, AI Learning Analytics provides linguistic, structural, and affective indicators that can approximate students' cognitive engagement, although cultural expression remains difficult to measure automatically. Second, AI-generated feedback mediated through CRT principles can enhance rather than homogenize culturally diverse voices. Third, this synergy supports equity-oriented, data-informed instruction aligned with SDG 4. The review also identifies risks of algorithmic bias, reduced human interaction, and contextual insensitivity. The study offers a conceptual framework for educators and policymakers designing culturally responsive, AI-supported creative writing pedagogy, while underscoring the continued necessity of human pedagogical judgment.

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